IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v10y2022i9p1567-d809548.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/10/9/1567/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-7390/10/9/1567/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Dejan G. Ćirić & Zoran H. Perić & Nikola J. Vučić & Miljan P. Miletić, 2023. "Analysis of Industrial Product Sound by Applying Image Similarity Measures," Mathematics, MDPI, vol. 11(3), pages 1-27, January.
    2. Sels, Veronique & Craeymeersch, Kjeld & Vanhoucke, Mario, 2011. "A hybrid single and dual population search procedure for the job shop scheduling problem," European Journal of Operational Research, Elsevier, vol. 215(3), pages 512-523, December.
    3. Heping Fang & Xiaopeng Fu & Zhiyong Zeng & Kunhua Zhong & Shuguang Liu, 2022. "An Improved Arithmetic Optimization Algorithm and Its Application to Determine the Parameters of Support Vector Machine," Mathematics, MDPI, vol. 10(16), pages 1-20, August.
    4. Mohammad Ali Beheshtinia & Parisa Feizollahy & Masood Fathi, 2021. "Supply Chain Optimization Considering Sustainability Aspects," Sustainability, MDPI, vol. 13(21), pages 1-23, October.
    5. Guiliang Gong & Raymond Chiong & Qianwang Deng & Qiang Luo, 2020. "A memetic algorithm for multi-objective distributed production scheduling: minimizing the makespan and total energy consumption," Journal of Intelligent Manufacturing, Springer, vol. 31(6), pages 1443-1466, August.
    6. Alma Y. Alanis, 2022. "Bioinspired Intelligent Algorithms for Optimization, Modeling and Control: Theory and Applications," Mathematics, MDPI, vol. 10(13), pages 1-2, July.
    7. Po-Hsiang Lu & Muh-Cherng Wu & Hao Tan & Yong-Han Peng & Chen-Fu Chen, 2018. "A genetic algorithm embedded with a concise chromosome representation for distributed and flexible job-shop scheduling problems," Journal of Intelligent Manufacturing, Springer, vol. 29(1), pages 19-34, January.
    8. Wei Xiong & Dongmei Fu, 2018. "A new immune multi-agent system for the flexible job shop scheduling problem," Journal of Intelligent Manufacturing, Springer, vol. 29(4), pages 857-873, April.
    9. 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.
    10. 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.
    11. Jaikumar Shanmuganathan & Aruldoss Albert Victoire & Gobu Balraj & Amalraj Victoire, 2022. "Deep Learning LSTM Recurrent Neural Network Model for Prediction of Electric Vehicle Charging Demand," Sustainability, MDPI, vol. 14(16), pages 1-28, August.
    12. Hao-Chin Chang & Tung-Kuan Liu, 2017. "Optimisation of distributed manufacturing flexible job shop scheduling by using hybrid genetic algorithms," Journal of Intelligent Manufacturing, Springer, vol. 28(8), pages 1973-1986, December.
    13. Arshad Ali & Yuvraj Gajpal & Tarek Y. Elmekkawy, 2021. "Distributed permutation flowshop scheduling problem with total completion time objective," OPSEARCH, Springer;Operational Research Society of India, vol. 58(2), pages 425-447, June.
    14. Shoujing Zhang & Tiantian Hou & Qing Qu & Adam Glowacz & Samar M. Alqhtani & Muhammad Irfan & Grzegorz Królczyk & Zhixiong Li, 2022. "An Improved Mayfly Method to Solve Distributed Flexible Job Shop Scheduling Problem under Dual Resource Constraints," Sustainability, MDPI, vol. 14(19), pages 1-19, September.
    15. Arash Amirteimoori & Reza Kia, 2023. "Concurrent scheduling of jobs and AGVs in a flexible job shop system: a parallel hybrid PSO-GA meta-heuristic," Flexible Services and Manufacturing Journal, Springer, vol. 35(3), pages 727-753, September.
    16. Nicolás Álvarez-Gil & Rafael Rosillo & David de la Fuente & Raúl Pino, 2021. "A discrete firefly algorithm for solving the flexible job-shop scheduling problem in a make-to-order manufacturing system," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 29(4), pages 1353-1374, December.
    17. 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.
    18. Du, Yu & Li, Jun-qing, 2024. "A deep reinforcement learning based algorithm for a distributed precast concrete production scheduling," International Journal of Production Economics, Elsevier, vol. 268(C).
    19. 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.
    20. Mahmoud Elsisi & Minh-Quang Tran & Hany M. Hasanien & Rania A. Turky & Fahad Albalawi & Sherif S. M. Ghoneim, 2021. "Robust Model Predictive Control Paradigm for Automatic Voltage Regulators against Uncertainty Based on Optimization Algorithms," Mathematics, MDPI, vol. 9(22), pages 1-19, November.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:10:y:2022:i:9:p:1567-:d:809548. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.