International Journal of Soft Computing and Engineering (IJSCE) ISSN: 2231-2307, Volume-2, Issue-2, May 2012 A Survey of Bio inspired Optimization Algorithms

Binitha S, S Siva Sathya

 example for optimization, because if we closely examine Abstract—Nature is of course a great and immense source of each and every features or phenomenon in nature it always inspiration for solving hard and complex problems in computer find the optimal strategy, still addressing complex interaction science since it exhibits extremely diverse, dynamic, robust, among organisms ranging from microorganism to fully complex and fascinating phenomenon. It always finds the optimal solution to solve its problem maintaining perfect fledged human beings, balancing the ecosystem, maintaining balance among its components. This is the thrust behind bio diversity, adaptation, physical phenomenon like river inspired computing. Nature inspired algorithms are meta formation, forest fire ,cloud, rain .etc..Even though the heuristics that mimics the nature for solving optimization strategy behind the solution is simple the results are amazing. problems opening a new era in computation .For the past Nature is the best teacher and its designs and capabilities are decades ,numerous research efforts has been concentrated in extremely enormous and mysterious that researchers are this particular area. Still being young and the results being very amazing, broadens the scope and viability of Bio Inspired trying to mimic nature in technology. Also the two fields have Algorithms (BIAs) exploring new areas of application and more a much stronger connection since, it seems entirely reasonable opportunities in computing. This paper presents a broad that new or persistent problems in computer science could overview of biologically inspired optimization algorithms, have a lot in common with problems nature has encountered grouped by the biological field that inspired each and the areas and resolved long ago. Thus an easy mapping is possible where these algorithms have been most successfully applied. between nature and technology. Bio inspired computing has

come up as a new era in computing encompassing a wide Index Terms— Bio Inspired Algorithm, Optimization algorithms range of applications, covering all most all areas including computer networks, security, robotics, bio medical I. INTRODUCTION engineering, control systems ,parallel processing ,data mining, power systems, production engineering and many Optimization is a commonly encountered mathematical more. problem in all engineering disciplines. It literally means Classical problem solving methodologies involve two finding the best possible/desirable solution. Optimization branches: Exact methods (logical, mathematical problems are wide ranging and numerous, hence methods for programming) and Heuristics. Heuristic approach seems to be solving these problems ought to be, an active research topic. superior in solving hard and complex optimization problems, Optimization algorithms can be either deterministic or particularly where the traditional methods fail. BIAs are such stochastic in nature. Former methods to solve optimization heuristics that mimics /imitate the strategy of nature since problems require enormous computational efforts, which tend many biological processes can be thought of as processes of to fail as the problem size increases. This is the motivation for constrained optimization.They make use of many random employing bio inspired stochastic optimization algorithms as decisions which classifies them as a special class of computationally efficient alternatives to deterministic randomized algorithms. Formulating a design for bioinspired approach. Meta-heuristics are based on the iterative algorithms involves choosing a proper representation of improvement of either a population of solutions (as in problem, evaluating the quality of solution using a fitness Evolutionary algorithms, Swarm based algorithms) or a single function and defining operators so as to produce a new set of solution (eg. Tabu Search) and mostly employ randomization solutions. and local search to solve a given optimization problem. A vast literature exists on bio inspired approaches for Towards technology through Nature solving an impressive array of problems and, more recently, a number of studies have reported on the success of such The real beauty of nature inspired algorithms lies in the techniques for solving difficult problems in all key areas of fact that it receives its sole inspiration from nature. They have computer science.The two most predominant and successful the ability to describe and resolve complex relationships from classes or directions in BIAs involves Evolutionary intrinsically very simple initial conditions and rules with little Algorithms and Swarm based Algorithms which are inspired or no knowledge of the search space Nature is the perfect by the natural evolution and collective behavior in animals respectively. But still, this has been further refined so as to Manuscript received March 28, 2012 classify the algorithms based on the area of inspiration from Binitha S, Department of Computer Science , Pondicherry University, nature so as to enhance a broader view over the domain. This Pondicherry, India, 91 9483079670, (e-mail: [email protected]). Dr. S Siva Sathya, Department of Computer Science, Pondicherry paper presents a survey and review on the BIAs along with University, Pondicherry, India, (e-mail: [email protected]). taxonomy and the relevant application areas.

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A Survey of Bio inspired Optimization Algorithms

Fig: Taxonomy and nomenclature of various bio inspired optimization algorithms grouped by the area of inspiration

The organization of the paper is as follows: Section 2 established algorithms among nature inspired algorithms, provides an overview of EAs. The new and emerging which is based on the biological evolution in nature that is algorithms of SI family are discussed in Section 3. Section 4 being responsible for the design of all living beings on earth, introduces algorithms inspired from natural ecosystem. and for the strategies they use to interact with each other. EAs Section 5 sketches a tabular form summary of all the employ this powerful design philosophy to find solutions to algorithms. The conclusion is drawn in Section 6. hard problems. EAs are non-deterministic algorithms or cost based optimization algorithms II. EVOLUTIONARY ALGORITHMS A family of successful EAs comprises (GA), (GP), , (EC)[1] is a paradigm in the evolutionary strategy (ES) and most recent Paddy Field realm that aims at benefiting from Algorithm. The members of the EA family share a great collective phenomena in adaptive populations of problem number of features in common. They are all population-based solvers utilizing the iterative progress comprising growth stochastic search algorithms performing with best-to-survive ,development, reproduction, selection, and survival as seen in criteria. Each algorithm commences by creating an initial a population . EAs are the most well known, classical and

