Multi-objective Optimization by non-dominationsearching based NSGA-II Algorithm

Abstract

The core elements of THE NSGA-II algorithm are genetic search and non-dominant sorting based on elite retention strategy It belongs to passive search and lacks an active search mechanism. In some cases, it will affect the efficiency of the algorithm in searching for the optimal Pareto front. According to the distribution characteristics of each generation of population samples, this paper calculates the non-dominated direction of the Pareto front from the solution set with Pareto ranks 1 and 2 in the population of that generation, and actively searches for a step toward the non-dominated direction from each sample of the Pareto front to find the better Non-dominant may solve and participate in the next generation of subgroup reconstruction. In the experimental verification, the effectiveness of the proposed method is verified through classic calculation examples and multi-objective optimization problems after power grid black start.