학술논문
The Effect of Sample and Particle Sizes in Discrete Particle Swarm Optimization for Simulation-based Optimization Problems
이용수 7
- 영문명
- 발행기관
- 한국산업경영시스템학회
- 저자명
- Dong-Soon Yim(임동순)
- 간행물 정보
- 『산업경영시스템학회지』제40권 제1호, 95~104쪽, 전체 10쪽
- 주제분류
- 경제경영 > 경영학
- 파일형태
- 발행일자
- 2017.03.30
4,000원
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국문 초록
영문 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.
목차
1. 서 론
2. 이산 최적화 문제 해결을 위한 GA와 DPSO
3. DPSO에서의 OCBA 적용
4. 실험 및 분석
4. 결 론
키워드
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