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http://dx.doi.org/10.5302/J.ICROS.2006.12.4.328

Comparative Study on Dimensionality and Characteristic of PSO  

Park Byoung-Jun (원광대학교 전기전자공학부)
Oh Sung-Kwun (수원대학교 전기공학과)
Kim Yong-Soo (대전대학교 컴퓨터공학과)
Ahn Tae-Chon (원광대학교 전기전자공학부)
Publication Information
Journal of Institute of Control, Robotics and Systems / v.12, no.4, 2006 , pp. 328-338 More about this Journal
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.
Keywords
particle swarm optimization; genetic algorithm; dimensionality; comparative study;
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