Differential Evolution with Adaptive Grid-Based Mutation Strategy for Multi-Objective Optimization

Differential Evolution (DE) has been extensively adopted for multi-objective optimization due to its efficient and straightforward framework. In DE, the mutation operator influences evolution of population. this paper, an adaptive Grid-based Multi-Objective is proposed address (ad-GrMODE). ad-GrMODE, grid environment employed perform a strategy in conjunction with performance indicators. The reflects convergence diversity together but associated user-specified parameter “div”. To solve problem, we adaptively tune Among DE strategies, “DE/current-to-best/1” applied single-objective optimization. This paper extends application addition, two-stage environmental selection where first stage, one-to-one between parent corresponding offspring solution performed. preserve elitism, stochastic respect metrics. We conducted experiments on 16 benchmark problems, including DTLZ WFG, validate ad-GrMODE algorithm. Besides evaluated method real-world problems. Results show that algorithm outperforms eight state-of-the-art algorithms.