A Framework Based on Historical Evolution Learning for Dynamic Multiobjective Optimization

TL;DR: Wang et al. as discussed by the authors proposed a historical evolution learning based framework to assist the static optimizers in fully using the historical evolution direction and Pareto optimal set distribution, and two new models are designed with a purpose of generating offsprings and environmental selection.

Abstract: Dynamic multiobjective optimization problems (DMOPs) are widely encountered in real-world applications and have received considerable attention in recent years. During the process of solving DMOPs, tracking the constantly changing Pareto optimal set (POS) quickly is the main task, and numerous methods have been proposed to achieve this task from different perspectives. However, how to improve the evolution of static optimizers via using historical information from past environments gets little attention. In fact, with adequate historical evolution information to learn, the trend of population evolution in the historical environments is promising to guide the future evolution and thus enhance the search ability. Therefore, in this paper, a historical evolution learning based framework is proposed to assist the static optimizers in fully using the historical evolution direction and POSs distribution. Specifically, two new models are designed with a purpose of generating offsprings and environmental selection, respectively. For enhancing the contribution of offsprings, a direction guidance model is developed to guide the offsprings according to the direction tendency of historical evolution. Furthermore, to improve the robustness of static optimizers and avoid the disturbance caused by misleading solutions, a manifold revise model is proposed to produce promising solutions via consulting historical POSs distribution. The proposed framework employs these two models collaboratively, and it is flexible and readily to be embedded into various dynamic response mechanisms and static optimizers in coping with DMOPs. The superiority of the framework has been comprehensively demonstrated by experimental comparison results on a variety of benchmark test problems.