Multi-objective hybrid algorithm based on gradient search and evolution mechanism

被引：0
|
作者：
Zhu C. [1 ]
Tang Z. [1 ]
Zhao X. [1 ]
Cao F. [1 ]
机构：
[1] College of Aerospace Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing
来源：
Beijing Hangkong Hangtian Daxue Xuebao/Journal of Beijing University of Aeronautics and Astronautics | 2024年 / 50卷 / 06期
基金：
中国国家自然科学基金;
关键词：
aerodynamic optimization; evolutionary algorithms; gradient method; hybrid algorithm; multi-objective optimization;
D O I：
10.13700/j.bh.1001-5965.2022.0544
中图分类号：
学科分类号：
摘要：
Because of its strong global exploration ability, the current multi-objective evolutionary algorithm (MOEA) has received a lot of attention. However, its local search ability close to the optimal value is relatively weak, and for optimization problems involving large-scale decision variables, MOEA requires a very large number of populations and iterations, which results in a low optimization efficiency. Gradient-based optimization algorithms can overcome these problems well, but they are difficult to be applied to multi-objective problems (MOPs). Therefore, this paper introduced a random weight function on the basis of a weighted average gradient, developed a multi-objective gradient operator, and combined it with a non-dominated sorting genetic algorithm-Ⅲ (NSGA- Ⅲ) based on reference points to develop multi-objective optimization algorithm (MOGBA) and multi-objective Hybrid Evolutionary algorithm (HMOEA). The latter greatly enhances the local search capability while retaining the good global exploration capability of NSGA-Ⅲ. Experiments with numbers demonstrate that HMOEA can effectively capture a wide range of Pareto forms, and that it is 5–10 times more efficient than standard multi-objective algorithms. And further, HMOEA is applied to the multi-objective aerodynamic optimization problem of the RAE2822 airfoil, and the ideal Pareto front is obtained, indicating that HMOEA is an efficient optimization algorithm with potential applications in aerodynamic optimization design. © 2024 Beijing University of Aeronautics and Astronautics (BUAA). All rights reserved.
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页码：1940 / 1951
页数：11
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