Global best harmony search algorithm with control parameters co-evolution based on PSO and its application to constrained optimal problems

A global best harmony search algorithm with control parameters co-evolution based on particle swarm optimization (PSO-CE-GHS) is proposed. In PSO-CE-GHS, two control parameters, i.e. harmony memory considering rate and pitch adjusting rate, are encoded to be a symbiotic individual of original individual (i.e. harmony vector). Harmony search operators are applied to evolve the original population. And, PSO is applied to co-evolve the symbiotic population. Thus, with the evolution of the original population in PSO-CE-GHS, the symbiotic population is dynamically and self-adaptively adjusted and the real-time optimum control parameters are obtained. The proposed PSO-CE-GHS algorithm has been applied to various benchmark functions and constrained optimal problems. The results show that the proposed algorithm can find better solutions when compared to HS and its variants.