PDPSO : enhanced particle swarm optimization algorithm based on dynamics model = PDPSO: 동역학 모델에 기반한 개선된 입자 군집 최적화 알고리즘 enhanced particle swarm optimization algorithm based on dynamics model

The canonical PSO algorithm is a robust stochastic evolutionary computation technique based on the information exchange between the particles. The Potential and Dynamics-based PSO algorithm is inspired by the canonical PSO and it is based on the motion dynamics. This thesis proposes a novel PSO algorithm, based on the potential field and the motion dynamics model. It is assumed that particles form potential fields and each particle has its own mass. The potential filed and mass are modeled by the particles’ fitness value. By using these fitness based models, the proposed algorithm performs well, in particular, in avoiding the local minima compare to the original PSO. Although the PDPSO algorithm is designed to have more exploration capability compare to the canonical algorithm. The algorithm shows more exploration power, but the exploitation capability was weakened. To solve the problem, an updated version of PDPSO which have more exploitation capability is proposed while the exploration power is kept in the updated algorithm. In addition, the relationship between swarm divergence and parameters of PDPSO is discussed.
Advisors
Kim, Jong-Hwanresearcher김종환researcher
Publisher
한국과학기술원
Issue Date
2009
Identifier
308816/325007  / 020073216
Language
eng
Description

학위논문(석사) - 한국과학기술원 : 전기및전자공학전공, 2009.2, [ vi, 45 p. ]

Keywords

Particle swarm optimization; Evolutionary computation; Optimization; PSO; 입자 군집 최적화; 진화연산; 최적화; Particle swarm optimization; Evolutionary computation; Optimization; PSO; 입자 군집 최적화; 진화연산; 최적화

URI
http://hdl.handle.net/10203/38696
Link
http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=308816&flag=t
Appears in Collection
EE-Theses_Master(석사논문)
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