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Comparative Study on Dimensionality and Characteristic of PSO

PSO의 특징과 차원성에 관한 비교연구

  • 박병준 (원광대학교 전기전자공학부) ;
  • 오성권 (수원대학교 전기공학과) ;
  • 김용수 (대전대학교 컴퓨터공학과) ;
  • 안태천 (원광대학교 전기전자공학부)
  • Published : 2006.04.01

Abstract

A new evolutionary computation technique, called particle swarm optimization(PSO), has been proposed and introduced recently. PSO has been inspired by the social behavior of flocking organisms, such as swarms of birds and fish schools and PSO is an algorithm that follows a collaborative population-based search model. Each particle of swarm flies around in a multidimensional search space looking for the optimal solution. Then, Particles adjust their position according to their own and their neighboring-particles experience. In this paper, characteristics of PSO such as mentioned are reviewed and compared with GA which is based on the evolutionary mechanism in natural selection. Also dimensionalities of PSO and GA are compared throughout numeric experimental studies. The comparative studies demonstrate that PSO is characterized as simple in concept, easy to implement, and computationally efficient and can generate a high-quality solution and stable convergence characteristic than GA.

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