DOCSLIB.ORG

Simulation of a Complex System: Flocking Behaviors

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Simulation of a Complex System: Flocking Behaviors Automated Systems - Computer Engineering Bibliographic Report Simulation of a complex system: Flocking Behaviors Jury : Pr. Laurent Hardouin Author : Dr. Nicolas Delanoue M. Clément Macadré Dr. Remy Guyonneau Pr. Jean-Baptiste Fasquel Pr. David Rousseau Draft August 22, 2020 Acknowledgements I would like to thank all the people responsible for the abundance of material and scientific papers available for free all over the Internet, which made the development of this report enjoyable and very interesting. Thanks as well for the guidance of my tutor, Pr. Laurent Hardouin. i Contents Introduction1 0.1 Context.....................................1 0.2 Outline of the research.............................1 1 Flocking simulation: State of the art3 1.1 Craig W. Reynolds : Flocks, Herds, and Schools, A Distributed Behavioral Model......................................3 1.1.1 Flocking Behaviors...........................4 1.1.2 The environment’s pressure......................5 1.1.3 Application of the flock model.....................5 1.2 James Shannon, BSc : Exploring the real world applications of cellular automata and its application to the simulation of flocking behavior....6 1.2.1 Cellular automaton...........................6 1.2.2 Cellular automaton applications....................6 1.3 James Kennedy and Russell Eberhart : Particle Swarm Optimization...8 1.3.1 Summary of the PSO’s development.................8 1.3.2 Multidimensional search and Neural Networks............9 2 Different approaches to the flocking simulation 11 2.1 Flock simulation using Reynolds "Boids theory"............... 11 2.1.1 A few details on the laws that govern the flock............ 11 2.2 Cellular Automata............................... 15 2.2.1 Game of life............................... 15 2.2.2 Flocking simulation........................... 16 iii iv Contents 2.3 Particle System Optimization......................... 18 2.3.1 PSO’s Algorithm............................ 19 2.3.2 Use in training a neural network.................... 20 3 Outcome of the study 25 3.1 Results of the simulations and the application................ 25 3.1.1 Flock simulation using Reynolds "Boids theory"........... 25 3.1.2 Cellular Automata........................... 27 3.1.3 Particle System Optimization..................... 28 3.1.4 PSO’s use in training a neural network................ 29 Conclusion 33 3.2 Evaluation.................................... 33 3.2.1 Future work............................... 33 List of Figures 1.1 Complex patterns emerge from the flocking of birds..............4 1.2 Pattern emerging from a Rule 30 cellular automaton with a specific initial state.......................................7 1.3 A Conus textile shell similar in appearance to Rule 30............7 2.1 Alignment rule applied to the Boid...................... 12 2.2 Separation rule applied to the Boid...................... 13 2.3 Cohesion rule applied to the Boid....................... 14 2.4 The board can be seen as a torus of revolution................ 15 2.5 Cell’s 2-layer neighborhood........................... 17 2.6 CA grid..................................... 17 2.7 CA Steering................................... 18 2.8 PSO’s search strategy.............................. 20 2.9 I/O of a single neuron............................. 21 2.10 XOR neural network layout.......................... 22 2.11 XOR Output Computation........................... 23 3.1 Flock Centering and Collision Avoidance implementation.......... 26 3.2 Velocity Matching rule implementation.................... 26 3.3 Flock of Boids.................................. 27 3.4 Conway’s Game of life............................. 27 3.5 Cellular automaton: migration of a flock of cells............... 28 3.6 Particle system’s displacement......................... 29 3.7 Optimizing with a particle system....................... 29 v vi List of Figures 3.8 Neural network trained to solve the XOR logic................ 30 3.9 Neural network: Bias and weights values................... 30 List of acronyms CA Cellular Automaton PSO Particle System Optimization NN Neural Network XOR The exclusive-or logic vii Introduction 0.1 Context This bibliographical report is a popularization and synthesis exercise which aims to provide a first experience of scientific research on a subject of my choice. Thus, based on the knowledge acquired during my first year of engineering studies in terms of simulation of complex systems and a certain curiosity for biomimicry [5], I decided to present and then carry out several approaches to the simulation of a bird flock as well as an application in neural networks[2]. 