Evolutionary Constrained Multi-Factorial Optimization Based on Task Similarity
Shio Kawakami, Keiki Takadama, Hiroyuki Satō · 2024
This paper proposes a constrained multi-factorial optimization algorithm, MFEA/TS (Multi-Factorial Evolutionary Algorithm based on Task Similarity), which estimates the constraint similarity and objective similarity through solution distributions in the variable space. It integrates these similarities into parent selection to enhance the simultaneous optimization of multiple problems. Additionally, this paper introduces continuous test problems that enable adjusting constraint and objective similarities visually by setting feasible regions of constraints and optimal solutions in the variable space, respectively. These test problems allow configuring conflicts between constraint and objective similarities. Experimental results on these test problems demonstrate that the proposed MFEA/TS can effectively detect the constraint and objective similarities designed in the test problems, thereby enhancing multi-factorial optimization. The proposed MFEA/TS achieves higher search performance compared to conventional algorithms such as MFEA, MFEA-II, MFDE, and SOEA.