Multi-Objective Hybrid Algorithm Integrating Gradient Search and Evolutionary Mechanisms

• DOI: 10.59782/sidr.v2i1.108
• Publication Date: 2024-10-07T15:38:18Z
• Authors: Zhu Caicheng; Tang Zhili; Zhao Xin; Cao Fang

The current multi-objective evolutionary algorithm (MOEA) has attracted much attention because of its good global exploration ability, but local search ability near the optimal value is relatively weak, and for optimization prob lems with large-scale decision variables, number populations iterations required by MOEA are very large, so efficiency low. Gradient-based algorithms can overcome these problems well, they difficult to be applied (MOPs). Therefore, this paper introduced random weight function on basis weighted average gradient, developed gradient operator, combined it non-dominated genetic based reference points (NSGA- III) proposed Deb in 2013 develop (MOGBA) Hybrid Evolutionary (HMOEA). latter greatly enhances capability while retaining NSGA-III. Numerical experiments show that HMOEA excellent capture various Pareto formations, improved times compared typical algorithms. And further aerodynamic problem RAE2822 airfoil, ideal front obtained, indicating an efficient potential applications design.