0.2 Outline of the research The first chapter of this report aims at presenting three scientific papers covering theories about the simulation of flocks of birds, schools of fishes and any system exhibiting emerging behaviors. The first approach concerns the work of Craig Reynolds [16] in 1986 with the development of rules between individuals called "Boid" in order to create an autonomous simulation. The second paper addresses the notion of cellular automata with the work of James Shannon [18] in 2013 and its application in the simulation of flocking behaviors. The third paper will focus on particle system optimizations developed by James Kennedy and Russell Eberhart [9] in 1995, which was originally inspired by the behavior of a flock of birds. In the second chapter we will present three simulations based on the theories previously detailed. Starting with a 3D simulation of a flock of birds based on Reynolds’ Boids theory, then we will create a cellular automaton based on Shannon’s theory with a little digression on Conway’s game of life [6]. Finally we will present the foraging simulation of a flock using a particle system that exhibits an optimizing behavior which we will use to train a neural network. In the last chapter we will comment on the final rendering of the simulations, as well as on the performance of the application mentioned previously. 1 Chapter 1 Flocking simulation: State of the art It is easy to imagine by observing the movements of a flock of birds or a school of fish that we are dealing with a single giant entity with a will of its own. Indeed, one is quickly absorbed by the lightning synchronicity with which hundreds of members can gather, change direction or escape from a predator. And yet it is indeed the individual actions of the elements that make up the flock that allow such behaviors to emerge [17]. A flock of bird is therefore considered to be a complex system [13]. Under this name are grouped systems composed of a multitude of members whose individual interactions bring out global properties. One property of such a system is its ability to self-organize through a collection of simple mechanisms. And at the origin of these mechanisms are the interactions of each element with its environment. 1.1 Craig W. Reynolds : Flocks, Herds, and Schools, A Distributed Behavioral Model Synthesis of Craig W.Reynolds’s Publication [16] Reynolds explains in his publica- tion that it is possible to reproduce the behavior of a pack, herd, swarm or flock by applying a minimum number of rules to the individuals that make up this system. Each individual is called a "Boid", and will behave according to its own perception of the system. In fact, one could have the illusion that a flock of birds moves as a single unit, with coordination, whereas in reality it is the reaction of each individual towards his neighbors that causes the behavior we observe to emerge. (fig. 1.1). Reynolds notes that a flocking simulation is similar to a particle system, which is a graphical technique simulating many natural phenomena such as fire, explosion, smoke, etc. The similarity lies in the fact that the system is composed of a large number of particles, with their own behavior, interacting with neighboring particles. Except that in 3 4 Chapter 1. Flocking simulation: State of the art Figure 1.1: Complex patterns emerge from the flocking of birds. our simulation, the particles are replaced by individuals with a geometric model. Therefore, these individuals must manage their orientations to cope with the direction in which they are moving. It is important to consider that the formation of a flock is an asset for the survival of its members [3]. Indeed, there is safety in numbers and it offers multiple advantages in order to escape predators, save energy by taking advantage of group dynamics, find food more quickly and expose themselves to social and reproductive interactions. We can therefore deduce that the flock aspires to be as massive as possible. We also know that the integrity of a flock depends on the interactions of its members, and their ability to respect certain behaviors. We can therefore observe a willingness of individuals to group together while avoiding collisions. 1.1.1 Flocking Behaviors Reynolds therefore sought to identify and simplify as much as possible the rules that, applied to each individual, allow a flock to emerge spontaneously. He identified that these rules are based on two parameters: the distance between two individuals and their velocities. The rules are therefore as follows: "Collision Avoidance", "Velocity Matching" 1.1. Craig W. Reynolds : Flocks, Herds, and Schools, A Distributed Behavioral Model 5 and "Flock Centering" as well as environmental pressures. This collection of rules prevents the members of the flock from touching each other, forces individuals to adapt their speeds to those of their neighbors and encourages the grouping of individuals in a flock. In the next chapter we will provide details on these rules in order to implement them in a simulation. Each of the three rules will thus produce a suggestion of direction and speed to adopt to take part in the flock. And depending on the situation, these suggestions will be more or less important, for example, if a collision is imminent, it will be necessary to favour an avoidance manoeuvre rather than adapting the speed of movement to those of the surroundings. These subtleties will therefore have to be taken into account when designing a simulation. 1.1.2 The environment’s pressure In order to create a convincing simulation, the swarm must interact with its environment.