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International Journal of Soft Computing and Engineering (IJSCE) ISSN: 2231-2307, Volume-2, Issue-2, May 2012 population of feasible solutions, and evolves iteratively from 2) Execute each program in the population and assign it a generation to generation towards a best solution. In fitness value according to how well it solves the problem. successive iterations of the algorithm, fitness-based selection 3) Create a new population of computer programs. takes place within the population of solutions. Better i) Copy the best existing programs solutions are preferentially selected for survival into the next ii) Create new computer programs by mutation. generation of solutions. iii) Create new computer programs by crossover (sexual reproduction). A. Genetic Algorithm GA is an evolutionary based stochastic optimization C.Evolution Strategies algorithm with a global search potential proposed by Holland Evolution Strategies was developed by three in 1975[2]. GAs are among the most successful class of students(Bienert, Rechenberg, Schwefel) at the Technical algorithms under EAs which are inspired by the evolutionary University in Berlin in 1964[4] in an effort to robotically ideas of natural selection. They follow the principles of optimize an aerodynamic design problem. Evolution Charles Darwin Theory of survival of the fittest. However, Strategies is a global optimization algorithm inspired by the because of its outstanding performance in optimization, GA theory of adaptation and evolution by means of natural has been regarded as a function optimizer. Algorithm begins selection. Specifically, the technique is inspired by by initializing a population of solution (chromosome).It macro-level or the species-level process of evolution comprises representation of the problem usually in the form of (phenotype, hereditary, variation) unlike genetic algorithm a bit vector. Then for each chromosome evaluate the fitness which deals with micro or genomic level (genome, using an appropriate fitness function suitable for the problem chromosomes, genes, alleles). A very important feature of ES .Based on this ,the best chromosomes are selected into the is the utilization of self-adaptive mechanisms for controlling mating pool, where they undergo cross over and mutation thus the application of mutation. These mechanisms are aimed at giving new set of solutions(offspring). optimizing the progress of the search by evolving not only the The three principal genetic operators in GA involve solutions for the problem being considered, but also some selection, crossover, and mutation. parameters for mutating these solutions. GA is useful and efficient when: Some common Selection and Sampling schemes in ES are  The search space is large complex or poorly known. as follows:  No mathematical analysis is available. (1+1)-ES: This is a simple selection mechanism in which  Domain knowledge is scarce to encode to narrow the works by creating one real-valued vector of object variables search space from its parent by applying mutation with an identical  For complex or loosely defined problems since it works standard deviation to each object variable. Then, the resulting by its own internal rules. individual is evaluated and compared to its parent, and the  Traditional search method fails. better survives to become a parent of the next generation, Even though GAs can rapidly locate good solutions, for while the other is discarded. difficult search spaces, it has some disadvantages: (μ +λ)-ES: Here μ parents are selected from the current 1) GA may have a tendency to converge towards local generation and generate λ offspring, through some optima rather than the global optimum of the problem if the recombination and /or mutation operators. Out of the union of fitness function is not defined properly. parents and offspring (μ + λ), the best μ kept for next 2) Operating on dynamic data sets is difficult. generation. It inherently incorporates elitism. 3) For specific optimization problems, and given the same (μ, λ)-ES: Currently used variant is (μ , λ)-ES .Here μ amount of computation time, simpler optimization algorithms parents selected from the current generation and used to may find better solutions than GAs. generate λ offspring (with λ >= μ ) and only the best μ 4) GAs are not directly suitable for solving constraint offspring individuals form the next generation discarding the optimization problems parents completely. This does not incorporate elitism. B. Genetic Programming D. Differential Evolution Proposed by Koza in 1992[3],GP being an extension to Another paradigm in EA family is differential evolution Genetic algorithms differs from the latter in terms of (DE) proposed by Storn and Price in 1995[5]. DE is similar to representation of the solution.GP represent an indirect GAs since populations of individuals are used to search for an encoding of a potential solution (in the form of a tree),in optimal solution. The main difference between Gas and DE is which search is applied to the solution directly , and a solution that, in GAs, mutation is the result of small perturbations to could be a computer program. The second fundamental the genes of an individual while in DE mutation is the result of difference is in the variable-length representation adopted by arithmetic combinations of individuals. At the beginning of GP in contrast with the fixed length encoding in GA. The the evolution process, the mutation operator of DE favors population in GP generates diversity not only in the values of exploration. As evolution progresses, the mutation operator the genes but also in the structure of the individuals. favors exploitation. Hence, DE automatically adapts the Hence GP resembles evolution of a population of computer mutation increments to the best value based on the stage of the programs. The four steps in Genetic programming involves: evolutionary process. Mutation in DE is therefore not based 1) Generate an initial population of computer programs on a predefined probability density function. comprising the functions and terminals. Advantages:

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 DE is easy to implement, requires little parameter tuning ―swarm‖ comes from the irregular movements of the particles  Exhibits fast convergence in the problem space.SI has been developed alongside with  It is generally considered as a reliable, accurate, robust EAs. Some most well-known strategies in this area are and fast optimization technique. discussed below. These trajectory tracking algorithms being Limitations: inspired by the collective behavior of animals, exhibit  According to Krink et al. (2004), noise may adversely decentralized, self-organized patterns in the foraging process. affect the performance of DE due to its greedy nature.  Also the user has to find the best values for the Swarm Intelligence Principles: SI can be described by problem-dependent control parameters used in DE and this is considering five fundamental principles. a time consuming task. 1) Proximity Principle: the population should be able to A self-adaptive DE (SDE) algorithm can eliminates the carry out simple space and time computations. need for manual tuning of control parameters 2) Quality Principle: the population should be able to respond to quality factors in the environment. E. Paddy Field Algorithm 3) Diverse Response Principle: the population should not Recent algorithm Proposed by Premaratne et al in 2009 commit its activity along excessively narrow channels. [6], which operates on a reproductive principle dependant on 4) Stability Principle: the population should not change its proximity to the global solution and population density mode of behavior every time the environment changes. similar to plant populations .Unlike evolutionary algorithms 5) Adaptability Principle: the population should be able to ,it does not involve combined behavior nor crossover between change its behavior mode when it is worth the computational individuals instead it uses pollination and dispersal.PFA price constitutes five basic steps. A. Particle Swarm Optimization 1. Sowing: The algorithm operates by initially scattering seeds (initial population p0) at random in an uneven field. Particle swarm optimization (PSO) is a computational 2. Selection: Here the best plants are selected based on a intelligence oriented, stochastic, population-based global threshold method so as to selectively weed out unfavorable optimization technique proposed by Kennedy and Eberhart in solutions and also controls the population. 1995[8]. It is inspired by the social behavior of bird flocking 3. Seeding: In this stage each plant develops a number of searching for food. PSO has been extensively applied to many seeds proportional to its health. The seeds that drop into the engineering optimization areas due to its unique searching most favorable places (most fertile soil, best drainage, soil mechanism, simple concept, computational efficiency, and moisture etc.) tend to grow to be the best plants (taller) and easy implementation. In PSO, the term ―particles‖ refers to produce more number of seeds. The highest plant of the population members which are mass-less and volume-less (or population would correspond to the location of the optimum with an arbitrarily small mass or volume) and are subject to conditions and the plant‘s fitness is determined by a fitness velocities and accelerations towards a better mode of function. behavior. Each particle in the swarm represents a solution in a 4. Pollination: For seed propagation pollination is a major high-dimensional space with four vectors, its current position, factor either via animals or through wind. High population best position found so far, the best position found by its density would increase the chance of pollination for pollen neighborhood so far and its velocity and adjusts its position in carried by the wind the search space based on the best position reached by itself 5. Dispersion: In order to prevent getting stuck in local (pbest) and on the best position reached by its neighborhood minima, the seeds of each plant are dispersed .Depending on (gbest) during the search process. In each iteration, each the status of the land it will grow into new plants and continue particle updates its position and velocity as follows: i i i the cycle. xk11 x k v k As per no free lunch rule, the PFA only has a lower i i i i g i computational cost. Since the PFA doesn‘t have crossover, vk1 v k  c 1 r 1()() p k  x k  c 2 r 2 p k  x k the optimum solution can be migrated to reach the optimum i where, xk represents Particle position solution. i vk represents Particle velocity pi represents Best "remembered" position III. SWARM INTELLIGENCE k cc, represents cognitive and social parameters, 12 Swarm Intelligence (Kennedy and Eberhart, 2001[7]) is a rr12, are random numbers between 0 and 1 recent and emerging paradigm in bio inspired computing for Steps in PSO algorithm can be briefed as below: implementing adaptive systems. In this sense, it is an extension of EC. While EAs are based on genetic adaptation 1) Initialize the swarm by assigning a random position in of organisms SI is based on collective social behavior of the problem space to each particle. organisms. As per definitions in literature, Swarm Intelligent 2) Evaluate the fitness function for each particle. encompasses the implementation of collective intelligence of 3) For each individual particle, compare the particle‘s groups of simple agents that are based on the behavior of real fitness value with its pbest . If the current value is better than world insect swarms, as a problem solving tool. The word

