Adaptive nested Monte Carlo approach for multi-objective efficient global optimization

• Published in: Journal of global optimization Vol. 91; no. 3; pp. 647 - 676
• Main Authors: Xu, Shengguan, Tan, Jianfeng, Zhang, Jiale, Chen, Hongquan, Gao, Yisheng
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.03.2025; Springer Nature B.V
• Subjects: Adaptive algorithms; Algorithms; Complexity; Computer Science; Global optimization; Hypercubes; Mathematical analysis; Mathematics; Mathematics and Statistics; Monte Carlo simulation; Multiple objective analysis; Operations Research/Decision Theory; Optimization; Real Functions; Expected hypervolume improvement; Monte Carlo method; Multi-objective optimization; Efficient global optimization

This paper presents a novel algorithm, namely the adaptive nested Monte Carlo based multi-objective Efficient Global Optimization (ANMC-MOEGO), which aims to enhance efficiency and accuracy while minimizing programming complexity in contrast to traditional multi-objective Efficient Global Optimizati...