Multi-Objective Optimization Using Evolutionary Algorithms

This article introduces multi-objective optimization based on evolutionary algorithms. As a computational method in computer science, this optimization approach helps identify optimal solutions by employing genetic algorithms and similar evolutionary computation techniques. We will demonstrate the implementation using MATLAB programming language, focusing on key algorithmic components such as population initialization, fitness evaluation, selection mechanisms, crossover operations, and mutation processes. The implementation includes Pareto-based ranking methods for handling multiple objectives simultaneously, with detailed explanations of NSGA-II (Non-dominated Sorting Genetic Algorithm II) core functions including non-dominated sorting, crowding distance calculation, and elite preservation strategies. Readers interested in this topic can reference our provided MATLAB code containing optimization functions, visualization tools for Pareto fronts, and parameter configuration examples. This comprehensive guide will enhance your understanding of how evolutionary algorithms solve complex multi-objective optimization problems in engineering and scientific applications.