Entropy-based Dynamic Particle Swarm Optimization Algorithm

Inspired by the multi-population parallel optimization mechanism,this paper proposes an Entropy-based Dynamic Multi-population Particle Swarm Optimization(EDM-PSO) algorithm which can be utilized to deal with dynamic optimization problems.The solution space was divided into multiple subspaces,in which the entropy models were utilized in each sub-space to increase the diversity of populations.Additionally,the multi-population parallel searching mechanism and multi-point detection mechanism were also implemented to seek the optimal solution and to detect ambient environmental changes respectively.Finally,a comparison between EDM-PSO and several other dynamical optimization algorithms in terms of the errors(standard deviation) when addressing a moving peaks function benchmark problem was made,resulting in that the EDM-PSO algorithm can be more beneficial to solving dynamic problems.