In this paper, we introduce a hybrid technique based on particle swarm optimization (PSO) algorithm combined with the nonlinear simplex search method. This approach is applied to multimodal function optimizing tasks. To evaluate its reliability and efficiency, we empirically compare the performance of two variants of the Particle Swarm Optimizer with our hybrid algorithm. The computational results obtained in experiments on large variety of test functions indicate that the hybrid algorithm is competitive with other techniques, and can be successfully applied to more demanding problem domains.
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