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International Conference of Reliable Information and Communication Technology

IRICT 2019: Emerging Trends in Intelligent Computing and Informatics pp 279–287Cite as

Modified Opposition Based Learning to Improve Harmony Search Variants Exploration

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1073))

Abstract

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.

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Acknowledgment

This research is funded by, UMP (RDU190334): A Novel Hybrid Harmony Search Algorithm with Nomadic People Optimizer Algorithm for Global Optimization and Feature Selection, and (FRGS/1/2018/ICT05/UMP/02/1) (RDU190102): A Novel Hybrid Kidney-Inspired Algorithm for Global Optimization Enhance Kidney Algorithm for IoT Combinatorial Testing Problem.

Author information

Authors and Affiliations

  1. IBM Centre of Excellence, Universiti Malaysia Pahang, Gambang, Malaysia

    AbdulRahman A. Alsewari

  2. Faculty of Computing, College of Computing and Applied Sciences, Universiti Malaysia Pahang, Kuantan, 26300, Pahang, Malaysia

    Alaa A. Alomoush, AbdulRahman A. Alsewari & Kamal Z. Zamli

  3. Computer Science Department, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia

    Hammoudeh S. Alamri & Waleed Alomoush

  4. Department of Computer Engineering, College of Engineering, University of Baghdad, Baghdad, Iraq

    Mohammed I. Younis

Authors
  1. Alaa A. Alomoush
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  2. AbdulRahman A. Alsewari
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  3. Hammoudeh S. Alamri
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  4. Kamal Z. Zamli
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  5. Waleed Alomoush
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  6. Mohammed I. Younis
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Corresponding authors

Correspondence to Alaa A. Alomoush or AbdulRahman A. Alsewari .

Editor information

Editors and Affiliations

  1. College of Computer Science and Engineering, Taibah University, Medina, Saudi Arabia

    Faisal Saeed

  2. School of Computing, Universiti Utara Malaysia (UUM), Sintok, Kedah Darul Aman, Malaysia

    Fathey Mohammed

  3. Management of Information Systems Department College of Business Administration, Taibah University, Yanbu, Saudi Arabia

    Nadhmi Gazem

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Alomoush, A.A., Alsewari, A.A., Alamri, H.S., Zamli, K.Z., Alomoush, W., Younis, M.I. (2020). Modified Opposition Based Learning to Improve Harmony Search Variants Exploration. In: Saeed, F., Mohammed, F., Gazem, N. (eds) Emerging Trends in Intelligent Computing and Informatics. IRICT 2019. Advances in Intelligent Systems and Computing, vol 1073. Springer, Cham. https://doi.org/10.1007/978-3-030-33582-3_27

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