Enhanced Particle Swarm Optimization Algorithm with Toolbox

This article introduces a novel enhanced particle swarm optimization algorithm accompanied by a specialized toolbox designed to assist researchers in optimizing algorithm performance more effectively. The implementation features adaptive parameter tuning and dynamic inertia weight adjustment through MATLAB's optimization functions.

We begin by detailing the algorithm's core principles and workflow, including key modifications such as velocity clamping mechanisms and neighborhood topology implementations using matrix operations. The toolbox incorporates essential functions for population initialization (initializeSwarm()), fitness evaluation (evaluateFitness()), and convergence monitoring (plotConvergence()), along with practical examples of parameter configuration files. We then demonstrate how to leverage these features through script templates and GUI interfaces to enhance optimization performance, including batch processing capabilities for multiple test functions.

The algorithm's performance across various application scenarios is analyzed through benchmark comparisons using sphere, Rosenbrock, and Rastrigin functions. Case studies from engineering design and machine learning applications illustrate practical implementation results, showing significant improvements in convergence speed and solution quality compared to standard PSO variants.

Finally, we summarize the algorithm's advantages in handling multimodal optimization problems and its limitations in high-dimensional spaces. Potential enhancement directions include hybrid approaches combining genetic algorithm operators and parallel computing implementations using MATLAB's Parallel Computing Toolbox, providing researchers with comprehensive insights for further development and application.