Simulation optimization using the Particle Swarm Optimization with optimal computing budget allocation

Simulation has been applied in many optimization problems to evaluate their solutions' performance under stochastic environment. For many approaches solving this kind of simulation optimization problems, most of the attention is on the searching mechanism. The computing efficiency problems are seldom considered and computing replications are usually equally allocated to solutions. In this paper, we integrate the notion of optimal computing budget allocation (OCBA) into a simulation optimization approach, Particle Swarm Optimization (PSO), to improve the efficiency of PSO. The computing budget allocation models for two versions of PSO are built and two allocation rules PSOs_OCBA and PSObw_OCBA are derived by some approximations. The numerical result shows the computational efficiency of PSO can be improved by applying these two allocation rules.

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