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Hybrid Sine Cosine Algorithm for Solving Engineering Optimization Problems

Author

Listed:
  • Ivona Brajević

    (Faculty of Applied Management, Economics and Finance, University Business Academy in Novi Sad, Jevrejska 24, 11000 Belgrade, Serbia)

  • Predrag S. Stanimirović

    (Faculty of Sciences and Mathematics, University of Niš, Višegradska 33, 18000 Niš, Serbia
    Laboratory “Hybrid Methods of Modelling and Optimization in Complex Systems”, Siberian Federal University, Prosp. Svobodny 79, 660041 Krasnoyarsk, Russia)

  • Shuai Li

    (Department of Electronic and Electrical Engineering, Swansea University, Fabian Way, Swansea SA1 8EN, UK)

  • Xinwei Cao

    (School of Business, Jiangnan University, Lihu Blvd, Wuxi 214122, China)

  • Ameer Tamoor Khan

    (Department of Computing, The Hong Kong Polytechnic University, 11 Yuk Choi Rd, Hung Hom 999077, Hong Kong)

  • Lev A. Kazakovtsev

    (Laboratory “Hybrid Methods of Modelling and Optimization in Complex Systems”, Siberian Federal University, Prosp. Svobodny 79, 660041 Krasnoyarsk, Russia)

Abstract

Engineering design optimization problems are difficult to solve because the objective function is often complex, with a mix of continuous and discrete design variables and various design constraints. Our research presents a novel hybrid algorithm that integrates the benefits of the sine cosine algorithm (SCA) and artificial bee colony (ABC) to address engineering design optimization problems. The SCA is a recently developed metaheuristic algorithm with many advantages, such as good search ability and reasonable execution time, but it may suffer from premature convergence. The enhanced SCA search equation is proposed to avoid this drawback and reach a preferable balance between exploitation and exploration abilities. In the proposed hybrid method, named HSCA, the SCA with improved search strategy and the ABC algorithm with two distinct search equations are run alternately during working on the same population. The ABC with multiple search equations can provide proper diversity in the population so that both algorithms complement each other to create beneficial cooperation from their merger. Certain feasibility rules are incorporated in the HSCA to steer the search towards feasible areas of the search space. The HSCA is applied to fifteen demanding engineering design problems to investigate its performance. The presented experimental results indicate that the developed method performs better than the basic SCA and ABC. The HSCA accomplishes pretty competitive results compared to other recent state-of-the-art methods.

Suggested Citation

  • Ivona Brajević & Predrag S. Stanimirović & Shuai Li & Xinwei Cao & Ameer Tamoor Khan & Lev A. Kazakovtsev, 2022. "Hybrid Sine Cosine Algorithm for Solving Engineering Optimization Problems," Mathematics, MDPI, vol. 10(23), pages 1-21, December.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:23:p:4555-:d:990760
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    References listed on IDEAS

    as
    1. Lina Zhang & Liqiang Liu & Xin-She Yang & Yuntao Dai, 2016. "A Novel Hybrid Firefly Algorithm for Global Optimization," PLOS ONE, Public Library of Science, vol. 11(9), pages 1-17, September.
    2. Ivona Brajević, 2021. "A Shuffle-Based Artificial Bee Colony Algorithm for Solving Integer Programming and Minimax Problems," Mathematics, MDPI, vol. 9(11), pages 1-20, May.
    3. Tae-Hyoung Kim & Minhaeng Cho & Sangwoo Shin, 2020. "Constrained Mixed-Variable Design Optimization Based on Particle Swarm Optimizer with a Diversity Classifier for Cyclically Neighboring Subpopulations," Mathematics, MDPI, vol. 8(11), pages 1-29, November.
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    Cited by:

    1. Oscar Danilo Montoya & Luis Fernando Grisales-Noreña & Jesús C. Hernández, 2023. "A Recursive Conic Approximation for Solving the Optimal Power Flow Problem in Bipolar Direct Current Grids," Energies, MDPI, vol. 16(4), pages 1-19, February.

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