 Proposal and validation of an optimization method using Monte Carlo method for multi-objective functionsSin-ichi Inage, Shouki Ohgi and Yoshinori Takahashi Mathematics and Computers in Simulation (MATCOM), 2024, vol. 215, issue C, 146-157 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. Keywords: Optimization algorithms; Muti-objective function; Monte Carlo method; Genetic algorithm (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:matcom:v:215:y:2024:i:c:p:146-157 DOI: 10.1016/j.matcom.2023.08.001 Access Statistics for this article Mathematics and Computers in Simulation (MATCOM) is currently edited by Robert Beauwens More articles in Mathematics and Computers in Simulation (MATCOM) from Elsevier |
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