A similar environment transfer strategy for dynamic multiobjective optimization

Abstract

Solving dynamic multiobjective optimization problems (DMOPs) is extremely challenging due to the need to address multiple conflicting objectives that change over time. Transfer prediction-based strategies typically leverage solutions from historical environments to generate an initial population for a new environment. However, these strategies often overlook the similarity between the historical and new environments, which can negatively impact the quality of the initial population. To address this issue, we propose a similar environment transfer strategy. Firstly, we select Pareto-optimal solutions from a randomly generated population in the new environment to form a prior Pareto set (PS). The prior PS is expand by oversampling sparse solutions. Then, we apply the maximum mean discrepancy (MMD) to measure the discrepancy between the prior PS and the PS from each historical environment. The historical environment with the smallest MMD is identified as the similar environment. Finally, we use solutions from this similar environment to establish a kernelized easy transfer learning model, which is employed to predict the quality of random solutions in the new environment. The initial population is formed by combining excellent solutions predicted by the model with the prior PS. Experimental results demonstrate that the proposed strategy significantly outperforms several state-of-the-art strategies.