Improved Particle Swarm Optimization Algorithm for Constrained Optimization Problems

Constrained optimization problems present common challenges in engineering and scientific computing, where traditional optimization algorithms often prove inefficient when handling complex constraints. The Particle Swarm Optimization (PSO) algorithm has emerged as a powerful tool for solving such problems due to its simplicity and effectiveness. This work explores an improved PSO algorithm specifically designed for optimization problems with constraints. The core concept of this algorithm involves using swarm intelligence to find optimal solutions. Each particle represents a potential solution that moves through the solution space, adjusting its position based on individual experience and collective knowledge. For constraint handling, the algorithm incorporates specialized mechanisms to ensure the search process remains within feasible regions throughout optimization. The enhanced PSO algorithm introduces three key improvements: First, it implements dynamic constraint handling techniques that incorporate constraint violation degrees into fitness evaluation. Second, it employs adaptive inertia weighting that maintains higher values during initial phases to promote global exploration, then gradually reduces weights to enhance local exploitation. Third, it integrates an elitism preservation strategy to prevent the loss of high-quality solutions during iterations. Algorithm implementation requires input parameters including problem definition, initial conditions, and control parameters. The output consists of the optimal solution's position and the minimized objective function value. By balancing exploration and exploitation capabilities, this algorithm effectively escapes local optima and finds global optima or high-quality approximate solutions within reasonable timeframes. In practical applications, this improved algorithm proves particularly suitable for nonlinear, multimodal, high-dimensional constrained optimization problems. Compared to traditional PSO, it demonstrates significant improvements in convergence speed and solution accuracy, while showing reduced sensitivity to initial parameter settings and enhanced robustness.