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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2017.1
- 수록면
- 95 - 104 (10page)
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This paper deals with solution methods for discrete and multi-valued optimization problems. The objective function of the problem incorporates noise effects generated in case that fitness evaluation is accomplished by computer based experiments such as Monte Carlo simulation or discrete event simulation. Meta heuristics including Genetic Algorithm (GA) and Discrete Particle Swarm Optimization (DPSO) can be used to solve these simulation based multi-valued optimization problems. In applying these population based meta heuristics to simulation based optimization problem, samples size to estimate the expected fitness value of a solution and population (particle) size in a generation (step) should be carefully determined to obtain reliable solutions.
Under realistic environment with restriction on available computation time, there exists trade-off between these values. In this paper, the effects of sample and population sizes are analyzed under well-known multi-modal and multi-dimensional test functions with randomly generated noise effects. From the experimental results, it is shown that the performance of DPSO is superior to that of GA. While appropriate determination of population sizes is more important than sample size in GA, appropriate determination of sample size is more important than particle size in DPSO. Especially in DPSO, the solution quality under increasing sample sizes with steps is inferior to constant or decreasing sample sizes with steps. Furthermore, the performance of DPSO is improved when OCBA (Optimal Computing Budget Allocation) is incorporated in selecting the best particle in each step.
In applying OCBA in DPSO, smaller value of incremental sample size is preferred to obtain better solutions.
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참고문헌 (20)
참고문헌 신청
Aizawa, A.N. / 1993 / Dynamic Control of Genetic Algorithms in a Noisy Environment / Proceeding of the Fifth International Conference on Genetic Algorithms : 103 ~ 115
Bartz-Beielstein, T / 2005 / Particle Swarm Optimization and Sequential Sampling in Noisy Environments / Proceedings of the 6th Metaheuristics International Conference (MIC2005) :
Branke, J. / 2001 / Genetic and Evolutionary Computation / Morgan Kaufman : 243 ~ 250
Chen, C.-H. / 2000 / Simulation Budget Allocation for Further Enhancing the Efficiency of Ordinal Optimization / Discrete Event Dynamic Systems : Theory and Applications 10 (
Fitzpatrick, J.M. / 1988 / Genetic Algorithm in Noisy Environments / Machine Learning 3 (2) : 101 ~ 120
Bartz-Beielstein, T / 2005 / Particle Swarm Optimization and Sequential Sampling in Noisy Environments / Proceedings of the 6th Metaheuristics International Conference (MIC2005) :
Branke, J. / 2001 / Genetic and Evolutionary Computation / Morgan Kaufman : 243 ~ 250
Chen, C.-H. / 2000 / Simulation Budget Allocation for Further Enhancing the Efficiency of Ordinal Optimization / Discrete Event Dynamic Systems : Theory and Applications 10 (
Fitzpatrick, J.M. / 1988 / Genetic Algorithm in Noisy Environments / Machine Learning 3 (2) : 101 ~ 120
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