Dynamic optimization using cultural based PSO

Many practical optimization problems are with the existence of uncertainties, among which a significant number belong to the dynamic optimization problem (DOP) category in which the fitness function changes through time. In this study, we propose the cultural based particle swarm optimization (PSO) to solve DOP problems. A cultural framework is introduced that incorporates the required information from the PSO into five sections of the belief space, namely situational knowledge, temporal knowledge, domain knowledge, normative knowledge and spatial knowledge. The stored information will be adopted to detect the changes in the environment and assists response to the change through a diversity based repulsion among particles and migration among swarms in the population space, also helps in selecting the leading particles in three different levels, personal, swarm and global level. Comparison of the proposed cultural based dynamic PSO demonstrates the better or equal performance with respect to other selected state-of-the-art dynamic PSO heuristics.