산업경영시스템학회지 (Journal of Korean Society of Industrial and Systems Engineering)

  • 제40권1호
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  • Pages.95-104
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  • 2017
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  • 2005-0461(pISSN)
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  • 2287-7975(eISSN)
한국산업경영시스템학회 (Society of Korea Industrial and System Engineering)
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시뮬레이션 최적화 문제 해결을 위한 이산 입자 군집 최적화에서 샘플수와 개체수의 효과

The Effect of Sample and Particle Sizes in Discrete Particle Swarm Optimization for Simulation-based Optimization Problems

  • 임동순 (한남대학교 산업경영공학과)
  • Yim, Dong-Soon (Department of Industrial and Management Engineering, Hannam University)
  • 투고 : 2017.02.09
  • 심사 : 2017.03.20
  • 발행 : 2017.03.31
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초록

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

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