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Particle Swarm Optimizations to Solve Multi-Valued Discrete Problems

다수의 값을 갖는 이산적 문제에 적용되는 Particle Swarm Optimization

  • Yim, Dong-Soon (Department of Industrial and Management Engineering, Hannam University)
  • 임동순 (한남대학교 산업경영공학과)
  • Received : 2013.08.08
  • Accepted : 2013.09.06
  • Published : 2013.09.30

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.

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