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Advanced golden jackal optimization for solving the constrained integer stochastic optimization problems

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  • Horng, Shih-Cheng
  • Lin, Shieh-Shing

Abstract

Constrained integer stochastic optimization problems (CISOP) are optimization problems where the cost function is stochastic and the constraints are deterministic. Solving the CISOP using conventional optimization techniques is time-consuming when the design space is huge. Ordinal optimization offers an efficient technique suitable for solving the CISOP. In this paper, an approach that uses golden jackal optimization assisted by ordinal optimization (GJOO) is developed for resolving the CISOP in a relatively short time. The GJOO is composed of three phases: metamodel, global search, and ranking and selection. At first, the polynomial chaos expansion is used as a metamodel to rapidly assess a solution. Next, advanced golden jackal optimization is adopted to determine N excellent solutions from the entire design space. At last, the modified optimal computing budget allocation is adopted to determine a distinguished solution from the N excellent solutions. The GJOO is utilized to the shipping/receiving docks optimization problem of a container freight station in air cargo. The performances of the GJOO are compared with those of five meta-heuristic methods. Empirical results demonstrate the efficiency and robustness of the GJOO algorithm.

Suggested Citation

  • Horng, Shih-Cheng & Lin, Shieh-Shing, 2024. "Advanced golden jackal optimization for solving the constrained integer stochastic optimization problems," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 217(C), pages 188-201.
    • Handle: RePEc:eee:matcom:v:217:y:2024:i:c:p:188-201
    DOI: 10.1016/j.matcom.2023.10.021
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    References listed on IDEAS

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    1. Shih-Cheng Horng & Shieh-Shing Lin, 2023. "Improved Beluga Whale Optimization for Solving the Simulation Optimization Problems with Stochastic Constraints," Mathematics, MDPI, vol. 11(8), pages 1-17, April.
    2. Dongling Cheng & Leipo Liu, 2022. "Water Allocation Optimization and Environmental Planning with Simulated Annealing Algorithms," Mathematical Problems in Engineering, Hindawi, vol. 2022, pages 1-11, May.
    3. Tommaso Giovannelli & Giampaolo Liuzzi & Stefano Lucidi & Francesco Rinaldi, 2022. "Derivative-free methods for mixed-integer nonsmooth constrained optimization," Computational Optimization and Applications, Springer, vol. 82(2), pages 293-327, June.
    4. Uemoto, Takumi & Naito, Kanta, 2022. "Support vector regression with penalized likelihood," Computational Statistics & Data Analysis, Elsevier, vol. 174(C).
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    Cited by:

    1. Shih-Cheng Horng & Shieh-Shing Lin, 2024. "Accelerated Driving-Training-Based Optimization for Solving Constrained Bi-Objective Stochastic Optimization Problems," Mathematics, MDPI, vol. 12(12), pages 1-19, June.

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