Adaptive Weight Method in Improved Particle Swarm Optimization Algorithm
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Resource Overview
Adaptive Weight Method in Improved PSO Algorithm Balanced Global Exploration and Local Refinement Capabilities
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In the improved Particle Swarm Optimization algorithm, we have implemented an adaptive weight method to balance the global exploration and local refinement capabilities of the PSO algorithm. This approach dynamically adjusts the inertia weight parameter during optimization, typically decreasing from a higher initial value (e.g., 0.9) to a lower final value (e.g., 0.4) as iterations progress. The implementation involves calculating weights based on iteration count using formulas like w = w_max - (w_max - w_min) * (current_iteration / max_iterations). This adaptive mechanism allows particles to explore more broadly in early stages while focusing on local refinement later, significantly enhancing algorithm performance and convergence efficiency.
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