Various evolutionary and nonevolutionary methods have been proposed in order to solve complex search and optimization problems. Among these methods, a special place is occupied by evolutionary algorithms (EAs)[ 1, 2] and by the techniques based on swarm intelligence (such as particle swarm optimization (PSO)[3] and ant colony optimization [ 4]). The main advantage of these methods is given by the possibility to use them for searching in various spaces without performing big changes in the structure of the algorithm. They can be easily adapted (by the human being or by themselves) to the peculiarities of the problem which is being solved.

PSO is a population-based stochastic optimization technique proposed by Kennedy and Eberhart [ 5– 7]. The standard PSO algorithm randomly initializes a group of particles (solutions) and then searches for optima by updating all the particles along a number of iterations. In any iteration, each particle is updated by following a few simple rules [ 8, 9]. The standard model implies that particles are updated synchronously [6]. This means that the current position and speed of a particle are computed by taking into account only the information from the previous iteration of particles. The model investigated in this paper is a more general one and