Individual Parameter Selection Strategy for Particle Swarm Optimization

With the industrial and scientific developments, many new optimization problems are needed to be solved. Several of them are complex multi-modal, high dimensional, nondifferential problems. Therefore, some new optimization techniques have been designed, such as genetic algorithm (Holland, 1992), ant colony optimization (Dorigo & Gambardella, 1997), etc. However, due to the large linkage and correlation among different variables, these algorithms are easily trapped to a local optimum and failed to obtain the reasonable solution. Particle swarm optimization (PSO) (Eberhart & Kennedy, 1995; Kennedy & Eberhart, 1995) is a population-based, self-adaptive search optimization method motivated by the observation of simplified animal social behaviors such as fish schooling, bird flocking, etc. It is becoming very popular due to its simplicity of implementation and ability to quickly converge to a reasonably good solution (Shen et al., 2005; Eberhart & Shi, 1998; Li et al., 2005). In a PSO system, multiple candidate solutions coexist and collaborate simultaneously. Each solution called a "particle", flies in the problem search space looking for the optimal position to land. A particle, as time passes through its quest, adjusts its position according to its own "experience" as well as the experience of neighboring particles. Tracking and memorizing the best position encountered build particle's experience. For that reason, PSO possesses a memory (i.e. every particle remembers the best position it reached during the past) . PSO system combines local search method (through self experience) with global search methods (through neighboring experience), attempting to balance exploration and exploitation. A particle status on the search space is characterized by two factors: its position and velocity, which are updated by following equations: