| Citation: | ZHU C C，TANG Z L，ZHAO X，et al. Multi-objective hybrid algorithm based on gradient search and evolution mechanism[J]. Journal of Beijing University of Aeronautics and Astronautics，2024，50（6）：1940-1951 （in Chinese） doi: 10.13700/j.bh.1001-5965.2022.0544
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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.
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