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International Journal of Soft Computing and Engineering (IJSCE) ISSN: 2231-2307, Volume-2, Issue-2, May 2012 the pbest value, then set this value as the pbest and the current based on the generic behavior of natural ants. ACO is particle‘s position, xi, as pi. structured into three main functions as follows: 4) Identify the particle that has the best fitness value. The AntSolutionsConstruct: This function performs the value of its fitness function is identified as guest and its solution construction process where the artificial ants move position as pg. through adjacent states of a problem according to a transition 5) Update the velocities and positions of all the particles rule, iteratively building solutions. using (1) and (2). Pheromone Update: performs pheromone trail updates. 6) Repeat steps 2–5 until a stopping criterion is met (e.g., This may involve updating the pheromone trails once maximum number of iterations or a sufficiently good fitness complete solutions have been built, or updating after each value). iteration. In addition to pheromone trail reinforcement, ACO also Advantages over Genetic Algorithm: includes pheromone trail evaporation. Evaporation of the (a) PSO is easier to implement and there are fewer pheromone trials helps ants to ‗forget‘ bad solutions that were parameters to adjust. learned early in the algorithm run. (b) PSO has a more effective memory capability than GA. DeamonActions: is an optional step in the algorithm which (c) PSO is more efficient in maintaining the diversity of the involves applying additional updates from a global swarm, since all the particles use the information related to the perspective (for this no natural counterpart exists). This may most successful particle in order to improve themselves, include applying additional pheromone reinforcement to the whereas in Genetic algorithm, the worse solutions are best solution generated (known as offline pheromone trail discarded and only the new ones are saved; i.e. in GA the update). population evolve around a subset of the best individuals. An alternative approach, called the ant colony system There are many similarities between the PSO and EAs. (ACS) has been introduced by Dorigo and Gambardella Both of them initialize solutions and update generations, (1997) to improve the performance of ant system. It is based while the PSO has no evolution operators as does the latter. In on four modifications of ant system: a different transition rule, a PSO, particles try to reach the optimum by following the a different pheromone trail update rule, the use of local current global optimum instead of using evolutionary updates of pheromone trail to favor exploration, and the use of operators, such as mutation and crossover. candidate list to restrict the choice. It is claimed that the PSO, in addition to continuous C. Artificial Bee Colony Algorithm (ABC) functions, has been showing stability and convergence in a multidimensional complex space also. (Clerc and Kennedy, Based on the behavior of the bees in nature, various 2002). swarm intelligence algorithms are available. These algorithms are classified into two; foraging behavior and mating B. Ant Colony Optimization behavior. Examples of algorithms simulating the foraging ACO is among the most successful swarm based behavior of the bees include the Artificial Bee Colony (ABC) algorithms proposed by Dorigo & Di Caro in 1999 [9] .It is a ,the Virtual Bee algorithm proposed by Yang , the Bee meta heuristic inspired by the foraging behavior of ants in the Colony Optimization algorithm proposed by Teodorovic and wild, and moreover, the phenomena known as stigmergy, term Dell‘Orco , the BeeHive algorithm proposed by Wedde et al., introduced by Grasse in 1959.Stigmergy refers to the indirect the Bee Swarm Optimization algorithm proposed by Drias et communication amongst a self-organizing emergent system al. and the Bees algorithm proposed by Pham et al. An via individuals modifying their local environment. The most individual entity (e.g., a bee in a bee colony) exhibit a simple interesting aspect of the collaborative behavior of several ant set of behavior policies (e.g., migration, replication, death), species is their ability to find shortest paths between the ants' but a group of entities (e.g., a bee colony) shows complex nest and the food sources by tracing pheromone trails Then, emergent behavior with useful properties such as scalability ants choose the path to follow by a probabilistic decision and adaptability. biased by the amount of pheromone: the stronger the Artificial Bee Colony is a predominant algorithm pheromone trail, the higher its desirability. Because ants in simulating the intelligent foraging behavior of a honeybee turn deposit pheromone on the path they are following, this swarm, proposed by Karaboga and Basturk [10].In ABC behavior results in a self-reinforcing process leading to the algorithm, the colony of artificial bees contains three groups formation of paths marked by high pheromone concentration. of bees: employed bees, onlookers and scouts. By modeling and simulating ant foraging behavior, brood A bee waiting on the dance area for making a decision to sorting, nest building and self-assembling, etc. algorithms choose a food source is called onlooker and one going to the can be developed that could be used for complex, food source visited by it before is named employed bee. The combinatorial optimization problems. other kind of bee is scout bee that carries out random search The first ant algorithm, named ‗‗Ant System‖ (AS), was for discovering new sources. The position of a food source developed in the nineties by Dorigo et al. (1996) and tested represents a possible solution to the optimization problem and successfully on the well known benchmark Travelling the nectar amount of a food source corresponds to the quality Salesman Problem. The ACO meta heuristic was developed (fitness) of the associated solution. A swarm of virtual bees is (Dorigo & Di Caro, 1999;) to generalize, the overall method generated and started to move randomly in two-dimensional of solving combinatorial problems by approximate solutions search space. Bees interact when they find some target nectar

