MOPSO Multi-Objective Particle Swarm Optimization Algorithm

The MOPSO (Multi-Objective Particle Swarm Optimization) algorithm is an effective optimization technique designed for solving multi-objective problems. Based on the standard particle swarm optimization framework, MOPSO operates by iteratively updating each particle's velocity and position to converge toward Pareto-optimal solutions. In MATLAB implementations, this algorithm facilitates solving diverse multi-objective challenges including resource allocation, path planning, and engineering design optimization. The MATLAB implementation typically involves key computational components: initializing particle positions randomly within search bounds, calculating velocity updates using inertia weights and acceleration coefficients, evaluating objectives through custom fitness functions, and maintaining an external archive for non-dominated solutions. Users must configure algorithm parameters appropriately for specific problems - including swarm size, iteration count, inertia weight strategy, and repository size - to achieve optimal performance. Algorithm enhancements can be implemented through code modifications such as adaptive weight adjustment mechanisms, constraint-handling techniques using penalty functions, and dominance-based sorting for archive management. Additional improvements may incorporate turbulence operators for diversity preservation, crowding distance calculations for solution spacing, and specialized leader selection strategies from the non-dominated set. These optimizations significantly improve convergence efficiency and solution accuracy while maintaining computational feasibility for complex multi-objective scenarios.