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Proposal and validation of an optimization method using Monte Carlo method for multi-objective functions

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  • Inage, Sin-ichi
  • Ohgi, Shouki
  • Takahashi, Yoshinori

Abstract

Solving optimization problems is essential for many engineering applications and research tools. In a previous report, we proposed a new optimization method, MOST (Monte Carlo Stochastic Optimization), using the Monte Carlo method, and applied it to benchmark problems for optimization methods, and optimization of neural network machine learning. While the proposed method MOST was a single objective, this study is an extension of MOST so that it can be applied to multi-objective functions for the purpose of improving generality. As the verification, it was applied to the optimization problem with the restriction condition first, and it was also applied to the benchmark problem for the multi-objective optimization technique verification, and the validity was confirmed. For comparison, the calculation by genetic algorithm was also carried out, and it was confirmed that MOST was excellent in calculation accuracy and calculation time.

Suggested Citation

  • Inage, Sin-ichi & Ohgi, Shouki & Takahashi, Yoshinori, 2024. "Proposal and validation of an optimization method using Monte Carlo method for multi-objective functions," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 215(C), pages 146-157.
    • Handle: RePEc:eee:matcom:v:215:y:2024:i:c:p:146-157
    DOI: 10.1016/j.matcom.2023.08.001
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    References listed on IDEAS

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    1. Behera, Sasmita & Sahoo, Subhrajit & Pati, B.B., 2015. "A review on optimization algorithms and application to wind energy integration to grid," Renewable and Sustainable Energy Reviews, Elsevier, vol. 48(C), pages 214-227.
    2. Inage, Sin-ichi & Hebishima, Hana, 2022. "Application of Monte Carlo stochastic optimization (MOST) to deep learning," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 199(C), pages 257-271.
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