다수의 값을 갖는 이산적 문제에 적용되는 Particle Swarm Optimization
Particle Swarm Optimizations to Solve Multi-Valued Discrete Problems
1
조회
0
다운로드
5
인용
0
참고문헌
키워드
초록
Many real world optimization problems are discrete and multi-valued. Meta heuristics including Genetic Algorithm and Particle Swarm Optimization have been effectively used to solve these multi-valued optimization problems. However, extensive comparative study on the performance of these algorithms is still required. In this study, performance of these algorithms is evaluated with multi-modal and multi-dimensional test functions. From the experimental results, it is shown that Discrete Particle Swarm Optimization (DPSO) provides better and more reliable solutions among the considered algorithms. Also, additional experiments shows that solution quality of DPSO is not lowered significantly when bit size representing a solution increases. It means that bit representation of multi-valued discrete numbers provides reliable solutions instead of becoming barrier to performance of DPSO.
Abstract
Many real world optimization problems are discrete and multi-valued. Meta heuristics including Genetic Algorithm and Particle Swarm Optimization have been effectively used to solve these multi-valued optimization problems. However, extensive comparative study on the performance of these algorithms is still required. In this study, performance of these algorithms is evaluated with multi-modal and multi-dimensional test functions. From the experimental results, it is shown that Discrete Particle Swarm Optimization (DPSO) provides better and more reliable solutions among the considered algorithms. Also, additional experiments shows that solution quality of DPSO is not lowered significantly when bit size representing a solution increases. It means that bit representation of multi-valued discrete numbers provides reliable solutions instead of becoming barrier to performance of DPSO.
참고문헌 (11)
- Goldberg, D.E., "Alleles, Loci, and the TSP, Proceedings of the First International Conference on Genetic Algorithms", Lawrence Erlbaum Associates, pp.154~159 (1985)
- Kennedy, J., "Particle Swarm Optimization", Proceeding of the 1995 IEEE International Conference on Neural Networks, pp.1942~1948 (1995)
- Kennedy, J., "A Discrete Binary Version of the Particle Swarm Algorithm", IEEE International Conference on Systems, Man, and Cybernetics, pp.4104~4108 (1997)
- Laskari, E.C., "Particle Swarm Optimization for Integer Programming", Proceedings of the IEEE 2002 Congress on Evolutionary Computations, pp.1582~1587 (2002)
- Michaelwicz, Z., "Genetic Algorithms + Data Structures = Evolution Programs" (1992), Springer-Verlag
- Pugh, J., "Discrete Multi-Valued Particle Swarm Optimization", Proceedings of IEEE Swarm Intelligence Symposium, pp.103~110 (2006)
- Hwachang Song, "Photovoltaic System Allocation Using Discrete Particle Swarm Optimization with Multi-level Quantization", Journal of Electrical Engineering & Technology, 4(2), pp.185~193 (2009), 대한전기학회
- Vesterstrom, J., "A Comparative Study of Differential Evolution, Particle Swarm Optimization, and Evolutionary Algorithms on Numerical Benchmark Problems", Proceedings of the IEEE 2004 congress on Evolutionary Computation, pp.1980~1987 (2004)
- Veeramachaneni, K., "Probabilistically Driven Particle Swarms for Optimization of Multi Valued Discrete Problems: Design and Analysis", Proceedings of IEEE Swarm Intelligence Symposium, pp.141~149 (2007)
- Yang, Xie-She, "Test Problems in Optimization in Engineering Optimization : An Introduction with Metaheuristic Applications" (2010), John Wiley and Sons
- 임동순, "튜브 제조 시스템의 생산 스케쥴링 사례연구", 산업경영시스템학회지, 32(3), pp.110~117 (2009), 한국산업경영시스템학회