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A Survey of Bio inspired Optimization Algorithms and the solution of the problem is obtained from the intensity Searching is a random search adopted by fish in search of of these bee interactions. food, with a tendency towards food concentration. The A randomly distributed initial population solutions objective is to minimize FS (food satisfaction). (xi=1,2…D) is being dispread over the D dimensional Swarming: aims in satisfying food intake needs, problem space. An employed bee produces a modification on entertaining swarm members and attracting new swarm the position (solution) in her memory depending on the local members. A fish located at Xi has neighbors within its visual. information (visual information) and tests the nectar amount Xc identifies the center position of those neighbors and is (fitness value) of the new source (new solution). Provided that used to describe the attributes of the entire neighboring the nectar amount of the new one is higher than that of the swarm. If the swarm center has greater concentration of food previous one, the bee memorizes the new position and forgets than is available at the fish‘s current position Xi (i.e., FSc < the old one. After all employed bees complete the search FSi), and if the swarm (Xc) is not overly crowded (ns/n < δ), process; they share the nectar information of the food sources the fish will move from Xi to next Xi+1, toward Xc . and their position information with the onlooker bees on the Following behavior implies when a fish locates food, dance area. neighboring individuals follow. Within a fish‘s visual, certain In the next phase Reproduction, based on the probability fish will be perceived as finding a greater amount of food than value associated with the food source, Pi,the artificial others, and this fish will naturally try to follow the best onlooker bee chooses a food source one(Xmin) in order to increase satisfaction(i.e., gain fit relatively more food[FSmin < FSi] and less crowding[nf/n < Pi  N δ]). nf represents number of fish within the visual of Xmin. fit Three major parameters involved in FSA include visual  n distance (visual), maximum step length (step), and a crowd n1 factor. FSA effectiveness seems primarily influenced by the

former two (visual and step). Where, N is the number of food sources (that is the number of employed bees), fit i is the fitness value of the solution i E. Intelligent Water Drops Algorithm (IWD) which is proportional to the nectar amount of the food source IWD is an innovative population based method proposed in the position i. by Hamed Shah-hosseini in 2007[12] .It is inspired by the In the last phase, Replacement of bee and Selection, if a processes in natural river systems constituting the actions and position can not be improved further through a predetermined reactions that take place between water drops in the river and number of cycles, then that food source is assumed to be the changes that happen in the environment that river is abandoned. The value of predetermined number of cycles is flowing. Based on the observation on the behavior of water an important control parameter of the ABC algorithm, which drops, an artificial water drop is developed which possesses is called ―limit‖ for abandonment. After each candidate some of the remarkable properties of the natural water drop. source position is produced and then evaluated by the This Intelligent Water Drop has two important properties: artificial bee, its performance is compared with that of its old 1. The amount of the soil it carries now, Soil (IWD). one. If the new food has an equal or better nectar than the old 2. The velocity that it is moving now, Velocity (IWD). source, it is replaces the old one in the memory. Otherwise, The environment in which the water flows depend on the the old one is retained in the memory. problem under consideration. An IWD moves in discrete The local search performance of ABC algorithm depends finite-length steps. From its current location to its next on neighborhood search and greedy selection mechanisms location, the IWD velocity is increased by the amount performed by employed and onlooker bees. The global search nonlinearly proportional to the inverse of the soil between the performance of the algorithm depends on random search two locations. Moreover, the IWD‘s soil is increased by process performed by scouts and neighbor solution removing some soil of the path joining the two locations. The production mechanism performed by employed and onlooker amount of soil added to the IWD is inversely (and bees nonlinearly) proportional to the time needed for the IWD to D. Fish Swarm Algorithm pass from its current location to the next location. This duration of time is calculated by the simple laws of physics for The fish swarm algorithm (FSA) is a new linear motion. Thus, the time taken is proportional to the population-based/swarm intelligent evolutionary computation velocity of the IWD and inversely proportional to the distance technique proposed by Li et al. [11] in 2002 which is inspired between the two locations. by the natural schooling behavior of fish.FSA presents a Another property of an IWD is that it prefers the paths with strong ability to avoid local minimums in order to achieve low soils on its beds to the paths with higher soils on its beds. global optimization. A fish is represented by its To implement this behavior of path choosing, a uniform D-dimensional position Xi = (x1, x2, . . ., xk, . . ., xD), and random distribution is used among the soils of the available food satisfaction for the fish is represented as FSi. The paths such that the probability of the next path to choose is relationship between two fish is denoted by their Euclidean inversely proportional to the soils of the available paths. The distance dij = ||Xi −Xj||. lower the soil of the path, the more chance it has for being FSA imitates three typical behaviors, defined as ―searching selected by the IWD. for food‖, ―swarming in response to a threat‖, and ―following to increase the chance of achieving a successful result‖. F. Bacterial Foraging Optimization Algorithm

