Hybrid Harmony Search Algorithm with Grey Wolf Optimizer and Modified Opposition-based Learning

Alomoush, Alaa A. and Alsewari, Abdulrahman A. and Alamri, Hammoudeh S. and Aloufi, Khalid and Kamal Z., Zamli (2019) Hybrid Harmony Search Algorithm with Grey Wolf Optimizer and Modified Opposition-based Learning. IEEE Access, 7. 68764- 68785. ISSN 2169-3536. (Published)

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Most metaheuristic algorithms, including harmony search (HS), suffer from parameter selection. Many variants have been developed to cope with this problem and improve algorithm performance. In this paper, a hybrid algorithm of HS with grey wolf optimizer (GWO) has been developed to solve the problem of HS parameter selection. Then, a modified version of opposition-based learning technique has been applied on the hybrid algorithm to improve the HS exploration because HS easily gets trapped into local optima. Two HS parameters were automatically updated using GWO, namely, pitch adjustment rate and bandwidth. The proposed hybrid algorithm for global optimization problems is called GWO-HS. GWO-HS was evaluated using 24 classical benchmark functions with 30 state-of-the-art benchmark functions from CEC2014. Then, GWO-HS has been compared with recent HS variants and other well-known metaheuristic algorithms. Results show that the GWO-HS is superior over the old HS variants and other well-known metaheuristics in terms of accuracy and speed process.

Item Type: Article
Uncontrolled Keywords: Computational intelligence, grey wolf optimizer, harmony search, hybrid algorithm, metaheuristic, optimization algorithm, CEC2014.
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Q Science > QA Mathematics > QA76 Computer software
Faculty/Division: Faculty of Computer System And Software Engineering
Depositing User: Dr. AbdulRahman Ahmed Mohammed Al-Sewari
Date Deposited: 03 Jul 2019 06:19
Last Modified: 03 Jul 2019 06:19
URI: http://umpir.ump.edu.my/id/eprint/25051
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