Adaptive control technique to enhance differential multi-objective evolutionary algorithm based on variation rate of quality measures

Abstract

When evaluating the multi-objective evolutionary algorithm, they not only focus on the quality of the solution set but also pay attention to the algorithm's ability to explore and exploit because that is the factor ensuring the convergence and diversity of the solution set. Maintaining a balance between exploration and exploitation of the algorithm is a difficult but interesting problem in the research field. In this article, we analyzed the relationship between the quality of the solution set and the algorithm's search efficiency to evaluate trends and propose an adaptive control technique based on the variation rate of quality measures to maintain a better balance between the exploration and exploitation capabilities. Experimental results on the multi-objective evolutionary algorithm using differential direction with some typical benchmark sets are highly competitive, demonstrating the ability to improve the algorithm's efficiency.