Load more

Recommended publications
  • Swarms and Swarm Intelligence
    SOFTWARE TECHNOLOGIES homogeneous. Intelligent swarms can also comprise heterogeneous elements Swarms from the outset, reflecting different capabilities as well as a possible social structure. and Swarm Researchers have used agent swarms as a computer modeling tech- nique and as a tool to study complex systems. Simulation examples include Intelligence bird swarms and business, economics, and ecological systems. In swarm sim- Michael G. Hinchey, Loyola College in Maryland ulations, each agent tries to maximize Roy Sterritt, University of Ulster its given parameters. Chris Rouff, Lockheed Martin Advanced Technology Laboratories In terms of bird swarms, each bird tries to find another to fly with, and then flies slightly higher to one side to reduce drag, with the birds eventually forming a flock. Other types of swarm simulations exhibit unlikely emergent behaviors, which are sums of simple Intelligent swarm technologies solve individual behaviors that form com- complex problems that traditional plex and often unexpected behaviors approaches cannot. when aggregated. Swarm intelligence Gerardo Beni and Jing Wang intro- duced the term swarm intelligence in a 1989 article (“Swarm Intelligence,” Proc. 7th Ann. Meeting of the Robotics e are all familiar with working together to achieve a goal Society of Japan, RSJ Press, 1989, pp. swarms in nature. The and produce significant results. 425-428). Swarm intelligence tech- word swarm conjures Swarms may operate on or under the niques (note the difference from intel- up images of large Earth’s surface, under water, or on ligent swarms) are population-based W groups of small insects other planets. stochastic methods used in combina- in which each member performs a torial optimization problems in which simple role, but the action produces SWARMS AND INTELLIGENCE the collective behavior of relatively sim- complex behavior as a whole.
    [Show full text]
  • Wolf Family Values
    Wolf family values The exquisitely balanced social life of the wolf has implications far beyond the pack, says Sharon Levy ORDON HABER was tracking a wolf pack Wolf Project. Despite many thousands of he had known for over 40 years when his hours spent in the field, Haber published G plane crashed on a remote stretch of the little peer-reviewed documentation of his Toklat river in Denali national park, Alaska, work. Now, however, in the months following last October. The fatal accident silenced one his sudden death, Smith and other wolf of the most outspoken and controversial biologists have reported findings that support advocates for wolf protection. Haber, an some of Haber’s ideas. independent biologist, had spent a lifetime Once upon a time, folklore shaped our studying the behaviour and ecology of wolves thinking about wolves. It is only in the past and his passion for the animals was obvious. two decades that biologists have started to “I am still in awe of what I see out there,” he build a clearer picture of wolf ecology (see wrote on his website. “Wolves enliven the “Beyond myth and legend”, page 42). Instead northern mountains, forests, and tundra like of seeing rogue man-eaters and savage packs, no other creature, helping to enrich our stay we now understand that wolves have evolved on the planet simply by their presence as other to live in extended family groups that include Few places remain highly advanced societies in our midst.” a breeding pair – typically two strong, where wolves can live His opposition to hunting was equally experienced individuals – along with several as nature intended intense.
