A perturbed particle swarm optimization using harmony search and mutation

This paper applies the harmony search (HS) and mutation to lessens the stagnant of PSO. When a particle stops improving, it is mutated or replace by a position value created from harmony memory, which memorized improve locations of particles (Pbest) in search space. Another HS generates the best position among all particles (Gbest) to replace stagnant Gbest to sway the swarm from the trapping location. This HS has another harmony memory that contains improving Gbest. The results of the proposed algorithm are compared with related modified-PSO using 27 benchmark functions. The proposed algorithm locates optimum in more benchmark functions with faster execution.