Enhanced Particle Swarm Optimization

Particle Swarm Optimization (PSO) often suffers from premature convergence during optimization problem solving, making it difficult to locate the global optimum solution. To address this limitation, several enhancement strategies can be implemented. Introducing mutation operations can significantly improve the algorithm's exploration capabilities, increasing the probability of discovering superior solutions. In code implementation, this typically involves randomly perturbing particle positions after velocity updates using Gaussian or uniform distribution mutations. Parameter tuning represents another critical improvement avenue. Adjusting key parameters like inertia weight (controlling momentum preservation), cognitive acceleration coefficient (local best influence), and social acceleration coefficient (global best influence) can optimize performance. A common approach involves implementing adaptive inertia weight strategies that decrease linearly from 0.9 to 0.4 during iterations. Hybrid optimization techniques combining PSO with other algorithms like Genetic Algorithms (GA) or Simulated Annealing (SA) can further enhance performance. For instance, integrating GA's crossover operations with PSO's velocity update mechanism creates a more robust search strategy. Similarly, incorporating SA's probabilistic acceptance criterion helps escape local optima. These multidimensional improvements - through mutation operations, parameter optimization, and hybrid algorithms - collectively enhance PSO's solution quality and convergence reliability for complex optimization problems.