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International Journal of Soft Computing and Engineering (IJSCE) ISSN: 2231-2307, Volume-2, Issue-2, May 2012 This has been evolving as a new and promising branch in the next iteration. The mutation can be uniform, Gaussian or Bio inspired Algorithms that can bridge the gap between exponential. microbiology and engineering. These classes of algorithms inherit the characteristics of bacterial foraging patterns such H. Firefly algorithm as chemo taxis, metabolism, reproduction and quorum sensing. The complex and organized activities exhibited in Firefly algorithm proposed by Yang [15] can be bacterial foraging patterns inspire a new approach to solve considered as an unconventional swarm-based heuristic complex optimization problems. algorithm for constrained optimization tasks inspired by the The Bacterial Foraging Optimization Algorithm (BF0) was flashing behavior of fireflies. The algorithm constitutes a introduced by Passino in 2002[13]. Foraging is a population-based iterative procedure with numerous agents phenomenon of a bacterial colony rather than an individual (perceived as fire flies) concurrently solving a considered behavior. BFOA consists of three principal mechanisms optimization problem. Agents communicate with each other namely, chemo taxis, reproduction, and elimination-dispersal. via bioluminescent glowing which enables them to explore Chemotaxis (cell movement) is the activity of bacteria cost function space more effectively than in standard gathering to nutrient-rich areas in a spontaneous fashion; in distributed random search. Intelligence optimization this context, a cell-to-cell communication mechanism is technique is based on the assumption that solution of an established to simulate the biological behavior of bacterial optimization problem can be perceived as agent (fire fly) movement (swim/tumble). which glows proportionally to its quality in a considered Reproduction comes from the concept of natural problem setting. Consequently each brighter fire fly attracts selection; under this procedure, only the best adapted bacteria its partners (regardless of their sex), which makes the search tend to survive and transmit their genetic characters to space being explored more efficiently. succeeding generations, while the less adapted ones tend to The firefly algorithm has three particular idealized rules perish. which are based on some of the basic flashing characteristics Elimination-dispersal events randomly select parts of the of real fireflies. They are the following: bacteria population to diminish and disperse into random 1) All fireflies are unisex and they will move towards more positions in the environment; this way the algorithm ensures attractive and brighter ones regardless of their sex. the diversity of the species, and prevents getting trapped to 2) The degree of attractiveness of a firefly is proportional to local optima, improving global search ability. its brightness. Also the brightness may decrease as the distance from the other fire flies increases due to the fact that the air absorbs light. If there is not a brighter or more G. Artificial Immune System Algorithm attractive fire fly than a particular one it will then move Proposed by Dasgupta,in 1999 [14].Artificial Immune randomly. algorithm is based on clonal selection principle and is a 3) The brightness or light intensity of a fire fly is population based algorithm .AIS is inspired by the human determined by the value of the objective function of a given immune system which is a highly evolved, parallel and problem. distributed adaptive system that exhibits the following Advantage: strengths: immune recognition, reinforcement learning, Mainly uses real random numbers and is based on the feature extraction, immune memory, diversity and robustness. global communication among the swarm particles (ie the The artificial immune system (AIS) combines these strengths firefly), hence more effective in multi objective optimization. and has been gaining significant attention due to its powerful adaptive learning and memory capabilities. I. Group search optimizer The main search power in AIS relies on the mutation operator and hence, the efficiency deciding factor of this technique. The steps in AIS are as follows: The group search optimizer (GSO), was investigated at 1. Initialization of antibodies (potential solutions to the the University of Liverpool (He et al., 2006)[16].It is a problem). Antigens represent the value of the objective population based optimization algorithm, which adopts the function f(x) to be optimized. producer–scrounger (PS) model metaphorically for designing 2. Cloning, where the affinity or fitness of each antibody is optimum searching strategies, inspired by animal foraging determined. Based on this fitness the antibodies are cloned; behavior. Similar to the PSO, the population of the GSO is that is the best will be cloned the most. The number of clones called a group and each individual in the population is called generated from the n selected antibodies is given by: a member. In the search space, each member knows its own Nc =∑ round (β*j/i) i = 1,2…….n , position, its head angle and a head direction, which can be Where Nc is the total number of clones, β is a multiplier calculated from the head angle via a polar to Cartesian factor and j is the population size of the antibodies. co-ordinate transformation. A group constitutes three types of 3. Hypermutation: The clones are then subjected to a members: producers, scroungers and rangers. hyper mutation process in which the clones are mutated in Producers: perform producing strategies, searching for inverse proportion to their affinity; the best antibody‘s clones food. are mutated lesser and worst antibody‘s clones are mutated Scroungers: perform scrounging strategies, joining most. The clones are then evaluated along with their original resources uncovered by others. antibodies out of which the best N antibodies are selected for

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Rangers: perform random walk motions and will be such as air, soil, water etc. There can be numerous and dispersed from their current positions. complex types of interactions among the species of The producer will find the best point with the best resource ecosystem. Also this can occur as interspecies interaction (fitness value). If the best point has a better fitness than its (between species) or intra species interaction (within current position (in minimization problem as an example), species). The nature of these interactions can be cooperative/ then it will fly to this point. Or it will stay in its current competitive. Cooperation includes division of labor and position and turn its head to a new randomly generated angle. represents the core of sociality. Inter species interaction can If the producer cannot find a better area after a number of be of mainly 3 types based on the outcome of interaction iterations, it will turn its head back to zero degree. For (positive, negative, neutral) termed as mutualism, parasitism, scroungers, area copying is adopted, which is the commonest commensalism respectively. Examples of cooperation in scrounging behavior in sparrows .Random walks are nature: within species (i.e., homogeneous cooperation, also employed by the rangers. If a scrounger (or ranger) finds a called social evolution), as in the social foraging behaviors of better location than the current producer and other animal herds, bird flocks, insect groups and bacterial scroungers, in the next searching iteration it will switch to be a colonies; between species (i.e., heterogeneous cooperation, producer, and all the other members, including the producer also called symbiosis), as in the mutualism between human in the previous searching iteration, will perform scrounging and honey guide. Moreover Biogeography includes the study strategies. It is also assumed that the producer and the of distribution of species over specified time and space. scroungers do not differ in their relevant phenotypic A. PS20 characteristics. Therefore, they can switch between the two roles. Proposed by Hanning Chen and Yunlong Zhu in 2008 [18],inspired by the ideas from the co evolution of symbiotic J. Shuffled frog Leaping Algorithm species in natural ecosystems and heterogeneous interaction Proposed by Muzaffar Eusuff and Kevin Lansey in between species. , PS2O is a multi-species optimizer which 2003[17].Shuffled frog-leaping algorithm (SFLA) is a extends the dynamics of the canonical PSO algorithm by population-based cooperative meta-heuristic algorithm with adding a significant ingredient that takes into account the efficient mathematical function and global search capability. symbiotic co evolution between species. The SFLA is a search metaphor inspired by natural memetics The algorithm initially create an ecosystem containing a and evolution. It is inspired by the interactive behavior and species set X = {S1, S2,. . .,Sn}, and each species possesses a global exchange of information of frogs searching for food members set Sn= {xi,x2…xm} i.e., totally n * m (n species laid on discrete stones randomly located in a pond. It and m members within species) individuals co evolve in the combines the advantages of the genetic-based memetic ecosystem. The ith member of the kth species is characterized algorithm (MA) and the social behavior-based PSO algorithm by the vector xik={xi1k,xi2k…xidk} .and the fitness being with such characteristics as simple concept, fewer parameters f(xki) and lower value of the fitness represents the higher adjustment, prompt formation, great capability in global ability of survival. Under this presumed external search and easy implementation. environmental stress, all individuals in this model co evolve The steps in SFLA include the following: to the states of lower and lower fitness by cooperating each  Initial population: Individual frogs are equivalent to the other both within species and between species. GA chromosomes, and represent a set of solutions. In each generation t, each individual xki will have social  Sorting and distribution : Frogs are sorted in evolution as well as symbiotic evolution. Social evolution descending order based on their fitness values, then each frog resembles the cooperation between individuals of the same is distributed to a different subset of the whole population species. Due to the socio biological background of the called a memeplex, the entire population is divided into m canonical PSO model, xki evolve according to the rules of the memeplexes, each containing n frogs canonical PSO algorithm in this process thus accelerating  Memeplex evolution: An independent local search is towards the personal best position and the best position found conducted for each frog memeplex, in what is called by its neighbors where as symbiotic evolution addresses the memeplex evolution. cooperation between individuals of distinct species. Xki  Shuffling: After a defined number of memetic beneficially interacts with and rewards all its symbiotic evolutionary steps, frogs are shuffled among memeplexes, partners (individuals of dissimilar species), i.e., each enabling frogs to interchange messages among different symbiotic partner donates its knowledge to aid other partners. memplexes and ensure that they move to an optimal position, Then xki accelerate towards its symbiotic partner of the best similar to particles in PSO. fitness. Finally if all individuals in the ecosystem cannot find a  Terminal condition : If a global solution or a fixed better position after a (here a is a constant) generations, it iteration number is reached, the algorithm stops means that all species suffer a severe external environmental stress. Then randomly choose half species of the ecosystem to IV. ECOLOGY go extinct to release this stress for other species to survive. At the same time, randomly initiate equal number of species in Natural ecosystems provides rich source of mechanisms the ecosystem for new experimentations and adaptations. In for designing and solving difficult engineering and computer PS2O cooperation occurred in two levels, i.e., species level science problems. It comprises the living organisms along (interaction between species) and individual level (interaction with the abiotic environment with which organisms interact within species).

