A robust performance evaluation approach for solution preservation in multiobjective optimization

Many real-world optimization problems have complex features, such as bias, multimodel, etc. Existing evolutionary algorithms mainly utilize solutions' current performance to decide their survivals, which are not comprehensive enough to describe the evolving trend, and may misguide the evolve decision. In this paper, a novel robust performance evaluation approach for evolutionary multiobjective optimization algorithm is proposed. Here, the robustness refers to the performance fluctuation degree among several generations, which can be expressed by interval values in respect to the decision and objective spaces. Based on the robust performance evaluation, solutions can be selected and preserved considering their historical performance, and thus, the exploration strength in convergence potential areas can be maintained. Meanwhile, to construct an evolutionary algorithm that embeds robustness evaluation, a robust elite managerial method and a learning-based updating strategy are also designed. Experiments on multiobjective benchmark problems and a real-world optimization in a robotic manipulation system have proved the superiority of the proposed approach.

Automated summary

This paper proposes a novel robust performance evaluation approach for evolutionary multiobjective optimization algorithm, which selects and preserves solutions based on their historical performance to maintain exploration strength in convergence potential areas.