Sample-path optimization in simulation
WSC '94 Proceedings of the 26th conference on Winter simulation
An integrated framework for deterministic and stochastic optimization
Proceedings of the 29th conference on Winter simulation
Simulation Budget Allocation for Further Enhancing theEfficiency of Ordinal Optimization
Discrete Event Dynamic Systems
Journal of Global Optimization
A large deviations perspective on ordinal optimization
WSC '04 Proceedings of the 36th conference on Winter simulation
A review of particle swarm optimization. Part I: background and development
Natural Computing: an international journal
Natural Computing: an international journal
Differentiated service inventory optimization using nested partitions and MOCBA
Computers and Operations Research
A new perspective on feasibility determination
Proceedings of the 40th Conference on Winter Simulation
Simulation optimization using the cross-entropy method with optimal computing budget allocation
ACM Transactions on Modeling and Computer Simulation (TOMACS)
The particle swarm - explosion, stability, and convergence in amultidimensional complex space
IEEE Transactions on Evolutionary Computation
Optimal computing budget allocation in particle swarm optimization
Proceedings of the 15th annual conference on Genetic and evolutionary computation
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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.