Constrained Multiple-Swarm Particle Swarm Optimization Within a Cultural Framework

Particle swarm optimization (PSO) has been recently adopted to solve constrained optimization problems. In this paper, a cultural-based constrained PSO is proposed to incorporate the information of the objective function and constraint violation into four sections of the belief space, specifically normative knowledge, spatial knowledge, situational knowledge, and temporal knowledge. The archived information facilitates communication among swarms in the population space and assists in selecting the leading particles in three different levels: personal, swarm, and global levels. Comprehensive comparison of the proposed heuristics over a number of benchmark problems with selected state-of-the-art constraint-handling techniques demonstrates that the proposed cultural framework helps the multiple-swarm PSO to perform competitively with respect to selected designs.