A FAST PARTICLE SWARM OPTIMIZATION

Particle swarm optimization (PSO) is a popular and robust strategy for optimization problems. One main difficulty in applying PSO to real-world applications is that PSO usually need a large number of fitness evaluations before a satisfying result can be obtained. This paper introduces a new "fast particle swarm optimization" (FPSO) that does not evaluate all new positions owning a fitness and associated reliability value of each particle of the swarm and the reliability value is only evaluated using the true fitness function if the reliability value is below a threshold. Moreover, applying random evaluation, reliability value update and self-adaptive threshold strategies to the FPSO further enhances the performance of the algorithm.

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