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Multi-Objective Swarm Intelligent Systems: Theory & Experiences (Studies in Computational Intelligence)

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Recently, a new class of heuristic techniques, the swarm intelligence has emerged. In this context, more recently, biologists and computer scientists in the field of “artificial 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 flocking and fish schooling are self-organizing, meaning that complex group behavior emerges from the interactions of individuals 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 flocks and fish 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 differentiates in emphasizing the cooperative behavior among group members. Swarm intelligence is used to solve optimization and cooperative problems among intelligent agents, mainly in artificial network training, cooperative and/or decentralized control, operational research, power systems, electro-magnetics device design, mobile robotics, and others. The most wellknown representatives of swarm intelligence in optimization problems are: 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 conflicting in most of the cases. Such an optimization is generally called multi-objective or multi-criterion optimization. In this context, the development of improvements for swarm intelligence methods to multi-objective problems is an emergent research area.
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