Evolution strategies –A comprehensive introduction
Natural Computing: an international journal
Molecular docking with multi-objective Particle Swarm Optimization
Applied Soft Computing
Expert Systems with Applications: An International Journal
An effective hybrid PSO-based algorithm for flow shop scheduling with limited buffers
Computers and Operations Research
Comprehensive learning particle swarm optimizer for global optimization of multimodal functions
IEEE Transactions on Evolutionary Computation
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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.