Enhancing Moea/d with Escape Mechanisms
Bilel Derbel, Geoffrey Pruvost, Byung‐Woo Hong · 2021
In this paper, we investigate the design of escape mechanisms within the state-of-the-art decomposition-based evolutionary multi-objective Moea/d framework. We propose to track the number of improvements made with respect to the single-objective sub-problems defined by decomposition. This allows us to compute an estimated sub-problem improvement probability which serves as an activation signal for some solution perturbation mechanism to occur. We report the benefits of such an approach by conducting a comprehensive experimental analysis on a broad range of combinatorial bi-objective bit-string landscapes with variable dimensions and ruggedness. Our empirical findings provide evidence on the effectiveness of the proposed escape mechanism and its ability in providing substantial improvement over conventional Moea/d. Besides, we provide a detailed analysis of parameters impact and anytime behavior in order to better highlight the strength of the proposed techniques as a function of available budget and problem characteristics.