Abstract
This paper proposes a novel optimization algorithm - adaptive chaotic particle swarm optimization (ACPSO). The short-term chaotic search is applied to the best particle in the iteration and an adaptive mechanism is used to control the scale of chaotic turbulence, which can avoid trapping in local optima and improve the searching performance of chaotic particle swarm optimization (CPSO). Simulation results and comparisons with the standard particle swarm optimization (PSO) illustrate the effectiveness of the ACPSO and show that the ACPOS has super ability in balancing the exploration and exploitation.
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