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A Hybrid Arithmetic Optimization and Golden Sine Algorithm for Solving Industrial Engineering Design Problems

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  • Qingxin Liu

    (School of Computer Science and Technology, Hainan University, Haikou 570228, China)

  • Ni Li

    (School of Mathematics and Statistics, Hainan Normal University, Haikou 571158, China
    Key Laboratory of Data Science and Intelligence Education of Ministry of Education, Hainan Normal University, Haikou 571158, China)

  • Heming Jia

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

  • Qi Qi

    (School of Computer Science and Technology, Hainan University, Haikou 570228, China)

  • Laith Abualigah

    (Faculty of Computer Sciences and Informatics, Amman Arab University, Amman 11953, Jordan
    School of Computer Science, Universiti Sains Malaysia, Gelugor 11800, Malaysia)

  • Yuxiang Liu

    (College of Physics and Information Engineering, Fuzhou University, Fuzhou 350108, China)

Abstract

Arithmetic Optimization Algorithm (AOA) is a physically inspired optimization algorithm that mimics arithmetic operators in mathematical calculation. Although the AOA has an acceptable exploration and exploitation ability, it also has some shortcomings such as low population diversity, premature convergence, and easy stagnation into local optimal solutions. The Golden Sine Algorithm (Gold-SA) has strong local searchability and fewer coefficients. To alleviate the above issues and improve the performance of AOA, in this paper, we present a hybrid AOA with Gold-SA called HAGSA for solving industrial engineering design problems. We divide the whole population into two subgroups and optimize them using AOA and Gold-SA during the searching process. By dividing these two subgroups, we can exchange and share profitable information and utilize their advantages to find a satisfactory global optimal solution. Furthermore, we used the Levy flight and proposed a new strategy called Brownian mutation to enhance the searchability of the hybrid algorithm. To evaluate the efficiency of the proposed work, HAGSA, we selected the CEC 2014 competition test suite as a benchmark function and compared HAGSA against other well-known algorithms. Moreover, five industrial engineering design problems were introduced to verify the ability of algorithms to solve real-world problems. The experimental results demonstrate that the proposed work HAGSA is significantly better than original AOA, Gold-SA, and other compared algorithms in terms of optimization accuracy and convergence speed.

Suggested Citation

  • 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.
  • Handle: RePEc:gam:jmathe:v:10:y:2022:i:9:p:1567-:d:809548
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    References listed on IDEAS

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    1. Ahmed A. Ewees & Laith Abualigah & Dalia Yousri & Ahmed T. Sahlol & Mohammed A. A. Al-qaness & Samah Alshathri & Mohamed Abd Elaziz, 2021. "Modified Artificial Ecosystem-Based Optimization for Multilevel Thresholding Image Segmentation," Mathematics, MDPI, vol. 9(19), pages 1-25, September.
    2. De Giovanni, L. & Pezzella, F., 2010. "An Improved Genetic Algorithm for the Distributed and Flexible Job-shop Scheduling problem," European Journal of Operational Research, Elsevier, vol. 200(2), pages 395-408, January.
    3. Jeffrey O Agushaka & Absalom E Ezugwu, 2021. "Advanced arithmetic optimization algorithm for solving mechanical engineering design problems," PLOS ONE, Public Library of Science, vol. 16(8), pages 1-29, August.
    4. Qingxin Liu & Ni Li & Heming Jia & Qi Qi & Laith Abualigah, 2022. "Modified Remora Optimization Algorithm for Global Optimization and Multilevel Thresholding Image Segmentation," Mathematics, MDPI, vol. 10(7), pages 1-42, March.
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    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. Honghua Rao & Heming Jia & Di Wu & Changsheng Wen & Shanglong Li & Qingxin Liu & Laith Abualigah, 2022. "A Modified Group Teaching Optimization Algorithm for Solving Constrained Engineering Optimization Problems," Mathematics, MDPI, vol. 10(20), pages 1-36, October.
    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.

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