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Swarm Intelligence and Its Applications in Swarm Robotics 6th WSEAS Int. Conference on Computational Intelligence, Man-Machine Systems and Cybernetics, Tenerife, Spain, December 14-16, 2007 41 Swarm Intelligence and Its Applications in Swarm Robotics ALEKSANDAR JEVTIC´ DIEGO ANDINA Universidad Politecnica´ de Madrid Universidad Politecnica´ de Madrid E.T.S.I. Telecomunicacion´ E.T.S.I. Telecomunicacion´ SPAIN SPAIN Abstract: This work gives an overview of the broad fiel of computational swarm intelligence and its applications in swarm robotics. Computational swarm intelligence is modelled on the social behavior of animals and its prin- ciple application is as an optimization technique. Swarm robotics is a relatively new and rapidly developing fiel which draws inspiration from swarm intelligence. It is an interesting alternative to classical approaches to robotics because of some properties of problem solving present in social insects, which is fl xible, robust, decentralized and self-organized. This work highlights the possibilities for further research. Key–Words: Swarm Robotics, Swarm Intelligence, Computational Swarm Intelligence 1 Introduction can be significan in situations where global knowl- edge of environment does not exist. Figure 1 shows Nature has always inspired researchers. By simple ob- the diagram of CI paradigms where the hybrid ap- serving we can sometimes notice the patterns, the set proaches exist as well. CI is generally applied to op- of rules that make seemingly chaotic processes logi- timization problems and many problems that can be cal. How do we think and how do we memorize? Why converted to optimization problems. is evolution so important for the survival of species? How do the social insects know how to follow the path to a source of food without the global knowledge? These questions are partially answered by computa- tional intelligence (CI). Partially, because answering some questions we are usually faced with new ones to answer. CI as a part of broader fiel of artificia intelli- gence (AI) comprises of the paradigms that relate to some kind of biological or naturally occurring sys- tem. These paradigms are artificia neural networks (ANNs), fuzzy systems (FS), evolutionary computing (EC) and swarm intelligence (SI). ANNs are compu- tational models of the human brain. Important char- Figure 1: Computational Intelligence Paradigms. acteristic of ANNs is capability to learn from the en- vironment and to retain information. FS approximate human reasoning using imprecise, or fuzzy, linguis- tic terms. They offer solutions to a disadvantage of 2 Swarm Intelligence ordinary rule-based expert systems that cannot han- dle new situations not already explicitly covered in SI systems are typically made up of a population of their knowledge base. EC is based on Darwin’s evo- simple agents interacting locally with one another and lutionary theory principles. It refers to computer- with their environment. The group of individuals act- based problem-solving systems that use computa- ing in such a manner is referred to as a swarm [1]. The tional methods of evolutionary processes (selection, term stigmergy is used to describe the indirect form reproduction, mutation) as the fundamental compo- of communication between individuals in a swarm nents of such computational systems. SI is modelled via environment (one individual modifie the environ- on the social behavior of insects, fis and birds. The ment, which in return modifie the behavior of other benefit of cooperation among individuals in a swarm individuals - they respond to the change). Individuals 6th WSEAS Int. Conference on Computational Intelligence, Man-Machine Systems and Cybernetics, Tenerife, Spain, December 14-16, 2007 42 within the group interact by exchanging locally avail- Each of the particles in a swarm corresponds to a able information such that the problem (global objec- simple agent that moves through a multi-dimensional tive) is solved more efficientl than it would be done search space sampling an objective function at vari- by a single individual. Problem-solving behavior that ous positions. The best solution can be represented as emerges from such interactions is called swarm in- a point or surface in the search space. Potential so- telligence. Algorithmic models of that behavior are lutions are plotted in this space and seeded with an called computational swarm intelligence (CSI). For initial velocity. The motion of a given particle is dic- the simplicity, the name most frequently used is just tated by its velocity which is continuously updated ”swarm intelligence”. in order to pull it towards its own best position and Many aspects of the collective activities of so- the best positions experienced by the neighbors in the cial behavior in nature are self-organized. Self- swarm. The performance of each particle is evaluated organization (SO) is a set of dynamical mechanisms using a predefine fitnes function which encapsulates whereby structures appear at the global level of a sys- the characteristics of the optimization problem. Over tem from interactions among its lower-level