Enhanced Multi-Objective Particle Swarm Optimization Algorithm

This article presents an enhanced Multi-Objective Particle Swarm Optimization (MOPSO) algorithm that has demonstrated remarkable success in solving classical multi-objective optimization problems such as ZDT, KUR, and SCH benchmarks. The algorithm incorporates dynamic inertia weight adjustment and crowding distance-based archive maintenance to preserve solution diversity. By modifying the f1 and f2 objective functions in the code implementation (typically through function handles or callback methods), users can readily adapt this algorithm to solve similar optimization challenges. The discussion covers performance analysis through convergence metrics and hypervolume indicators, along with practical application domains in engineering design and resource allocation. Implementation examples include position update equations with constraint handling and non-dominated sorting techniques to illustrate the algorithm's effectiveness. This enhanced MOPSO approach provides researchers and practitioners with a robust framework for handling complex multi-objective optimization scenarios.