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Article Details

Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

Separability Detection Cooperative Particle Swarm Optimizer based on Covariance Matrix Adaptation

Author 1: Sheng Fuu Lin
Author 2: Yi-Chang Cheng
Author 3: Jyun-Wei Chang
Author 4: Pei-Chia Hung

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Digital Object Identifier (DOI) : 10.14569/IJACSA.2012.030404

Article Published in International Journal of Advanced Computer Science and Applications(IJACSA), Volume 3 Issue 4, 2012.

  • Abstract and Keywords
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Abstract: The particle swarm optimizer (PSO) is a population-based optimization technique that can be widely utilized to many applications. The cooperative particle swarm optimization (CPSO) applies cooperative behavior to improve the PSO on finding the global optimum in a high-dimensional space. This is achieved by employing multiple swarms to partition the search space. However, independent changes made by different swarms on correlated variables will deteriorate the performance of the algorithm. This paper proposes a separability detection approach based on covariance matrix adaptation to find non-separable variables so that they can previously be placed into the same swarm to address the difficulty that the original CPSO encounters.

Keywords: cooperative behavior; particle swarm optimization; covariance matrix adaptation; separability.

Sheng Fuu Lin, Yi-Chang Cheng, Jyun-Wei Chang and Pei-Chia Hung, “Separability Detection Cooperative Particle Swarm Optimizer based on Covariance Matrix Adaptation” International Journal of Advanced Computer Science and Applications(IJACSA), 3(4), 2012. http://dx.doi.org/10.14569/IJACSA.2012.030404

@article{Lin2012,
title = {Separability Detection Cooperative Particle Swarm Optimizer based on Covariance Matrix Adaptation},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2012.030404},
url = {http://dx.doi.org/10.14569/IJACSA.2012.030404},
year = {2012},
publisher = {The Science and Information Organization},
volume = {3},
number = {4},
author = {Sheng Fuu Lin and Yi-Chang Cheng and Jyun-Wei Chang and Pei-Chia Hung}
}


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