DocumentCode :
2998401
Title :
A new optimization heuristic for continuous and integer decisions with constraints in simulation
Author :
Ozden, Mufit
Author_Institution :
Dept. of Comput. Sci. & Syst. Anal., Miami Univ., Oxford, OH, USA
fYear :
2005
fDate :
4-7 Dec. 2005
Abstract :
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.
Keywords :
integer programming; learning (artificial intelligence); simulation; statistical distributions; continuous decision; decision distribution; engineering problem; machine learning; metaheuristic optimization; mixed integer decision; optimization heuristic; pressure-vessel design; simulation constraint; simulation model; Computational modeling; Constraint optimization; Decision making; Design engineering; Distributed decision making; Evolutionary computation; Learning automata; Machine learning; Random variables; Simulated annealing;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Simulation Conference, 2005 Proceedings of the Winter
Print_ISBN :
0-7803-9519-0
Type :
conf
DOI :
10.1109/WSC.2005.1574331
Filename :
1574331
Link To Document :
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