MATLAB Implementation of Multi-Objective Optimization Algorithm

This article presents a MATLAB implementation of multi-objective optimization algorithms designed to handle simultaneous optimization of numerous objectives. The program employs advanced optimization techniques such as Pareto front-based approaches and evolutionary algorithms including NSGA-II (Non-dominated Sorting Genetic Algorithm II) implementation. Key MATLAB functions utilized include gamultiobj for genetic algorithm-based multi-objective optimization, paretosearch for Pareto front identification, and custom objective function handlers. The code structure demonstrates effective constraint handling, population initialization methods, and fitness evaluation procedures. Through practical examples, we illustrate how to configure optimization parameters, define objective functions using anonymous function syntax (@x), and analyze results through Pareto-optimal solution visualization. This implementation serves as an educational tool for understanding multi-objective optimization concepts while providing practical framework for real-world applications in engineering design, resource allocation, and decision-making problems. The article includes code segments demonstrating algorithm initialization, iteration control, and result extraction techniques suitable for international technical audiences.