Learning automata: an introduction
Learning automata: an introduction
Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
New advances and applications of combining simulation and optimization
WSC '96 Proceedings of the 28th conference on Winter simulation
Use of a self-adaptive penalty approach for engineering optimization problems
Computers in Industry
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In this paper, a new metaheuristic optimization approach is developed for the mixed integer decisions with constraints within a simulation model. Each decision variable is handled by an optimizer that uses a machine learning technique. At the beginning of each iteration, the decisions are selected randomly from their decision distributions. The performance evaluation is estimated during a short simulation run. The optimizers modify their selection distributions for the decisions that prove to be "good" performance judged against an advancing threshold value. Then, a new set of decisions is generated for the next run. When the average performance reaches a good competency, the threshold value is advanced to a higher level. Thus, the optimizers are forced to learn toward the optimal solution. In this paper, after brief explanation of the approach, we present an application to a challenging engineering problem dealing with pressure-vessel design.