The Modified Harmony Search Algorithm With Control Parameters Co-evolution And Its Application

Optimization problem generally exist in all areas of human activity, since last century inspired by natural phenomena or laws, many intelligent optimization algorithms have been proposed to solve complicated optimization problem. Harmony search algorithm is a novel heuristic algorithm proposed in recent years, it has been applied to many engineering fields successfully because of its simple structure, easy to implement and better robustness characteristics; However, the performance of harmony search is affected greatly by its control parameters set, and its parameters set lack mature theoretical support. So, two modified harmony search algorithm with control parameters co-evolution are proposed in this paper, particle swarm optimization algorithm and differential evolution optimization algorithm are applied to guide the control parameters to self-adaptively change in the modified harmony search algorithms respectively, therefore, the real-time best control parameters are gotten. The experimental result of the standard test functions and constrained optimization functions show that the performance of the modified algorithms is improved greatly. The concrete content is as follow:(1) 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, 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, which are adaptive to the current situation, 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 is improved greatly.(2)A Harmony Search Algorithm with Control Parameters Co-evolution Based Differential Evolution optimization (DEHS) is proposed. In DEHS, 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, DE is applied to co-evolve the symbiotic population. Thus, with the evolution of the original population in DEHS, the symbiotic population is dynamically and self-adaptively adjusted and the real-time optimum control parameters are obtained. The proposed DEHS 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. The penalty function used for solving constrained optimization problems in this paper is introduced and the compared result with other common penalty functions show that its performance is better.