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Swarm Stability And Optimization

Author: Veysel Gazi
Publisher: Springer Science & Business Media
ISBN: 9783642180415
Size: 31.64 MB
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Swarming species such as flocks of birds or schools of fish exhibit fascinating collective behaviors during migration and predator avoidance. Similarly, engineered multi-agent dynamic systems such as groups of autonomous ground, underwater, or air vehicles (“vehicle swarms”) exhibit sophisticated collective behaviors while maneuvering. In this book we show how to model and control a wide range of such multi-agent dynamic systems and analyze their collective behavior using both stability theoretic and simulation-based approaches. In particular, we investigate problems such as group aggregation, social foraging, formation control, swarm tracking, distributed agreement, and engineering optimization inspired by swarm behavior.

Particle Swarm Optimization Stability Analysis

Author: Ouboti Seydou Eyanaa Djaneye-Boundjou
Publisher:
ISBN:
Size: 60.13 MB
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Optimizing a multidimensional function -- uni-modal or multi-modal -- is a problem that regularly comes about in engineering and science. Evolutionary Computation techniques, including Evolutionary Algorithm and Swarm Intelligence (SI), are biological systems inspired search methods often used to solve optimization problems. In this thesis, the SI technique Particle Swarm Optimization (PSO) is studied. Convergence and stability of swarm optimizers have been subject of PSO research. Here, using discrete-time adaptive control tools found in literature, an adaptive particle swarm optimizer is developed. An error system is devised and a controller is designed to adaptively drive the error to zero. The controller features a function approximator, used here as a predictor to estimate future signals. Through Lyapunov's direct method, it is shown that the devised error system is ultimately uniformly bounded and the adaptive optimizer is stable. Moreover, through LaSalle-Yoshizawa theorem, it is also shown that the error system goes to zero as time evolves. Experiments are performed on a variety of benchmark functions and results for comparison purposes between the adaptive optimizer and other algorithms found in literature are provided.

Advanced Intelligent Computing Theories And Applications

Author: De-Shuang Huang
Publisher: Springer Science & Business Media
ISBN: 3540742018
Size: 58.59 MB
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Constitutes the refereed proceedings of the Third International Conference on Intelligent Computing, ICIC 2007, held in Qingdao, China in August 2007. This work addresses various issues in the field of intelligent computing technology, including theories, methodologies, and applications, such as artificial intelligence and soft computing.

Handbook Of Swarm Intelligence

Author: Bijaya Ketan Panigrahi
Publisher: Springer Science & Business Media
ISBN: 9783642173905
Size: 12.69 MB
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From nature, we observe swarming behavior in the form of ant colonies, bird flocking, animal herding, honey bees, swarming of bacteria, and many more. It is only in recent years that researchers have taken notice of such natural swarming systems as culmination of some form of innate collective intelligence, albeit swarm intelligence (SI) - a metaphor that inspires a myriad of computational problem-solving techniques. In computational intelligence, swarm-like algorithms have been successfully applied to solve many real-world problems in engineering and sciences. This handbook volume serves as a useful foundational as well as consolidatory state-of-art collection of articles in the field from various researchers around the globe. It has a rich collection of contributions pertaining to the theoretical and empirical study of single and multi-objective variants of swarm intelligence based algorithms like particle swarm optimization (PSO), ant colony optimization (ACO), bacterial foraging optimization algorithm (BFOA), honey bee social foraging algorithms, and harmony search (HS). With chapters describing various applications of SI techniques in real-world engineering problems, this handbook can be a valuable resource for researchers and practitioners, giving an in-depth flavor of what SI is capable of achieving.

Glowworm Swarm Optimization

Author: Krishnanand N. Kaipa
Publisher: Springer
ISBN: 3319515950
Size: 15.70 MB
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This book provides a comprehensive account of the glowworm swarm optimization (GSO) algorithm, including details of the underlying ideas, theoretical foundations, algorithm development, various applications, and MATLAB programs for the basic GSO algorithm. It also discusses several research problems at different levels of sophistication that can be attempted by interested researchers. The generality of the GSO algorithm is evident in its application to diverse problems ranging from optimization to robotics. Examples include computation of multiple optima, annual crop planning, cooperative exploration, distributed search, multiple source localization, contaminant boundary mapping, wireless sensor networks, clustering, knapsack, numerical integration, solving fixed point equations, solving systems of nonlinear equations, and engineering design optimization. The book is a valuable resource for researchers as well as graduate and undergraduate students in the area of swarm intelligence and computational intelligence and working on these topics.