    [Show full text]
  • An Exploration in Stigmergy-Based Navigation Algorithms
    From Ants to Service Robots: an Exploration in Stigmergy-Based Navigation Algorithms عمر بهر تيری محبت ميری خدمت گر رہی ميں تری خدمت کےقابل جب هوا توچل بسی )اقبال( To my late parents with love and eternal appreciation, whom I lost during my PhD studies Örebro Studies in Technology 79 ALI ABDUL KHALIQ From Ants to Service Robots: an Exploration in Stigmergy-Based Navigation Algorithms © Ali Abdul Khaliq, 2018 Title: From Ants to Service Robots: an Exploration in Stigmergy-Based Navigation Algorithms Publisher: Örebro University 2018 www.publications.oru.se Print: Örebro University, Repro 05/2018 ISSN 1650-8580 ISBN 978-91-7529-253-3 Abstract Ali Abdul Khaliq (2018): From Ants to Service Robots: an Exploration in Stigmergy-Based Navigation Algorithms. Örebro Studies in Technology 79. Navigation is a core functionality of mobile robots. To navigate autonomously, a mobile robot typically relies on internal maps, self-localization, and path plan- ning. Reliable navigation usually comes at the cost of expensive sensors and often requires significant computational overhead. Many insects in nature perform robust, close-to-optimal goal directed naviga- tion without having the luxury of sophisticated sensors, powerful computational resources, or even an internally stored map. They do so by exploiting a simple but powerful principle called stigmergy: they use their environment as an external memory to store, read and share information. In this thesis, we explore the use of stigmergy as an alternative route to realize autonomous navigation in practical robotic systems. In our approach, we realize a stigmergic medium using RFID (Radio Frequency Identification) technology by embedding a grid of read-write RFID tags in the floor.
    [Show full text]
  • Flocks, Herds, and Schools: a Distributed Behavioral Model 1
    Published in Computer Graphics, 21(4), July 1987, pp. 25-34. (ACM SIGGRAPH '87 Conference Proceedings, Anaheim, California, July 1987.) Flocks, Herds, and Schools: A Distributed Behavioral Model 1 Craig W. Reynolds Symbolics Graphics Division [obsolete addresses removed 2] Abstract The aggregate motion of a flock of birds, a herd of land animals, or a school of fish is a beautiful and familiar part of the natural world. But this type of complex motion is rarely seen in computer animation. This paper explores an approach based on simulation as an alternative to scripting the paths of each bird individually. The simulated flock is an elaboration of a particle system, with the simulated birds being the particles. The aggregate motion of the simulated flock is created by a distributed behavioral model much like that at work in a natural flock; the birds choose their own course. Each simulated bird is implemented as an independent actor that navigates according to its local perception of the dynamic environment, the laws of simulated physics that rule its motion, and a set of behaviors programmed into it by the "animator." The aggregate motion of the simulated flock is the result of the dense interaction of the relatively simple behaviors of the individual simulated birds. Categories and Subject Descriptors: 1.2.10 [Artificial Intelligence]: Vision and Scene Understanding; 1.3.5 [Computer Graphics]: Computational Geometry and Object Modeling; 1.3.7 [Computer Graphics]: Three Dimensional Graphics and Realism-Animation: 1.6.3 [Simulation and Modeling]: Applications. General Terms: Algorithms, design.b Additional Key Words, and Phrases: flock, herd, school, bird, fish, aggregate motion, particle system, actor, flight, behavioral animation, constraints, path planning.
    [Show full text]
  • HV Battery SOC Estimation Algorithm Based on PSO
    HV Battery SOC Estimation Algorithm Based on PSO Qiang Sun1, Haiying Lv1, Shasha Wang2, Shixiang Jing1, Jijun Chen1 and Rui Fang1 1College of Engineering and Technology, Tianjin Agricultural University, Jinjing Road, Tianjin, China 2Beijing Electric Vehicle Co., Ltd., East Ring Road, Beijing, China [email protected], [email protected] Keywords: HV battery, SOC, PSO, individual optimum, global optimum. Abstract: In view of the traditional algorithm to estimate the electric vehicle battery state of charge (SOC), lots of problems were caused by the inaccurate battery model parameter estimation error. Particle swarm optimization (PSO) algorithm is a global optimization algorithm based on swarm intelligence, it has intrinsic parallel search mechanism, and is suitable for the complex optimization field. PSO is simple in concept with few using of parameters, and easily in implementation. It is proved to be an efficient method to solve optimization problems, and has successfully been applied in area of function optimization. This article adopts the PSO optimization method to solve the problem of battery parameters. The paper make a new attempt on SOC estimation method, applying PSO to SOC parameters optimization. It can build optimization model to get a more accurate estimate SOC in the case of less parameters with faster convergence speed. 1 INTRODUCTION suitable for the complex optimization field. The paper make a new attempt on SOC estimation Lithium battery has many advantages such as high method, applying PSO to SOC parameters energy density, high power density, long cycle life optimization, It can build optimization model to get and so on. As the energy storage element of electric a more accurate estimate SOC in the case of less vehicles, it is used widely.