compo- time, particles accelerate towards those with better fit nents [2]. SO relies on four basic ingredients: ness values. PSO is a simple, but powerful search technique. It ² Positive feedback (amplification) examples are has few parameters to adjust and is easy to implement. recruitment and reinforcement. For instance, re- cruitment to a food source is a positive feedback 2.2 Ant Colony Optimization that relies on trail-laying and trail-following in some ant species, or dances in bees. Artificia ant systems model the social interaction and seemingly intelligent behavior of naturally occurring ² Negative feedback: counterbalances positive colonies of ants. Observing the ant colonies we can feedback and helps to stabilize the collective pat- notice that, although the behavior of a single ant tern in the form of saturation, exhaustion or com- doesn’t seem logical, the resulting behavior of the petition. colony solves the problems of great importance for the survival. The ants use the trail-laying trail-following ² Amplificatio of fluctuations randomness is of- behavior to communicate via environment. They lay ten crucial since it enables discovery of new so- pheromone which attracts other ants. The emergent lutions. behavior of the colony is observed in their ability to, ² Multiple interactions: a minimal density of mu- amongst others, locate optimal food resources and tually tolerant individuals is required to generate perform nest brooming, including cemetery mainte- a self-organized structure. nance. The Ant Colony Optimization (ACO) [4] repre- The objective of SI is to model the simple behav- sents the model of the collective foraging behavior ior of the individuals, their local interactions with the of ants. Deneubourg et al. [5] showed that path se- environment and neighboring individuals, in order to lection to a food source is based on self-organization. obtain more complex behaviors that can be used to In the Binary Bridge experiment, two ants are taking solve complex problems, mostly optimization prob- the paths of different length from the nest to a food lems. A critical number of individuals are required source. The ant that will return firs to the nest is the for ”intelligence” to arise. one taking the shorter path. This path will, therefore, The two best known SI algorithms are: Parti- contain the larger pheromone concentration and it will cle Swarm Optimization (PSO) and Ant Colony Opti- attract other ants to take the same route. As more and mization (ACO). more ants start to follow the trail of higher pheromone concentration, a positive feedback loop is created un- til all the ants follow the shortest path. Thus, social 2.1 Particle Swarm Optimization interaction and coordination for foraging occurs indi- Particle Swarm Optimization (PSO) [3] was originally rectly through pheromone deposits which modify the inspired by the flockin behavior of birds. In terms environment. of this bird flockin analogy, a particle swarm opti- The basic idea underlying all ant-based algo- mizer consists of a number of particles, or birds, that rithms is to use a positive feedback mechanism, based fl around and search space, or the sky, for the best on an analogy with the trail-laying trail-following be- location. The individuals communicate either directly havior of some species of ants, to reinforce the good or indirectly with one another search directions (gra- solutions. A virtual pheromone is used as reinforce- dients). ment to allow the good solutions to be memorized 6th WSEAS Int. Conference on Computational Intelligence, Man-Machine Systems and Cybernetics, Tenerife, Spain, December 14-16, 2007 43 and then potentially used to make up better solu- limited by the emergent behavior observed in social tions. Premature convergence (stagnation) of the al- insects. gorithm to good, though not very good solutions, is SI techniques as ACO and PSO can be used as a avoided by implementing a negative feedback through control algorithm for distributed robot swarms [7], but pheromone evaporation and that also includes a time a good problem-solving system does not have to be scale in the algorithm. The time scale must not be too biologically relevant. However, the remarkable suc- large, otherwise suboptimal premature convergence cess of social insects in surviving and colonizing our behavior might occur. It must not be too short either; planet can serve as a starting point for new metaphors otherwise no cooperative behavior can emerge. Co- in engineering and computer science. operative behavior is another important concept: ants in the next iteration use the pheromone trail from the ants in the previous one to guide their exploration. 3.1 Criteria for Swarm Robotics Some optimization algorithms model the behav- What makes a system swarm-robotic? ior of honeybees. The recently developed Bees al- Autonomy – It is required that the individuals that gorithm of Cardiff University implements techniques make up the swarm-robotic system are autonomous that bees employ when foraging for food [6]. robots. They are able to physically interact with the environment and affect it.
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