Exploratory Particle Swarm Optimization

Author: Armin Rashvand
Publisher:
ISBN:
Size: 43.93 MB
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The goal of this research is to propose, implement, and analyze a new particle swarm optimization (PSO) algorithm with enhanced exploration, referred to as exploratory particle swarm optimization (ExPSO). We use the PSO and ExPSO algorithms to optimize tuning parameters for a passivity-based impedance controller on a hip robot simulation model which is used for testing a prosthetic leg. ExPSO has features in common with negative reinforcement particle swarm optimization (NPSO); both algorithms use not only individuals' successes, but also their mistakes, to modify individual velocities in the search space. NPSO uses mistakes to avoid poor solutions, but ExPSO uses mistakes to increase exploration. The 2005 Congress on Evolutionary Computation (CEC 2005) benchmark problems are used to evaluate the performance and parameter tuning of PSO and ExPSO. We find that ExPSO can arrive at optimum solutions better and faster than PSO and NPSO, especially for high-dimensional and complex problems. ExPSO can find solutions that are up to 55% better in terms of cost function values. For the problems that we tested, the standard form for ExPSO which is based on standard PSO (SPSO), namely ExSPSO, can solve 10 out of 38 benchmarks better than SPSO. SPSO can solve 7 out of 38 benchmarks better than ExSPSO, and both algorithms can solve 21 out of 38 benchmarks equally well. Additionally, analytical convergence conditions for ExPSO are derived.

Ant Colony Optimization And Swarm Intelligence

Author: Marco Dorigo
Publisher: Springer Science & Business Media
ISBN: 3540875263
Size: 10.92 MB
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The series of biannual international conferences “ANTS – International C- ference on Ant Colony Optimization and Swarm Intelligence”, now in its sixth edition, was started ten years ago, with the organization of ANTS’98. As some readers might recall, the ?rst edition of ANTS was titled “ANTS’98 – From Ant Colonies to Arti?cial Ants: First International Workshop on Ant Colony Op- mization. ” In fact, at that time the focus was mainly on ant colony optimization (ACO), the ?rst swarm intelligence algorithm to go beyond a pure scienti?c interest and to enter the realm of real-world applications. Interestingly, in the ten years after the ?rst edition there has been a gr- ing interest not only for ACO, but for a number of other studies that belong more generally to the area of swarmintelligence. The rapid growth of the swarm intelligence ?eld is attested by a number of indicators. First, the number of s- missions and participants to the ANTS conferences has steadily increased over the years. Second, a number of international conferences in computational - telligence and related disciplines organize workshops on subjects such as swarm intelligence, ant algorithms, ant colony optimization, and particle swarm op- mization. Third, IEEE startedorganizing,in 2003,the IEEE SwarmIntelligence Symposium (in order to maintain unity in this growing ?eld, we are currently establishingacooperationagreementbetweenIEEE SISandANTSsoastohave 1 IEEE SIS in odd years and ANTS in even years). Last, the Swarm Intelligence journal was born.

Nature Inspired Cooperative Strategies For Optimization Nicso 2013

Author: German Terrazas
Publisher: Springer
ISBN: 331901692X
Size: 40.93 MB
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Biological and other natural processes have always been a source of inspiration for computer science and information technology. Many emerging problem solving techniques integrate advanced evolution and cooperation strategies, encompassing a range of spatio-temporal scales for visionary conceptualization of evolutionary computation. This book is a collection of research works presented in the VI International Workshop on Nature Inspired Cooperative Strategies for Optimization (NICSO) held in Canterbury, UK. Previous editions of NICSO were held in Granada, Spain (2006 & 2010), Acireale, Italy (2007), Tenerife, Spain (2008), and Cluj-Napoca, Romania (2011). NICSO 2013 and this book provides a place where state-of-the-art research, latest ideas and emerging areas of nature inspired cooperative strategies for problem solving are vigorously discussed and exchanged among the scientific community. The breadth and variety of articles in this book report on nature inspired methods and applications such as Swarm Intelligence, Hyper-heuristics, Evolutionary Algorithms, Cellular Automata, Artificial Bee Colony, Dynamic Optimization, Support Vector Machines, Multi-Agent Systems, Ant Clustering, Evolutionary Design Optimisation, Game Theory and other several Cooperation Models.

New Optimization Techniques In Engineering

Author: Godfrey C. Onwubolu
Publisher: Springer
ISBN: 3540399305
Size: 50.71 MB
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Presently, general-purpose optimization techniques such as Simulated Annealing, and Genetic Algorithms, have become standard optimization techniques. Concerted research efforts have been made recently in order to invent novel optimization techniques for solving real life problems, which have the attributes of memory update and population-based search solutions. The book describes a variety of these novel optimization techniques which in most cases outperform the standard optimization techniques in many application areas. New Optimization Techniques in Engineering reports applications and results of the novel optimization techniques considering a multitude of practical problems in the different engineering disciplines – presenting both the background of the subject area and the techniques for solving the problems.

Proceedings Of Seventh International Conference On Bio Inspired Computing Theories And Applications Bic Ta 2012

Author: Jagdish C. Bansal
Publisher: Springer Science & Business Media
ISBN: 8132210387
Size: 46.33 MB
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The book is a collection of high quality peer reviewed research papers presented in Seventh International Conference on Bio-Inspired Computing (BIC-TA 2012) held at ABV-IIITM Gwalior, India. These research papers provide the latest developments in the broad area of "Computational Intelligence". The book discusses wide variety of industrial, engineering and scientific applications of nature/bio-inspired computing and presents invited papers from the inventors/originators of novel computational techniques.