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International Journal of Soft Computing and Engineering (IJSCE) ISSN: 2231-2307, Volume-2, Issue-2, May 2012 B. Invasive Weed Colony Optimization algorithm is intended to determine the solutions which Invasive Weed Optimization (IWO) is a numerical maximize the HSI by immigrating and emigrating features of stochastic search algorithm proposed by Mehrabian and the habitats. Lucas in 2006 [19], inspired by the ecological process of In BBO, there are two main operators: migration (which weed colonization and distribution. It is capable of solving includes both emigration and immigration) and mutation .A general multi-dimensional, linear and nonlinear optimization habitat H is a vector of N (SIVs) integers initialized randomly. problems with appreciable efficiency .Adapting with their Before optimizing, each individual of population is evaluated environments, invasive weeds cover spaces of opportunity and then follows migration and mutation step to reach global left behind by improper tillage; followed by enduring minima. In migration the information is shared between occupation of the field. Their behavior changes with time habitats that depend on emigration rates μ and immigration since as the colony become dense there is lesser opportunity rates λ of each solution. Each solution is modified depending of life for the ones with lesser fitness. The steps of the on probability Pmod that is a user defined parameter. Each algorithm are described as below: individual has its own λ and μ and are functions of the number Initialization: includes a population of initial solutions of species K in the habitat .Poor solutions accept more useful being dispread over the D dimensional problem space with information from good solution, which improve the random positions. exploitation ability of algorithm. In BBO, the mutation is used Fitness Evaluation: Evaluate the individual fitness and to increase the diversity of the population to get the good rank the population according to their fitness. solutions. Reproduction: Allowed to produce seeds depending on its Features: own and the colony's lowest and highest fitness. This helps to  In BBO the original population is not discarded after concentrate on the highest fitness values in the search domain each generation. It is rather modified by migration. and hence increases convergence towards the group best  Another distinctive feature is that, for each generation, value. BBO uses the fitness of each solution to determine its Spatial Dispersal: The generated seeds are being immigration and emigration rate. randomly dispersed over the D dimensional search space by normally distributed random numbers with mean V. COMPARATIVE ANALYSIS:

Table: Brief summary of the Bio Inspired optimization algorithms equal to zero; but varying variance. The standard deviation This section presents a comparative analysis of the (SD), σ, of the random function will be reduced from a algorithms seen so far in terms of the representation, previously defined initial value σ initial, to a final value, σ operators, areas of application and control parameters final, in every generation, which is given as follows: (iter max iter )n iter n ()() initial   final   final (iter max) Selection: Select the Pmax or maximum number of plants from the best plants reproduced. C. Biogeography- Based Optimization Biogeography-Based Optimization (BBO) is a global optimization algorithm developed by Dan Simon in 2008[20] and is inspired by mathematical models of biogeography by Robert MacArthur and Edward Wilson. Biogeography is the study of distribution of species in nature over time and space; that is the immigration and emigration of species between habitats. The application of this idea to allow information sharing between candidate solutions. Each possible solution is an island and their features that characterize habitability are called suitability index variables (SIV). The fitness of each solution is called its habitat suitability index (HSI) and depends on many features of the habitat. High-HSI solutions tend to share their features with low-HSI solutions by emigrating solution features to other habitats. Low- HSI solutions accept a lot of new features from high-HSI solutions by immigration from other habitats. Immigration and emigration tend to improve the solutions and thus evolve a solution to the optimization problem. The value of HSI is considered as the objective function, and the

