Multi-Objective Particle Swarm Optimization Algorithm

The Multi-Objective Particle Swarm Optimization (MOPSO) algorithm is an advanced optimization technique based on swarm intelligence principles. This algorithm efficiently optimizes dual objective functions through iterative updates of particle positions and velocities while maintaining solution diversity. Key implementation features include Pareto dominance ranking for solution comparison, external archive maintenance for non-dominated solutions, and adaptive velocity calculations using cognitive and social parameters. The algorithm employs specialized crowding distance metrics to preserve solution distribution across the Pareto front. MOPSO finds extensive applications in complex optimization scenarios across engineering design, economic modeling, and decision support systems. Through its efficient population-based search mechanism, MOPSO significantly enhances problem-solving capabilities by balancing exploration and exploitation phases, ultimately improving optimization accuracy and computational efficiency for multi-objective problems.