Specific Implementation Cases of Multi-Objective Optimization

This article presents several specific implementations of multi-objective optimization programs developed in MATLAB, ideal for beginners to study and share. We will conduct an in-depth analysis of each program's advantages and limitations, along with practical application scenarios. The discussion includes fundamental concepts of multi-objective optimization and its applications across various domains such as engineering design, finance, and manufacturing. Additionally, we introduce commonly used algorithms and techniques in multi-objective optimization, including Genetic Algorithms (with population initialization, fitness evaluation, and Pareto front selection mechanisms), Simulated Annealing (featuring temperature scheduling and acceptance probability functions), and Particle Swarm Optimization (implementing velocity updates and position tracking). Each algorithm section includes MATLAB code structure explanations focusing on key functions like objective function handling, constraint management, and convergence criteria. Through this article, you will gain both theoretical knowledge and practical implementation skills in multi-objective optimization, providing a solid foundation for further exploration.