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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. |
