Particle Swarm Optimization (PSO), which was intended to be a population-based global search method, is known to suffer from premature convergence prior to discovering the true global minimizer. In this thesis, a novel regrouping mechanism is proposed, which aims to liberate particles from the state of premature convergence. This is done by automatically regrouping the swarm once particles have converged to within a pre-specified percentage of the diameter of the search space. The degree of uncertainty inferred from the distribution of particles at premature convergence is used to determine the magnitude of the regrouping per dimension. The resulting PSO with regrouping (RegPSO) provides a mechanism more efficient than repeatedly restarting the search by making good use of the state of the swarm at premature convergence. Results suggest that RegPSO is less problem-dependent and consequently provides more consistent performance than the comparison algorithms across the benchmark suite used for testing.
|Adviser||Mounir Ben Ghalia|
|School||THE UNIVERSITY OF TEXAS - PAN AMERICAN|
|Subjects||Mathematics; Electrical engineering; Computer science|
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