A comparison of computational efforts between particle swarm optimization and genetic algorithm for identification of fuzzy models

Fuzzy systems are rule-based systems that provide a framework for representing and processing information in a way that resembles human communication and reasoning process. Fuzzy modeling or fuzzy model identification is an arduous task, demanding the identification of many parameters that can be viewed as an optimization process. Evolutionary algorithms are well suited to the problem of fuzzy modeling because they are able to search complex and high dimensional search space while being able to avoid local minima (or maxima). The Particle Swarm Optimization (PSO) algorithm, like other evolutionary algorithms, is a stochastic technique based on the metaphor of social interaction. PSO is similar to the Genetic Algorithm (GA) as these two evolutionary heuristics are population-based search methods. The main objective of this paper is to present the tremendous savings in computational efforts that can be achieved through the use of PSO algorithm in comparison to GA, when used for the identification of fuzzy models from the available input-output data. For realistic comparison, the training data, models complexity and some other common parameters that influence the computational efforts considerably are not changed. The real data from the rapid Nickel-Cadmium (Ni-Cd) battery charger developed has been used for the purpose of illustration and simulation purposes.