ACM Transactions on Mathematical Software (TOMS)
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
A tutorial on simulation optimization
WSC '92 Proceedings of the 24th conference on Winter simulation
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Computational intelligence PC tools
Computational intelligence PC tools
A review of simulation optimization techniques
Proceedings of the 30th conference on Winter simulation
Simulation optimization research and development
Proceedings of the 30th conference on Winter simulation
Comparison of global search methods for design optimization using simulation
WSC '91 Proceedings of the 23rd conference on Winter simulation
An approach for finding discrete variable design alternatives using a simulation optimization method
Proceedings of the 31st conference on Winter simulation: Simulation---a bridge to the future - Volume 1
Simulation optimization with the linear move and exchange move optimization algorithm
Proceedings of the 31st conference on Winter simulation: Simulation---a bridge to the future - Volume 1
The particle swarm: social adaptation in information-processing systems
New ideas in optimization
Simulation Made Easy: A Manager's Guide
Simulation Made Easy: A Manager's Guide
Swarm Intelligence applied in synthesis of hunting strategies in a three-dimensional environment
Expert Systems with Applications: An International Journal
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Simulation optimization is rapidly becoming a mainstream tool for simulation practitioners, as several simulation packages include add-on optimization tools. In this paper we are concentrating on an automated optimization approach that is based on adapting model parameters in order to handle uncertainty that arises from stochastic elements of the process under study. We particularly investigate the use of global search methods in this context, as these methods allow the optimization strategy to escape from sub-optimal (i.e., local) solutions and, in that sense, they improve the efficiency of the simulation optimization process. The paper compares several global search methods and demonstrates the successful application of the Particle Swarm Optimizer to simulation modeling optimization and design of a steelworks plant, a representative example of the stochastic and unpredictable behavior of a complex discrete event simulation model.