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NAME REPRESENTATION OPERATORS AREAS OF APPLICATION CONTROL OF PARAMETERS ALGO RITH M

optimization problems in data mining and rule Binary, real no:s, Crossover, Population size, extraction, dynamic and multiple criteria permutation of mutation, max.generation web-site optimizations , decision thresholds for elements, list of rules, selection, number, cross distributed detection in wireless sensor program elements, data over probability, networks , Computer aided design path GA structure, tree, Inversion , mutation planning of mobile robots, fixed charge Matrix probability, transportation problem, various scheduling Gene length of problems ,assignment problems, flight control Silencing chromosome, system design, pattern recognition , reactive chromosome power dispatch , sensor-based robot path encoding planning, training of radial basis function, multi-objective vehicle routing problem, minimum energy broadcast problem in wireless ad hoc network, software engineering problems, pollutant emission reduction problem in the manufacturing industry, Power System Optimization problems, port folio Optimization ,optimal learning path in e learning, Web page classification system ,closest sting problem in bioinformatics ,structural optimization, defect identification system, molecular modeling, web service selection, cutting stock problem,drug design, personalized e-learning system, SAT Solvers

portfolio optimization, Design of image GP Tree structure Crossover, Population size, exploring agent, epileptic pattern recognition, (terminals & function Maximum automated synthesis of analogue electrical set) Reproduction number of circuits symbolic regression, robotics, data ,mutation, generations, mining(Automatic feature extraction, permutation,E Probability of classification etc.),cancer diagnosis, power diting, crossover, transformer fault classification automatic Encapsulation Probability of synthesis of analog electrical circuits ,,Decimation mutation

parameter estimation (Hatanaka et al., 1996), ES Real-valued vectors Mutation, Population size, image processing (Gonzalez et al., 2001) Selection, Maximum ,computer vision system (Bergener et al., discrete number of 2001),Task scheduling and car automation Recombinatio generations, ,structural optimization, for n Probability of gas-turbine fault-diagnoses, A multi-parametric crossover, evolution strategies algorithm for vehicle Probability of routing problems, clustering mutation

unsupervised image classification ,clustering , DE Real-valued vectors Crossover, S Poulation size digital filter design , optimization of non-linear mutation ,Nd diamension of functions , global optimization of non-linear problem,F scale chemical engineering processes and ,selection factor,Pr multi-objective optimization . probability of crossover

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multimodal biomedical image registration number of PSO D dimensional initializer, (Wachowiak et al., 2004) and the Iterated Prisoner‘s particles, vector for updater and Dilemma (Franken and Engelbrechet, 2005), Dimension of position evaluator. classification of instances in multiclass databases, particles, Range ,speed,best state feature selection, web service composition course of particles, composition, Vmax, Learning Power System Optimization problems (economic factors: c1c2, dispatching), Edge detection in noisy images, finding inertia weight , optimal machining parameter assembly line maximum balancing problem in production and operations number of management, various scheduling problems ,vehicle iterations routing problems, prediction of tool life in ANN, multi-objective, dynamic, constrained and combinatorial optimization problems ,QoS in adhoc multicast, Anomaly detection, color image segmentation , sequential ordering problem, constrained portfolio optimization problem, selective particle regeneration for data clustering , Extracting rules from fuzzy neural network, machinery fault detection, Unit commitment computation, Signature verification TSP Problem, Quadratic Assignment problem (QAP) number of ants ACO Undirected graph Pheromone Job-Shop Scheduling problem. dynamic problem of ,iterations , Update and data network routing, a shortest path problem where pheromone Measure, trail properties of the system such as node availability evaporation rate, evaporation vary over time. continuous optimization and parallel amount of processing implementations . vehicle routing reinforcement problem ,graph colouring and set covering, agent-based dynamic scheduling, digital image processing, classification problem in data mining, protein folding problem Continuous function optimization. tuning parameters PFA Linear=[x1,x2..x] Dispersal, size of in PID Controllers in higher order systems and RBF pollination population, the Neural Network Parameters Optimization boundary of parameter space, initial value of the maximum number of seeds attribute string( a immune computer security ,anomaly detection, clustering Antibody AIS real-valued operators( /classification, numeric function optimization, population size ,. vector), integer cloning, hyper ,learning ,IIR filter design, control, robotics, data Number of string , binary mutation and mining[149],virus detection, pattern recognition , antibodies to be string, symbolic selection based tuning of controllers, multi- modal optimization, job selected for string on elitism) shop scheduling hyper-mutation ,number of antibodies to be replaced, multiplier factor β D-dimensional Scheduling problems, image segmentation, number of food ABC Reproduction, vector capacitated vehicle routing problem , sources which replacement of (xi=1,2…D) (WSNs),assembly line balancing problem, Solving is equal to the bee, selection reliability redundancy allocation problem, training number of

neural networks , XOR, Decoder–Encoder and 3-Bit employed or Parity benchmark problems , pattern onlooker bees classification , reliability redundancy allocation (SN), the value problems, clustering, resource-constrained project of limit, the scheduling problem, p-center problem maximum cycle number (MCN)

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FSA Xi = (x1, x2, . . ., Swarming function optimization , Parameter estimation , Visual xk, . . .,xD), ,following, combinatorial optimization], least squares support distance,max searching vector machine and geo technical engineering step length problems ,crowd factor Truss structure design, benchmark functions (He et GSO Unit vector Scrounging, Population size, al., 2006) and applied for optimal power flow ranging, percentage of problems (Fei et al.,2007), mechanical design rangers, no: of optimization problems, multi objective optimization, producing rangers, Head Optimal placement of FACTS devices, machine angle, position, condition monitoring, optimal location and capacity maximum of distributed generations. pursuit angle, maximum turning angle, maximum pursuit distance Color Image Segmentation , Solving TSP , number of frogs

Automatic recognition of speech emotion water , P, number of Unit Commitment Problem , Grid Task scheduling, memeplexes, Optimal viewpoint selection for volume rendering , and number of SFLA Xi=(xi1, xi 2, . . . multi-user detection in DS-CDMA distribution , evolutionary . . . , xiS) Fuzzy controller design , Optimal Reactive Power iterations for Replacement, Flow , A Web Document Classification , Mobile each memeplex shuffling robot path planning , classification rule mining , before shuffling. Combined Economic Emission Dispatch , Job-shop scheduling, ground water model calibration problems, Multicast Routing Optimization θ i ( j, k,l) inverse airfoil design ,application for harmonic Reproduction, Dimension of represents i-th estimation problem in power systems, optimal power chemotaxis, the search space. bacterium at system stabilizers design , tuning the PID controller Dispersion , ,number of jth chemotactic, of an AVR, an optimal power flow solution, machine elimination bacteria , k-th reproductive learning, an application of job shop scheduling BFA number of and l-th benchmark problems; the parameters of membership chemotactic elimination functions and the weights of rules of a fuzzy rule set steps , number dispersal step. are estimated, transmission loss reduction of elimination ,implemented as the parameter estimation of and dispersal nonlinear system model (NSM) for heavy oil thermal events , number cracking, evaluation of independent components to of reproduction work with mixed signals, solve constrained steps , economic load dispatch problems ,application in the probability of null steering of linear antenna arrays by controlling elimination and the element amplitudes, applications in multi dispersal, objective optimization. location of each bacterium ,no: of iterations, step size c(i) weed Vector in D Reproduction, time-modulated linear antenna array synthesis, population size, dimensional dispersal, cooperative multiple task assignment of UAV, modulation IWCO space selection fractional order PID Controller, Training of index ,standard Feed-Forward Neural Networks, Nash equilibrium deviations search in electricity markets, blind multi-user detection for MC-CDMA interference suppression over multipath fading channel, recommender system

