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Enhanced Remora Optimization Algorithm for Solving Constrained Engineering Optimization Problems

Author

Listed:
  • Shuang Wang

    (School of Information Engineering, Sanming University, Sanming 365004, China)

  • Abdelazim G. Hussien

    (Department of Computer and Information Science, Linköping University, SE-581 83 Linköping, Sweden
    Faculty of Science, Fayoum University, Fayoum 63514, Egypt)

  • Heming Jia

    (School of Information Engineering, Sanming University, Sanming 365004, China)

  • Laith Abualigah

    (Faculty of Computer Sciences and Informatics, Amman Arab University, Amman 11953, Jordan)

  • Rong Zheng

    (School of Information Engineering, Sanming University, Sanming 365004, China)

Abstract

Remora Optimization Algorithm (ROA) is a recent population-based algorithm that mimics the intelligent traveler behavior of Remora. However, the performance of ROA is barely satisfactory; it may be stuck in local optimal regions or has a slow convergence, especially in high dimensional complicated problems. To overcome these limitations, this paper develops an improved version of ROA called Enhanced ROA (EROA) using three different techniques: adaptive dynamic probability, SFO with Levy flight, and restart strategy. The performance of EROA is tested using two different benchmarks and seven real-world engineering problems. The statistical analysis and experimental results show the efficiency of EROA.

Suggested Citation

  • Shuang Wang & Abdelazim G. Hussien & Heming Jia & Laith Abualigah & Rong Zheng, 2022. "Enhanced Remora Optimization Algorithm for Solving Constrained Engineering Optimization Problems," Mathematics, MDPI, vol. 10(10), pages 1-32, May.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:10:p:1696-:d:816349
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    References listed on IDEAS

    as
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    Citations

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    Cited by:

    1. Khizer Mehmood & Naveed Ishtiaq Chaudhary & Zeshan Aslam Khan & Khalid Mehmood Cheema & Muhammad Asif Zahoor Raja & Ahmad H. Milyani & Abdullah Ahmed Azhari, 2022. "Dwarf Mongoose Optimization Metaheuristics for Autoregressive Exogenous Model Identification," Mathematics, MDPI, vol. 10(20), pages 1-21, October.
    2. Jinhua You & Heming Jia & Di Wu & Honghua Rao & Changsheng Wen & Qingxin Liu & Laith Abualigah, 2023. "Modified Artificial Gorilla Troop Optimization Algorithm for Solving Constrained Engineering Optimization Problems," Mathematics, MDPI, vol. 11(5), pages 1-42, March.
    3. Laith Abualigah & Ali Diabat & Raed Abu Zitar, 2022. "Orthogonal Learning Rosenbrock’s Direct Rotation with the Gazelle Optimization Algorithm for Global Optimization," Mathematics, MDPI, vol. 10(23), pages 1-42, November.
    4. Changsheng Wen & Heming Jia & Di Wu & Honghua Rao & Shanglong Li & Qingxin Liu & Laith Abualigah, 2022. "Modified Remora Optimization Algorithm with Multistrategies for Global Optimization Problem," Mathematics, MDPI, vol. 10(19), pages 1-36, October.
    5. Di Wu & Honghua Rao & Changsheng Wen & Heming Jia & Qingxin Liu & Laith Abualigah, 2022. "Modified Sand Cat Swarm Optimization Algorithm for Solving Constrained Engineering Optimization Problems," Mathematics, MDPI, vol. 10(22), pages 1-41, November.

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