MATLAB Code Implementation for Multi-Objective Optimization

This article explores multi-objective optimization methodologies implemented in MATLAB, a powerful approach for handling complex problems with multiple competing objectives. We present two core MATLAB files that demonstrate practical implementations: one likely containing optimization algorithm routines (potentially using evolutionary algorithms like NSGA-II or weighted sum approaches), and another handling objective function definitions and constraint management. The accompanying PowerPoint documentation provides comprehensive guidance on algorithm selection parameters, convergence criteria, and practical application scenarios. Through structured code examples featuring key functions such as fgoalattain or gamultiobj for Pareto front generation, this resource enables users to understand trade-off analysis between objectives, implement constraint handling mechanisms, and visualize optimization results using MATLAB's plotting capabilities. By studying these materials, you will gain proficiency in formulating multi-objective problems, selecting appropriate solution strategies, and applying these techniques to real-world engineering and research challenges.