    [Show full text]
  • Mathematical Modelling and Applications of Particle Swarm Optimization
    Master’s Thesis Mathematical Modelling and Simulation Thesis no: 2010:8 Mathematical Modelling and Applications of Particle Swarm Optimization by Satyobroto Talukder Submitted to the School of Engineering at Blekinge Institute of Technology In partial fulfillment of the requirements for the degree of Master of Science February 2011 Contact Information: Author: Satyobroto Talukder E-mail: [email protected] University advisor: Prof. Elisabeth Rakus-Andersson Department of Mathematics and Science, BTH E-mail: [email protected] Phone: +46455385408 Co-supervisor: Efraim Laksman, BTH E-mail: [email protected] Phone: +46455385684 School of Engineering Internet : www.bth.se/com Blekinge Institute of Technology Phone : +46 455 38 50 00 SE – 371 79 Karlskrona Fax : +46 455 38 50 57 Sweden ii ABSTRACT Optimization is a mathematical technique that concerns the finding of maxima or minima of functions in some feasible region. There is no business or industry which is not involved in solving optimization problems. A variety of optimization techniques compete for the best solution. Particle Swarm Optimization (PSO) is a relatively new, modern, and powerful method of optimization that has been empirically shown to perform well on many of these optimization problems. It is widely used to find the global optimum solution in a complex search space. This thesis aims at providing a review and discussion of the most established results on PSO algorithm as well as exposing the most active research topics that can give initiative for future work and help the practitioner improve better result with little effort. This paper introduces a theoretical idea and detailed explanation of the PSO algorithm, the advantages and disadvantages, the effects and judicious selection of the various parameters.
    [Show full text]
  • Differential Wolf-Pack-Size Persistence and the Role of Risk When Hunting Dangerous Prey
    Behaviour 153 (2016) 1473–1487 brill.com/beh Differential wolf-pack-size persistence and the role of risk when hunting dangerous prey Shannon M. Barber-Meyer a,b,∗, L. David Mech a,c, Wesley E. Newton a and Bridget L. Borg d a U.S. Geological Survey, Northern Prairie Wildlife Research Center, 8711 37th Street, SE, Jamestown, ND 58401-7317, USA b Present address: U.S. Geological Survey, 1393 Highway 169, Ely, MN 55731, USA c Present address: The Raptor Center, University of Minnesota, 1920 Fitch Avenue, St. Paul, MN 55108, USA d Denali National Park and Preserve, P.O. Box 9, Denali, AK 99755-0009, USA *Corresponding author’s e-mail address: [email protected] Accepted 18 July 2016; published online 19 August 2016 Abstract Risk to predators hunting dangerous prey is an emerging area of research and could account for possible persistent differences in gray wolf (Canis lupus) pack sizes. We documented significant differences in long-term wolf-pack-size averages and variation in the Superior National Forest (SNF), Denali National Park and Preserve, Yellowstone National Park, and Yukon, Canada (p< 0.01). The SNF differences could be related to the wolves’ risk when hunting primary prey, for those packs (N = 3) hunting moose (Alces americanus) were significantly larger than those (N = 10) hunting white-tailed deer (Odocoileus virginianus)(F1,8 = 16.50, p = 0.004). Our data support the hypothesis that differential pack-size persistence may be perpetuated by differences in primary prey riskiness to wolves, and we highlight two important extensions of this idea: (1) the potential for wolves to provision and defend injured packmates from other wolves and (2) the importance of less-risky, buffer prey to pack-size persistence and year-to-year variation.
