Particle Swarm Optimization (PSO), which is a robust stochastic evolutionary computation engine, belongs to the broad category of swarm intelligence (SI) techniques. SI paradigm has been inspired by the social behavior of ants, bees, wasps, birds, fishes and other biological creatures and is emerging as an innovative and powerful computational metaphor for solving complex problems in design, optimization, control, management, business and finance. SI may be defined as any attempt to design distributed problem-solving algorithms that emerges from the social interaction. The objective of this chapter is to present the use of PSO algorithm for building optimal fuzzy models from the available data. The fuzzy model identification procedure using PSO as an optimization engine has been implemented as a Matlab toolbox and is also presented in this chapter. For the purpose of illustration and validation of the approach, the data from the rapid Nickel-Cadmium (Ni-Cd) battery charger developed by the authors has been used.
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