A self-adaptive multi-objective harmony search algorithm based on harmony memory variance

• This paper aims to improve the performance of harmony search algorithm for solving multi-objective optimization problems.
• Firstly, a novel self-adaptive mechanism has been proposed to improve performance of harmony search algorithm for solving multi-objective problems.
• Secondly, the proposed algorithm is applied to solve many classical benchmark problems and it is also compared with other multi-objective evolutionary algorithms.
• Thirdly, the proposed algorithm is applied to solve a practical engineering problem.
• Fourthly, the impact of harmony memory size on the performance of the proposed algorithm is analyzed.

Although harmony search (HS) algorithm has shown many advantages in solving global optimization problems, its parameters need to be set by users according to experience and problem characteristics. This causes great difficulties for novice users. In order to overcome this difficulty, a self-adaptive multi-objective harmony search (SAMOHS) algorithm based on harmony memory variance is proposed in this paper. In the SAMOHS algorithm, a modified self-adaptive bandwidth is employed, moreover, the self-adaptive parameter setting based on variation of harmony memory variance is proposed for harmony memory considering rate (HMCR) and pitch adjusting rate (PAR). To solve multi-objective optimization problems (MOPs), the proposed SAMOHS uses non-dominated sorting and truncating procedure to update harmony memory (HM). To demonstrate the effectiveness of the SAMOHS, it is tested with many benchmark problems and applied to solve a practical engineering optimization problem. The experimental results show that the SAMOHS is competitive in convergence performance and diversity performance, compared with other multi-objective evolutionary algorithms (MOEAs). In the experiment, the impact of harmony memory size (HMS) on the performance of SAMOHS is also analyzed.