Modified Opposition Based Learning to Improve Harmony Search Variants Exploration

Al-Omoush, Alaa A. and Alsewari, Abdulrahman A. and Alamri, Hammoudeh S. and Kamal Z., Zamli (2020) Modified Opposition Based Learning to Improve Harmony Search Variants Exploration. In: 4th International Conference of Reliable Information and Communication Technology. IRICT 2019, 22-23 September 2019 , Johor Bahru, Johor, Malaysia. pp. 279-287., 1073. ISBN 978-3-030-33582-3

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Harmony Search Algorithm (HS) is a well-known optimization algorithm with strong and robust exploitation process. HS such as many optimization algorithms suffers from a weak exploration and susceptible to fall in local optima. Owing to its weaknesses, many variants of HS were introduced in the last decade to improve its performance. The Opposition-based learning and its variants have been successfully employed to improve many optimization algorithms, including HS. Opposition-based learning variants enhanced the explorations and help optimization algorithms to avoid local optima falling. Thus, inspired by a new opposition-based learning variant named modified opposition-based learning (MOBL), this research employed the MOBL to improve five well-known variants of HS. The new improved variants are evaluated using nine classical benchmark function and compared with the original variants to evaluate the effectiveness of the proposed technique. The results show that MOBL improved the HS variants in term of exploration and convergence rate.

Item Type: Conference or Workshop Item (Paper)
Additional Information: Indexed by Scopus
Uncontrolled Keywords: Harmony search; Opposition based learning; Meta-heuristics Evolutionary; algorithms Optimization
Subjects: Q Science > QA Mathematics > QA76 Computer software
Faculty/Division: Centre of Excellence: IBM Centre of Excellence
Faculty of Computer System And Software Engineering
Depositing User: Dr. AbdulRahman Ahmed Mohammed Al-Sewari
Date Deposited: 08 Nov 2019 07:35
Last Modified: 24 Feb 2020 02:37
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