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An Improved Particle Swarm Optimization Algorithm Based on Quotient Space Theory

An Improved Particle Swarm Optimization Algorithm Based on Quotient Space Theory

Yuhong Chi, Fuchun Sun, Weijun Wang, Chunming Yu
Copyright: © 2012 |Volume: 4 |Issue: 2 |Pages: 13
ISSN: 1942-9045|EISSN: 1942-9037|EISBN13: 9781466614208|DOI: 10.4018/jssci.2012040101
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MLA

Chi, Yuhong, et al. "An Improved Particle Swarm Optimization Algorithm Based on Quotient Space Theory." IJSSCI vol.4, no.2 2012: pp.1-13. http://doi.org/10.4018/jssci.2012040101

APA

Chi, Y., Sun, F., Wang, W., & Yu, C. (2012). An Improved Particle Swarm Optimization Algorithm Based on Quotient Space Theory. International Journal of Software Science and Computational Intelligence (IJSSCI), 4(2), 1-13. http://doi.org/10.4018/jssci.2012040101

Chicago

Chi, Yuhong, et al. "An Improved Particle Swarm Optimization Algorithm Based on Quotient Space Theory," International Journal of Software Science and Computational Intelligence (IJSSCI) 4, no.2: 1-13. http://doi.org/10.4018/jssci.2012040101

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Abstract

To control the swarm to fly inside the limited search space and deal with the problems of slow search speed and premature convergence in particle swarm optimization algorithm, the authors applied the theory of topology, and proposed a novel quotient space-based boundary condition named QsaBC by using the properties of quotient space and homeomorphism in this paper. In QsaBC, Search space-zoomed factor and Attractor factor are introduced according to analyzing the dynamic behavior and stability of particles, which not only reduce the subjective interference and enforce the capability of global search, but also enhance the power of local search and escaping from an inferior local optimum. Four CEC’2008 benchmark functions were selected to evaluate the performance of QsaBC. Comparative experiments show that QsaBC can get the satisfactory optimization solution with fast convergence speed. Furthermore, QsaBC is more effective to do with errant particles, easier to calculate and has better robustness than other experienced methods.

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