A Multi-Objective Evolutionary Algorithm Based on Adaptive Grid

With the wide application of optimization problems in engineering practice and scientific research, evolutionary algorithms have been gradually studied. Recently, the main solutions for multi-objective optimization problems include multi-objective evolutionary algorithm based on Pareto dominance or decomposition, and hybrid algorithm. Ground on this, we propose the IGNSGA algorithm, which combines the NSGA-II algorithm based on the Pareto dominance relationship with the optimization mechanism of adaptive meshing after adding the dominant individual accumulation strategy. This way can increase the search pressure, improve the search efficiency, and guarantee the convergence and distribution of Pareto solution, which makes the algorithm have universal applicability.