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Accelerated Driving-Training-Based Optimization for Solving Constrained Bi-Objective Stochastic Optimization Problems

Author

Listed:
  • Shih-Cheng Horng

    (Department of Computer Science & Information Engineering, Chaoyang University of Technology, Taichung 413310, Taiwan)

  • Shieh-Shing Lin

    (Department of Electrical Engineering, St. John’s University, New Taipei City 251303, Taiwan)

Abstract

The constrained bi-objective stochastic optimization problem (CBSOP) considers the optimization problem with stochastic bi-objective functions subject to deterministic constraints. The CBSOP is part of a set of hard combinatorial optimization problems regarding time complexity. Ordinal optimization (OO) theory provides a commonly recognized structure to handle hard combinatorial optimization problems. Although OO theory may solve hard combinatorial optimization problems quickly, the deterministic constraints will critically influence computing performance. This work presents a metaheuristic approach that combines driving-training-based optimization (DTBO) with ordinal optimization (OO), abbreviated as DTOO, to solve the CBSOP with a large design space. The DTOO approach comprises three major components: the surrogate model, diversification, and intensification. In the surrogate model, the regularized minimal-energy tensor product with cubic Hermite splines is utilized as a fitness estimation of design. In diversification, an accelerated driving-training-based optimization is presented to determine N remarkable designs from the design space. In intensification, a reinforced optimal computing budget allocation is used to find an extraordinary design from the N remarkable designs. The DTOO approach is applied to a medical resource allocation problem in the emergency department. Simulation results obtained by the DTOO approach are compared with three heuristic approaches to examine the performance of the DTOO approach. Test results show that the DTOO approach obtains an extraordinary design with higher solution quality and computational efficiency than the three heuristic approaches.

Suggested Citation

  • 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.
  • Handle: RePEc:gam:jmathe:v:12:y:2024:i:12:p:1863-:d:1415168
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    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. Babli Kumari & Izhar Ahmad, 2023. "Penalty function method for a variational inequality on Hadamard manifolds," OPSEARCH, Springer;Operational Research Society of India, vol. 60(1), pages 527-538, March.
    4. Mohammed H. Qais & Hany M. Hasanien & Saad Alghuwainem & Ka Hong Loo, 2023. "Propagation Search Algorithm: A Physics-Based Optimizer for Engineering Applications," Mathematics, MDPI, vol. 11(20), pages 1-26, October.
    Full references (including those not matched with items on IDEAS)

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