Abstract
At the late evolution stage of the basic particle swarm optimization (BPSO), convergence process starts to slow down and the best fitness particle fluctuates around the globally-optimal solution, which may give rise to decrease on convergence precision of the BPSO. Therefore, an improved algorithm for particle swarm optimization was proposed. The modified version of PSO uses a controllable velocity-updating mode to control velocity of evolved particles, which is expected to be useful for tuning the search for the globally-optimal solution. Optimization examples showed that the improved PSO is superior to the BPSO, on not only convergence precision but also computation expense.
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