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.
Author(s): Maurice Clerc (auth.), Bijaya Ketan Panigrahi, Yuhui Shi, Meng-Hiot Lim (eds.)
Series: Adaptation, Learning, and Optimization 8
Edition: 1
Publisher: Springer-Verlag Berlin Heidelberg
Year: 2010
Language: English
Pages: 544
Tags: Computational Intelligence; Artificial Intelligence (incl. Robotics)
Front Matter....Pages -
Front Matter....Pages 1-1
From Theory to Practice in Particle Swarm Optimization....Pages 3-36
What Makes Particle Swarm Optimization a Very Interesting and Powerful Algorithm?....Pages 37-65
Developing Niching Algorithms in Particle Swarm Optimization....Pages 67-88
Test Function Generators for Assessing the Performance of PSO Algorithms in Multimodal Optimization....Pages 89-117
Linkage Sensitive Particle Swarm Optimization....Pages 119-132
Parallel Particle Swarm Optimization Algorithm Based on Graphic Processing Units....Pages 133-154
Velocity Adaptation in Particle Swarm Optimization....Pages 155-173
Integral-Controlled Particle Swarm Optimization....Pages 175-199
Particle Swarm Optimization for Markerless Full Body Motion Capture....Pages 201-220
An Adaptive Multi-Objective Particle Swarm Optimization Algorithm with Constraint Handling....Pages 221-239
Multiobjective Particle Swarm Optimization for Optimal Power Flow Problem....Pages 241-268
A Multi-objective Resource Assignment Problem in Product Driven Supply Chain Using Quantum Inspired Particle Swarm Algorithm....Pages 269-292
Front Matter....Pages 293-293
Honeybee Optimisation – An Overview and a New Bee Inspired Optimisation Scheme....Pages 295-327
Parallel Approaches for the Artificial Bee Colony Algorithm....Pages 329-345
Bumble Bees Mating Optimization Algorithm for the Vehicle Routing Problem....Pages 347-369
Front Matter....Pages 371-371
Ant Colony Optimization: Principle, Convergence and Application....Pages 373-388
Optimization of Fuzzy Logic Controllers for Robotic Autonomous Systems with PSO and ACO....Pages 389-417
Front Matter....Pages 419-419
A New Framework for Optimization Based-On Hybrid Swarm Intelligence....Pages 421-449
Glowworm Swarm Optimization for Multimodal Search Spaces....Pages 451-467
Direct and Inverse Modeling of Plants Using Cat Swarm Optimization....Pages 469-485
Front Matter....Pages 419-419
Parallel Bacterial Foraging Optimization....Pages 487-502
Reliability-Redundancy Optimization Using a Chaotic Differential Harmony Search Algorithm....Pages 503-516
Gene Regulatory Network Identification from Gene Expression Time Series Data Using Swarm Intelligence....Pages 517-542
Back Matter....Pages -
