Abstract
Particle swarm optimization (PSO) is a novel population-based searching technique proposed as an alternative to genetic algorithm (GA). It has had wide applications in a variety of fields. We suggest a hybrid clustering algorithm, which applies the combination of conventional PSO and SA (Simulated Annealing) algorithm to the process of K-means clustering in order to solve the problem of premature convergence. In addition we develop an adjustment algorithm, which modifies the acceleration constants of PSO by comparison of global and local best position, and is applied to the mixture algorithm named as SA-PSO so as to minimize the search of unnecessary areas and enhance performance. We simulated and compared three algorithms (K-PSO, SA-PSO and Adjusted SA-PSO). The results demonstrated our new approach (Adjusted SA-PSO) had the most excellent performance in usefulness and reliability evaluation, which denotes fitness function and mean absolute error respectively.
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Lee, D.S., Seo, Y.W., Lee, K.C. (2011). An Adjusted Simulated Annealing Approach to Particle Swarm Optimization: Empirical Performance in Decision Making. In: Nguyen, N.T., Kim, CG., Janiak, A. (eds) Intelligent Information and Database Systems. ACIIDS 2011. Lecture Notes in Computer Science(), vol 6592. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20042-7_57
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DOI: https://doi.org/10.1007/978-3-642-20042-7_57
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