Multi-objective optimization problem based on non-dominated sorting genetic algorithm

Abstract

Multi-objective optimization problem is a very common problem in mathematical modeling and daily research [1]. It belongs to the planning problem, and its ultimate goal is to find the optimal solution under certain constraints [2]. In order to solve the multi-objective programming problem, we usually use the multi-objective programming function in MATLAB, or take the traditional intelligent algorithm to solve, but the traditional algorithm is easy to fall into local optimal solution, and the error is large, which cannot meet the actual needs [3]. Therefore, this paper proposes an improved genetic algorithm, that is, non-dominated sorting genetic algorithm (NSGA-II) to solve multi-objective optimization problems, in order to better meet the research and practical needs [4]. In order to verify its superiority, this paper uses two examples, by comparing the results of the traditional algorithm and the improved genetic algorithm, using the comprehensive evaluation method to analyze, and finally draws the conclusion that the improved genetic algorithm is superior to the traditional algorithm.