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Orthogonal Learning Rosenbrock’s Direct Rotation with the Gazelle Optimization Algorithm for Global Optimization

Author

Listed:
  • Laith Abualigah

    (Faculty of Information Technology, Al-Ahliyya Amman University, Amman 19328, Jordan)

  • Ali Diabat

    (Division of Engineering, New York University Abu Dhabi, Saadiyat Island, Abu Dhabi 129188, United Arab Emirates
    Department of Civil and Urban Engineering, Tandon School of Engineering, New York University, Brooklyn, NY 11201, USA)

  • Raed Abu Zitar

    (Sorbonne Center of Artificial Intelligence, Sorbonne University-Abu Dhabi, Abu Dhabi 38044, United Arab Emirates)

Abstract

An efficient optimization method is needed to address complicated problems and find optimal solutions. The gazelle optimization algorithm (GOA) is a global stochastic optimizer that is straightforward to comprehend and has powerful search capabilities. Nevertheless, the GOA is unsuitable for addressing multimodal, hybrid functions, and data mining problems. Therefore, the current paper proposes the orthogonal learning (OL) method with Rosenbrock’s direct rotation strategy to improve the GOA and sustain the solution variety (IGOA). We performed comprehensive experiments based on various functions, including 23 classical and IEEE CEC2017 problems. Moreover, eight data clustering problems taken from the UCI repository were tested to verify the proposed method’s performance further. The IGOA was compared with several other proposed meta-heuristic algorithms. Moreover, the Wilcoxon signed-rank test further assessed the experimental results to conduct more systematic data analyses. The IGOA surpassed other comparative optimizers in terms of convergence speed and precision. The empirical results show that the proposed IGOA achieved better outcomes than the basic GOA and other state-of-the-art methods and performed better in terms of solution quality.

Suggested Citation

  • 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.
  • Handle: RePEc:gam:jmathe:v:10:y:2022:i:23:p:4509-:d:987696
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    References listed on IDEAS

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    1. Chen, Huiling & Wang, Mingjing & Zhao, Xuehua, 2020. "A multi-strategy enhanced sine cosine algorithm for global optimization and constrained practical engineering problems," Applied Mathematics and Computation, Elsevier, vol. 369(C).
    2. Mohammad H. Nadimi-Shahraki & Shokooh Taghian & Seyedali Mirjalili & Laith Abualigah, 2022. "Binary Aquila Optimizer for Selecting Effective Features from Medical Data: A COVID-19 Case Study," Mathematics, MDPI, vol. 10(11), pages 1-24, June.
    3. Ibrahim Attiya & Laith Abualigah & Samah Alshathri & Doaa Elsadek & Mohamed Abd Elaziz, 2022. "Dynamic Jellyfish Search Algorithm Based on Simulated Annealing and Disruption Operators for Global Optimization with Applications to Cloud Task Scheduling," Mathematics, MDPI, vol. 10(11), pages 1-23, June.
    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. Abdelaziz El Shinawi & Rehab Ali Ibrahim & Laith Abualigah & Martina Zelenakova & Mohamed Abd Elaziz, 2021. "Enhanced Adaptive Neuro-Fuzzy Inference System Using Reptile Search Algorithm for Relating Swelling Potentiality Using Index Geotechnical Properties: A Case Study at El Sherouk City, Egypt," Mathematics, MDPI, vol. 9(24), pages 1-13, December.
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    7. Mohamed Abd Elaziz & Laith Abualigah & Dalia Yousri & Diego Oliva & Mohammed A. A. Al-Qaness & Mohammad H. Nadimi-Shahraki & Ahmed A. Ewees & Songfeng Lu & Rehab Ali Ibrahim, 2021. "Boosting Atomic Orbit Search Using Dynamic-Based Learning for Feature Selection," Mathematics, MDPI, vol. 9(21), pages 1-17, November.
    8. 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.
    9. Ibrahim Attiya & Laith Abualigah & Doaa Elsadek & Samia Allaoua Chelloug & Mohamed Abd Elaziz, 2022. "An Intelligent Chimp Optimizer for Scheduling of IoT Application Tasks in Fog Computing," Mathematics, MDPI, vol. 10(7), pages 1-18, March.
    10. Qingxin Liu & Ni Li & Heming Jia & Qi Qi & Laith Abualigah & Yuxiang Liu, 2022. "A Hybrid Arithmetic Optimization and Golden Sine Algorithm for Solving Industrial Engineering Design Problems," Mathematics, MDPI, vol. 10(9), pages 1-30, May.
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    1. Jing Yang & Jiale Xiong & Yen-Lin Chen & Por Lip Yee & Chin Soon Ku & Manoochehr Babanezhad, 2023. "Improved Golden Jackal Optimization for Optimal Allocation and Scheduling of Wind Turbine and Electric Vehicles Parking Lots in Electrical Distribution Network Using Rosenbrock’s Direct Rotation Strat," Mathematics, MDPI, vol. 11(6), pages 1-23, March.

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