Multi-Objective Particle Swarm Optimization Algorithm

Multi-Objective Particle Swarm Optimization (MOPSO) is an optimization algorithm extensively applied in domains such as parameter optimization. It operates by simulating the collective behavior of particles to search for optimal solutions through iterative position and velocity updates. The algorithm employs key components including Pareto dominance principles for solution comparison, archive maintenance for storing non-dominated solutions, and specialized crowding distance mechanisms for diversity preservation. This approach enables simultaneous optimization of multiple conflicting objective functions, providing enhanced flexibility and effectiveness when addressing complex real-world problems. MOPSO has demonstrated significant success across engineering design, economic modeling, and scientific research applications, helping researchers and engineers discover superior solutions in design and optimization challenges. The algorithm typically implements position updates using velocity vectors influenced by personal best (pbest) and global best (gbest) positions, with special handling for multiple objectives through techniques like leader selection from non-dominated fronts. Consequently, Multi-Objective Particle Swarm Optimization serves as a powerful and valuable tool for multi-criteria decision-making problems.