Multi-Objective Particle Swarm Optimization (MOPSO) Algorithm

In the fields of computer science and operations research, the Multi-Objective Particle Swarm Optimization (MOPSO) algorithm serves as a metaheuristic approach specifically designed for addressing multi-objective optimization problems. This algorithm integrates traditional Particle Swarm Optimization principles with advanced multi-objective optimization techniques, enabling effective solutions for complex multi-objective scenarios without relying on any prior knowledge. The implementation typically involves maintaining an external archive to store non-dominated solutions, utilizing dominance-based criteria for particle evaluation, and applying specialized crowding distance or niching techniques to preserve solution diversity across the Pareto front. MOPSO algorithms are widely employed in designing and optimizing complex engineering systems including aircraft, automotive systems, and power grid infrastructures. Additionally, they find significant applications in financial and economic domains such as portfolio optimization and risk management strategies. The algorithm's core functionality revolves around particle position updates using velocity equations that balance individual best positions and global best solutions from the archive, making MOPSO an invaluable tool for tackling real-world complex optimization challenges.