Dominance-based variable analysis for large-scale multi-objective problems

Optimization problems with multiple objectives and many input variables inherit challenges from both large-scale optimization and multi-objective optimization. To solve the problems, decomposition and transformation methods are frequently used. In this study, an improved control variable analysis is proposed based on dominance and diversity in Pareto optimization. Further, the decomposition method is used in a cooperative coevolution framework with orthogonal sampling mutation. The algorithm's performances are compared against the weighted optimization framework. The results show that the proposed decomposition method has much better accuracy compared to the traditional method. The results also show that the cooperative coevolution framework with a good grouping is very competitive. Additionally, the number of search directions in orthogonal sampling can be easily configured. A small number of search directions will reduce the search space greatly while also restricting the area that can be explored and vice versa.

Automated summary

This study proposes an improved control variable analysis based on dominance and diversity in Pareto optimization, and applies it in a cooperative coevolution framework with orthogonal sampling mutation. The results demonstrate that the proposed method outperforms the traditional method in terms of accuracy and competitiveness.