An Inverse Model-based Solution Generation Method for Evolutionary Multi-objective Optimization Algorithms

Tianye Shu, Hisao Ishibuchi, Lie Meng Pang

Abstract: For a multi-objective optimization problem, an inverse model approximates a mapping from the objective space to the decision space. Recently, the inverse model has been used in some evolutionary multi-objective optimization algorithms (EMOAs) to generate offspring solutions. In those algorithms, the inverse model is usually built based on the current population and is used to generate offspring solutions around the current population. In this paper, we utilize both the current and previous populations to estimate the locations of future (i.e., improved) solutions in the objective space. The estimated objective vectors are presented to the inverse model to generate improved solutions in the decision space for future generations. Thus, our inverse model is used to generate better solutions than the current solutions instead of generating new solutions by interpolating the current solutions. Based on this idea, a solution generation method is proposed, which can be easily embedded into almost all EMOAs. We embed our method into two standard EMOAs (i.e., NSGA-II and NSGA-III) and two inverse model-based EMOAs (i.e., IM-MOEA and IM-MOEA/D). Our experimental results show that our method improves the performance of these EMOAs in most of the three-objective WFG and LSMOP problems.

External IDs:dblp:conf/ijcnn/ShuIP25