Multi-Objective Particle Swarm Optimization Algorithm Implementation

In the current landscape of multi-objective optimization algorithms, MOPSO stands out as one of the most widely adopted approaches. I developed this implementation during my research project, drawing inspiration from Coello's influential paper. Despite its straightforward architecture, the code includes extensive inline comments and documentation to facilitate understanding and modification. The implementation follows standard MOPSO principles with key components including: - Particle initialization with random velocity and position vectors - Archive maintenance for non-dominated solutions using crowding distance - Leader selection mechanism from the Pareto-optimal front - Velocity and position updates with inertial weighting - Boundary handling and constraint management techniques Throughout the development process, I referenced multiple related publications and studies to incorporate best practices and innovative approaches. This implementation not only deepened my understanding of MOPSO's operational mechanics and realization methods but also provides a valuable reference example for researchers and practitioners working with multi-objective optimization problems.