Journal of Computing Science and Engineering

  • 제9권2호
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  • Pages.39-50
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  • 2015
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  • 1976-4677(pISSN)
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  • 2093-8020(eISSN)
한국정보과학회 (Korean Institute of Information Scientists and Engineers)
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An Optimization Algorithm with Novel Flexible Grid: Applications to Parameter Decision in LS-SVM

  • Gao, Weishang (Information and Engineering College, Dalian University) ;
  • Shao, Cheng (Institute of Advanced Control, Dalian University of Technology) ;
  • Gao, Qin (School of Control Science and Engineering, Dalian University of Technology)
  • 투고 : 2014.08.25
  • 심사 : 2015.03.30
  • 발행 : 2015.06.30
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초록

Genetic algorithm (GA) and particle swarm optimization (PSO) are two excellent approaches to multimodal optimization problems. However, slow convergence or premature convergence readily occurs because of inappropriate and inflexible evolution. In this paper, a novel optimization algorithm with a flexible grid optimization (FGO) is suggested to provide adaptive trade-off between exploration and exploitation according to the specific objective function. Meanwhile, a uniform agents array with adaptive scale is distributed on the gird to speed up the calculation. In addition, a dominance centroid and a fitness center are proposed to efficiently determine the potential guides when the population size varies dynamically. Two types of subregion division strategies are designed to enhance evolutionary diversity and convergence, respectively. By examining the performance on four benchmark functions, FGO is found to be competitive with or even superior to several other popular algorithms in terms of both effectiveness and efficiency, tending to reach the global optimum earlier. Moreover, FGO is evaluated by applying it to a parameter decision in a least squares support vector machine (LS-SVM) to verify its practical competence.

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