Improved Particle Swarm Optimization Algorithm for Constrained Optimization Problems

Program Name: Improved Particle Swarm Optimization Algorithm for solving optimization problems with various constraints | Program Function: This algorithm introduces diversified search strategies and adaptive weight adjustment mechanisms to more effectively solve optimization problems with various constraints | Input Conditions: Users need to provide various initial conditions and parameter settings to guide the algorithm execution | Output Values: The algorithm will output the optimal solution position and corresponding minimum function value, helping users better understand the solution space and optimization results. Implementation Details: The algorithm incorporates diversity maintenance techniques through niching strategies and dynamically adjusts inertia weights based on convergence status. Constraint handling is implemented using penalty function methods or feasibility rules. Key functions include particle position update with velocity clamping, boundary constraint handling, and fitness evaluation with constraint violations.