Hybrid Particle Swarm Optimization Algorithm with Simplex Acceleration

A hybrid particle swarm optimization algorithm that enhances optimization efficiency through simplex acceleration. This algorithm leverages swarm intelligence principles by organizing multiple particles into a population and simulating their movement through search space to locate optimal solutions. Unlike conventional PSO, the hybrid variant incorporates simplex method operations (reflection, expansion, contraction) to accelerate convergence and improve search precision. Key implementation components include: velocity update equations with inertia weights, personal-best and global-best tracking, and simplex-based local refinement operations. The algorithm demonstrates particular effectiveness in complex problem domains and multi-objective optimization scenarios. By synergistically combining simplex acceleration with particle swarm dynamics, this hybrid approach achieves superior balance between global exploration and local exploitation, delivering enhanced optimization performance through computationally efficient iterative updates.