Enhancing three variants of harmony search algorithm for continuous optimization problems

Alomoush, Alaa A. and Alsewari, Abdulrahman A. and Kamal Z., Zamli and Alrosan, Ayat and Alomoush, Waleed and Alissa, Khalid (2021) Enhancing three variants of harmony search algorithm for continuous optimization problems. International Journal of Electrical and Computer Engineering (IJECE), 11 (3). pp. 2343-2349. ISSN 2088-8708. (Published)

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Meta-heuristic algorithms are well-known optimization methods, for solving real-world optimization problems. Harmony search (HS) is a recognized meta-heuristic algorithm with an efficient exploration process. But the HS has a slow convergence rate, which causes the algorithm to have a weak exploitation process in finding the global optima. Different variants of HS introduced in the literature to enhance the algorithm and fix its problems, but in most cases, the algorithm still has a slow convergence rate. Meanwhile, opposition-based learning (OBL), is an effective technique used to improve the performance of different optimization algorithms, including HS. In this work, we adopted a new improved version of OBL, to improve three variants of Harmony Search, by increasing the convergence rate speed of these variants and improving overall performance. The new OBL version named improved opposition-based learning (IOBL), and it is different from the original OBL by adopting randomness to increase the solution's diversity. To evaluate the hybrid algorithms, we run it on benchmark functions to compare the obtained results with its original versions. The obtained results show that the new hybrid algorithms more efficient compared to the original versions of HS. A convergence rate graph is also used to show the overall performance of the new algorithms.

Item Type: Article
Additional Information: Indexed by Scopus
Uncontrolled Keywords: Evolutionary algorithms; Harmony search algorithm; Hybrid algorithms; Meta-heuristics; Optimization algorithms
Subjects: Q Science > QA Mathematics > QA76 Computer software
Faculty/Division: Institute of Postgraduate Studies
Faculty of Computing
Depositing User: Mrs Norsaini Abdul Samat
Date Deposited: 30 Jun 2021 05:07
Last Modified: 30 Jun 2021 05:07
URI: http://umpir.ump.edu.my/id/eprint/31374
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