Multi-Objective Particle Swarm Optimization Algorithm Implementation in MATLAB

This algorithm represents a MATLAB implementation of the Multi-Objective Particle Swarm Optimization (MOPSO) method. Through extensive debugging and testing cycles, comprehensive documentation has been developed to ensure immediate usability. The algorithm serves as an optimization framework specifically designed to address multi-objective optimization challenges. It builds upon the fundamental principles of particle swarm optimization while incorporating significant enhancements and adaptations tailored for multi-objective scenarios. Key implementation features include: - Utilization of a sophisticated fitness function that evaluates particle performance across multiple objectives simultaneously - Advanced position and velocity update mechanisms that maintain swarm diversity while converging toward Pareto-optimal solutions - Implementation of archiving techniques to store non-dominated solutions throughout the optimization process - Incorporation of crowding distance calculations to ensure proper distribution along the Pareto front The algorithm employs a structured approach where each particle's position represents a potential solution in the multi-dimensional search space. The velocity update equation combines cognitive and social components while maintaining constraints to prevent swarm explosion. The fitness evaluation module calculates objective function values for each particle, while the non-dominated sorting algorithm classifies solutions based on Pareto dominance. Although the implementation involves complex algorithmic components including dominance checks, solution archiving, and crowding distance computations, rigorous testing and debugging have validated both correctness and reliability. The code structure follows MATLAB best practices with clear function modularization, appropriate vectorization techniques, and comprehensive error handling. For interested users, additional technical details and illustrative code examples demonstrating parameter configuration, objective function definition, and result visualization are available to facilitate deeper understanding and practical application.