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

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