Improved Harmony Search Algorithm: LHS |
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| Affiliation: | 1. School of Mechanical and Electric Engineering, Guangzhou University, Guangzhou 510006, China;2. College of Information & Science, Northeastern University, Shenyang 110819, China;3. Graduate School of Business and Law, RMIT University, Melbourne 3000, Australia;4. School of Electrical Engineering and Automation, Xuzhou Normal University, Xuzhou 221116, China;1. Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran;2. Dept. of Architectural Engineering, Pennsylvania State Univ., 104 Engineering Unit A, University Park, PA 16802.2, USA;1. State Key Lab of Digital Manufacturing Equipment & Technology, School of Mechanical Science and Engineering, Huazhong University of Science and Technology, 1037 Luoyu Road, Wuhan, China;2. Department of Electronics and Information Engineering, Huazhong University of Science and Technology, 1037 Luoyu Road, Wuhan, China;1. Department of Information Technology, Al-Huson University College, Al-Balqa Applied University, P.O. Box 50, Al-Huson, Irbid, Jordan;2. School of Computer Sciences, Universiti Sains Malaysia, 11800 Pinang, Malaysia;3. Faculty of Computer Science, Al-Aqsa University, P.O. Box 4051, Gaza, Palestine;4. Department of Quality Assurance and Performance, College of Imam Azam University, P.O. Box 72002, Baghdad, Iraq |
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| Abstract: | In this paper, we propose an improved harmony search algorithm named LHS with three key features: (i) adaptive global pitch adjustment is designed to enhance the exploitation ability of solution space; (ii) opposition-based learning technique is blended to increase the diversity of solution; (iii) competition selection mechanism is established to improve solution precision and enhance the ability of escaping local optima. The performance of the LHS algorithm with respect to harmony memory size (HMS) and harmony memory considering rate (HMCR) are also analyzed in detail. To further evaluate the performance of the proposed LHS algorithm, comparison with ten state-of-the-art harmony search variants over a large number of benchmark functions with different characteristics is carried out. The numerical results confirm the superiority of the proposed LHS algorithm in terms of accuracy, convergence speed and robustness. |
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| Keywords: | Adaptive global pitch adjustment Opposition-based learning Competition selection Accuracy |
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