Enhanced Global Particle Swarm Optimization Algorithm

Building upon the improved global particle swarm optimization framework, we can integrate advanced concepts from recent research to enhance algorithmic performance. Key implementations include introducing convergence factors and adaptive inertia weight mechanisms, which significantly impact the algorithm's convergence speed and search capabilities. The convergence factor can be programmed to dynamically adjust particle movement constraints during optimization, typically implemented through a constriction coefficient that ensures stable convergence. The adaptive inertia weight can be coded to linearly/non-linearly decrease during iterations, balancing global exploration and local exploitation phases. These enhancements can be implemented using update equations like: w = w_max - (w_max - w_min) * (current_iteration / max_iterations) v_i = w * v_i + c1 * rand() * (pbest_i - x_i) + c2 * rand() * (gbest - x_i) where w represents the adaptive inertia weight. Through these algorithmic refinements, the optimized PSO demonstrates markedly improved problem-solving efficiency and superior performance metrics compared to conventional basic implementations.