    [Show full text]
  • Emergent Teamwork
    Emergent Teamwork Craig Reynolds Cognitive Animation Workshop • June 4-5, 2008 • Yosemite 1 This talk ■ Whirlwind tour of collective behavior in nature ■ Some earlier simulation models ■ Recent work on agent-based “collective construction” 2 Tag cloud artificial life theoretical biology flocks ethology multi-agent crowds biology behavior simulation entomology motion stigmergy collective decision emergence making self organization construction searching sorting self assembly swarm intelligence 3 Tag cloud artificial life theoretical biology flocks ethology multi-agent crowds biology behavior simulation entomology motion stigmergy collective decision emergence making self organization construction searching sorting self assembly swarm intelligence 4 Tag cloud artificial life theoretical biology flocks ethology multi-agent crowds biology behavior simulation entomology motion stigmergy collective decision emergence making self organization construction searching sorting self assembly swarm intelligence 5 Stigmergy ■ Communication through the environment ■ often with pheromones ■ physical marks or structures ■ no abstract communication required ■ Coined in 1959 to describe construction by termites ■ by the French biologist Pierre-Paul Grassé ■ from Greek words stigma (sign) and ergon (action) 6 Bricklaying by MC MasterChef via Flickr Humans building a brick wall: ■ obvious where to place next brick ■ add more people to build faster ■ typically coordinated with language 7 History of a long delayed project ■ My interest in stigmergy goes back to talk
    [Show full text]
  • African Wild Dog Lycaon Pictus Group Hunting
    African Wild Dog Lycaon pictus Group Hunting -Wild dogs do not have big powerful jaws like cats so they cannot bring down large animals alone. Hunting in a pack requires cooperation among pack members, this enables wild dogs to bring down animals five times their size. Wild dogs hunt mainly at dawn and dusk because they use their sense of sight to find prey. They usually approach silently, pursue the fleeing prey until it tires, and then attack and kill the animal. Their mottled coloring also aids in hunting by making the pack appear larger than it is! Speedy Pursuit -African wild dogs have tremendous endurance running at speeds of 37 mph for three miles or more pursuing prey. Their long legs and large lungs help them run long distances without tiring. Their speed and endurance as well as the pack structure make them successful 70-90% of the time! Classification The African wild dog is a member of the “true dog” family, Canidae. They are related to jackals, foxes, coyotes, wolves, dingoes and even domestic dogs. While hyenas can look similar, they are of a different family classification (Hyaenidae) and less related than other dogs. Class: Mammalia Order: Carnivora Family: Canidae Genus: Lycaon Species: pictus Distribution African wild dogs were historically found from the Sahara to South Africa, but are currently more limited in range. Habitat Wild dogs inhabit grassland, savannah, open woodlands and montane regions. Physical Description • Weigh between 40-80 pounds (18-36 kg); males and females are the same size. • Stand about 30 inches (76 cm) at the shoulder.
    [Show full text]
  • Autonomous and Autonomic Swarms
    Autonomous and Autonomic Swarms Michael G. Hinchey, James L. Rash, Walter E Truszkowski Information Systems Division NASA Goddard Space Flight Center Greenbelt, MD 2077 1, USA michael.g.hinchey, [email protected], [email protected] Christopher A. Rouff Roy Sterritt SAIC Faculty of Engineering Advanced Concepts Business Unit University of Ulster McLean, VA 22102 Northern Ireland rouffc @saic.com r. sterritt @ ulster.ac.uk Abstract packs of wolves, etc. These groupings behave like swarms in many ways. With wolves, for example, the elder male and A watershed in systems engineering is represented by female (alpha male and alpha female) are accepted as iead- the advent of swarm-based systems that accomplish mis- ers who communicate with the pack via body language and sions through cooperative action by a (large) group of au- facial expressions. Moreover, the alpha male marks the ter- tonomous individuals each having simple capabilities and ritory of the pack, and excludes wolves that are not mem- no global knowledge of the group’s objective. Such systems, bers of the pack. with individuals capable qf surviving in hostile environ- The idea that swxmns can he IJSPA to solve complex ph- ments, pose unprecedented challenges to system develop- lems has been taken up in several areas of computer sci- ers. Design and testing and verification at much higher lev- ence, which we will briefly introduce in Section 2. The term els will be required, together with the corresponding tools, “swarm” in this paper refers to a large grouping of simple to bring such systems to fruition.