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Cooperative Cognitive Wireless Communication, D dimensional initializer, number of constructing collaborative service systems (CSSs) vector for updater particles, PS2O position , speed, ,extinction, Dimension of best state evaluator particles, Range of particles,

Vmax, Learning factors: inertia weight , maximum number of iterations

general benchmark functions, constrained H=h1,h2..hn as migration number of optimization ,the sensor selection problem for individuals of (emigration habitats aircraft engine health estimation , power system habitat. and (population optimization, groundwater detection and satellite immigration) , size), maximum image classification , web-based BBO graphical user mutation migration rates, interface , global numerical optimization, optimal mutation rate, BBO meter placement for security constrained state estimation

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VI. CONCLUSION REFERENCES Bio inspired algorithms are going to be a new revolution [1] Back, T. 1996: Evolutionary algorithms in theory and in computer science. The scope of this area is really vast since practice. Oxford University Press. as compared to nature, computer science problems are only a [2] J.H. Holland, Genetic algorithms and the optimal subset, opening a new era in next generation computing, allocation of trials, SIAM J. Comput. 2 (2) (1973) 88–105 modeling and algorithm engineering. This paper provides an [3] Koza, John R. 1992. Genetic Programming: On the overview of a range of BIAs drawn from an evolutionary Programming of Computers by Means of Natural metaphor or natural phenomena including the EAs (GA, GP, Selection. Cambridge, MA: The MIT Press. ES, DE and PFA), SI algorithms(PSO, ACO, ABC, BFA, [4] Beyer, H.G. and Schwefel, H.P. 2002: Evolution FFA, AIS, FSA, IWD, SFLA, GSO) and ecology inspired strategies. Natural Computing 1,3–52. algorithms (IWC, PS2O, BBO).Generally speaking, almost [5] R. Storn, K. Price, Differential evolution – a simple and all of the EA and SI algorithms perform with heuristic efficient heuristic for global optimization over population-based search procedures that incorporate random continuous spaces, Journal of Global Optimization 11 variation and selection. It has been witnessed that the (1997) 341–359. applications and growth of natural computing in the last years [6] Upeka Premaratne , Jagath Samarabandu, and Tarlochan is very drastic and has been applied to numerous optimization Sidhu, ―A New Biologically Inspired Optimization problems in computer networks, control systems, Algorithm‖,Fourth International Conference on bioinformatics, data mining, game theory, music, biometrics, Industrial and Information Systems, ICIIS 2009,28-31 power systems, image processing, industry and engineering, December 2009, Sri Lanka. parallel and distributed computing, robotics, economics and [7] Bonabeau, E., Dorigo, M. and Theraulaz, G.1999: finance, forecasting problems, applications involving the Swarm intelligence. Oxford University Press security of information systems etc. Biologically inspired [8] Kennedy, J.; Eberhart, R. (1995). "Particle Swarm computing still has much room to grow since this research Optimization". Proceedings of IEEE International community is quite young. There still remain significantly Conference on Neural Networks. IV. pp. 1942–1948. challenging tasks for the research community to address for [9] Dorigo, M., Maniezzo, V., & Colorni, A. (1996). Ant System: Optimization by a colony of cooperating agents. the realization of many existing and most of the emerging IEEE Transactions on Systems, Man, and Cybernetics – areas in technology. In particular, there are great Part B, 26, 29–41. opportunities in exploring a new approach/algorithm. For this [10] D. Karaboga, B. Basturk, A powerful and efficient it requires collaboration of researchers from different algorithm for numerical function optimization: artificial communities like computer science, artificial intelligence, bee colony (ABC) algorithm, Journal of Global biology, ecology, social science etc. in order to have a broader Optimization 39 (2007) 459–471 and deeper view and analysis of each micro level [11] X. Li, Z. Shao, J. Qian,Anoptimizing method base on steps/interactions there by having much more significant and autonomous animates: fish- swarm algorithm, Systems outstanding results. Nevertheless, nature-inspired algorithms Engineering Theory and Practice 22 (2002) 32–38. are among the most powerful algorithms for optimization [12] Shah_Hosseini, H. Shahid Beheshti Univ., which is going to have a wide impact on future generation Tehran Problem solving by intelligent water drops IEEE computing. Congress on Evolutionary Computation, 2007. CEC 2007. ACKNOWLEDGEMENT [13] K. M. Passino, ―Biomimicry of bacterial foraging for distributed optimization and control,‖ IEEE Control Syst. This work is a part of the AICTE funded project titled ‗Bio Mag., vol. 22, no. 3, pp.52–67, Jun. 2002 inspired intrusion response system through feature relevance [14] D. Dasgupta, Artificial Immune Systems and Their analysis on attack classification‘, Under the Research Applications, Springer, Berlin, 1999 Promotion Scheme (RPS), Ref.No: [15] X.S Yang, ―Fire for multimodal 8023/BOR/RID/RPS-59/2009-10. optimization‖,in proceedings of the stochastic Algorithms. Foundations and Applications(SAGA 109) vol.5792of Lecture notes in Computer Sciences

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International Journal of Soft Computing and Engineering (IJSCE) ISSN: 2231-2307, Volume-2, Issue-2, May 2012 [20] Simon, D., 2008. Biogeography-based optimization. IEEE Transactions on Evolu- tionary Computation. 12 (6), 702–713.

Binitha S, Received the BTech degree in Computer Science and Engineering from College of Engineering Cherthala under CUSAT, Kerala. Currently pursuing M-Tech. in Computer Science from Pondicherry University, Pondicherry, India.

Dr. S.Siva Sathya is an Associate professor in the Dept. of Computer Science, Pondicherry University. Her areas of interests include Evolutionary Algorithms, Bioinformatics, Intrusion detection, etc. She has to her credit a number of research papers in International journals and conferences.

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