Multi-Objective Swarm Intelligent Systems: Theory & Experiences

ISBN-13: 9783642051647 / Angielski / Twarda / 2009 / 240 str.

Nedjah

Opis

Recently, a new class of heuristic techniques, the swarm intelligence has emerged. In this context, more recently, biologists and computer scientists in the ?eld of"arti?cial life"have been turning to insects for ideas that can be used for heuristics. Many aspects of the collective activities of social insects, such as foraging of ants, birds ?ocking and ?sh schooling are self-organizing, meaning that complex group behavior emerges from the interactions of in- viduals who exhibit simple behaviors by themselves. Swarm intelligence is an innovative computational way to solving hard problems. This discipline is mostly inspired by the behavior of ant colonies, bird ?ocks and ?sh schools and other biological creatures. In general, this is done by mimicking the behavior of these swarms. Swarm intelligence is an emerging research area with similar population and evolution characteristics to those of genetic algorithms. However, it di?erentiates in emphasizing the cooperative behavior among group m- bers. Swarm intelligence is used to solve optimization and cooperative pr- lems among intelligent agents, mainly in arti?cial network training, co- erative and/or decentralized control, operational research, power systems, electro-magnetics device design, mobile robotics, and others. The most we- knownrepresentativesofswarmintelligenceinoptimizationproblemsare: the food-searching behavior of ants, particle swarm optimization, and bacterial colonies. Real-world engineering problems often require concurrent optimization of several design objectives, which are con?icting in most of the cases. Such an optimization is generally called multi-objective or multi-criterion optimi- tion.Inthis context, the developmentofimprovementsfor swarmintelligence methods to multi-objective problems is an emergent research area.

Spis treści

Multiobjective Gaussian Particle Swarm Approach Applied to Multi-loop PI Controller Tuning of a Quadruple-Tank System.- A Non-ordered Rule Induction Algorithm through Multi-Objective Particle Swarm Optimization: Issues and Applications.- Use of Multiobjective Evolutionary Algorithms in Water Resources Engineering.- Micro-MOPSO: A Multi-Objective Particle Swarm Optimizer That Uses a Very Small Population Size.- Dynamic Multi-objective Optimisation Using PSO.- Meta-PSO for Multi-Objective EM Problems.- Multi-Objective Wavelet-Based Pixel-Level Image Fusion Using Multi-Objective Constriction Particle Swarm Optimization.- Multi-objective Damage Identification Using Particle Swarm Optimization Techniques.

Blurb

The editors of this volume, Nadia Nedjah, Leandro dos Santos Coelho and Luiza de Macedo Mourelle, have done a superb job of assembling some of the most innovative and intriguing applications and additions to the methodology and theory of multi-objective swarm intelligence — the immitation of social swarms behaviors for the solution of optimization problems with respect to many criteria.