Modified Opposition Based Learning to Improve Harmony Search Variants Exploration

  • Alaa A. AlomoushEmail author
  • AbdulRahman A. AlsewariEmail author
  • Hammoudeh S. Alamri
  • Kamal Z. Zamli
  • Waleed Alomoush
  • Mohammed I. Younis
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1073)


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.


Harmony search Opposition based learning Meta-heuristics Evolutionary algorithms Optimization 



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.


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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Alaa A. Alomoush
    • 2
    Email author
  • AbdulRahman A. Alsewari
    • 1
    • 2
    Email author
  • Hammoudeh S. Alamri
    • 3
  • Kamal Z. Zamli
    • 2
  • Waleed Alomoush
    • 3
  • Mohammed I. Younis
    • 4
  1. 1.IBM Centre of Excellence, Universiti Malaysia PahangGambangMalaysia
  2. 2.Faculty of Computing, College of Computing and Applied SciencesUniversiti Malaysia PahangKuantanMalaysia
  3. 3.Computer Science DepartmentImam Abdulrahman Bin Faisal UniversityDammamSaudi Arabia
  4. 4.Department of Computer Engineering, College of EngineeringUniversity of BaghdadBaghdadIraq

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