    [Show full text]
  • An Improved Grey Wolf Optimizer Based on Differential Evolution And
    www.nature.com/scientificreports OPEN An Improved Grey Wolf Optimizer Based on Diferential Evolution and Elimination Mechanism Received: 20 August 2015 Jie-Sheng Wang1,2 & Shu-Xia Li1 Accepted: 27 April 2019 The grey wolf optimizer (GWO) is a novel type of swarm intelligence optimization algorithm. An Published: xx xx xxxx improved grey wolf optimizer (IGWO) with evolution and elimination mechanism was proposed so as to achieve the proper compromise between exploration and exploitation, further accelerate the convergence and increase the optimization accuracy of GWO. The biological evolution and the “survival of the fttest” (SOF) principle of biological updating of nature are added to the basic wolf algorithm. The diferential evolution (DE) is adopted as the evolutionary pattern of wolves. The wolf pack is updated according to the SOF principle so as to make the algorithm not fall into the local optimum. That is, after each iteration of the algorithm sort the ftness value that corresponds to each wolf by ascending order, and then eliminate R wolves with worst ftness value, meanwhile randomly generate wolves equal to the number of eliminated wolves. Finally, 12 typical benchmark functions are used to carry out simulation experiments with GWO with diferential evolution (DGWO), GWO algorithm with SOF mechanism (SGWO), IGWO, DE algorithm, particle swarm algorithm (PSO), artifcial bee colony (ABC) algorithm and cuckoo search (CS) algorithm. Experimental results show that IGWO obtains the better convergence velocity and optimization accuracy. Te swarm intelligence algorithms are proposed to mimic the swarm intelligence behavior of biological in nature, which has become a hot of cross-discipline and research feld in recent years.
    [Show full text]
  • From Ephemeralization and Stigmergy to the Global Brain
    Accelerating Socio-Technological Evolution: from ephemeralization and stigmergy to the global brain Francis Heylighen http://pespmc1.vub.ac.be/HEYL.html ECCO, Vrije Universiteit Brussel Abstract: Evolution is presented as a trial-and-error process that produces a progressive accumulation of knowledge. At the level of technology, this leads to ephemeralization, i.e. ever increasing productivity, or decreasing of the friction that normally dissipates resources. As a result, flows of matter, energy and information circulate ever more easily across the planet. This global connectivity increases the interactions between agents, and thus the possibilities for conflict. However, evolutionary progress also reduces social friction, via the creation of institutions. The emergence of such “mediators” is facilitated by stigmergy: the unintended collaboration between agents resulting from their actions on a shared environment. The Internet is a near ideal medium for stigmergic interaction. Quantitative stigmergy allows the web to learn from the activities of its users, thus becoming ever better at helping them to answer their queries. Qualitative stigmergy stimulates agents to collectively develop novel knowledge. Both mechanisms have direct analogues in the functioning of the human brain. This leads us to envision the future, super-intelligent web as a “global brain” for humanity. The feedback between social and technological advances leads to an extreme acceleration of innovation. An extrapolation of the corresponding hyperbolic growth model would forecast a singularity around 2040. This can be interpreted as the evolutionary transition to the Global Brain regime. Evolutionary progress The present paper wishes to directly address the issue of globalization as an evolutionary process. As observed by Modelski (2007) in his introductory paper to this volume, globalization can be characterized by two complementary processes, both taking place at the planetary scale: (1) growing connectivity between people and nations; (2) the emergence of global institutions.
    [Show full text]