A self-adaptive multi-objective harmony search algorithm based on harmony memory variance |
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| Affiliation: | 1. Department of Mathematics, University of Cádiz, Spain;2. Research Unit Computational Logic, Vienna University of Technology, Wien, Austria;1. School of Electrical & Automatic Engineering, Changshu Institute of Technology, 215500 Changshu, China;2. School of Automation, Nanjing University of Science & Technology, 210094 Nanjing, China;1. Center for Advanced Bioinformatics & Systems Medicine, Sookmyung Women’s University, Seoul 140-742, Republic of Korea;2. Discovery Biology Group, Institute Pasteur Korea, Seongnam-si, Gyeonggi-do 463-400, Republic of Korea;3. Imaging Processing Platform, Institute Pasteur Korea, Seongnam-si, Gyeonggi-do 463-400, Republic of Korea;4. Department of Biological Sciences, Sookmyung Women’s University, Seoul 140-742, Republic of Korea;1. Pondicherry University (A Central University of India), India;2. Periyar Govt. College, Cuddalore, India;3. National University of Kaohsiung, Taiwan |
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| Abstract: | 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. |
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| Keywords: | Multi-objective optimization Pareto front (PF) Harmony search (HS) Self-adaptive parameter setting Self-adaptive bandwidth |
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