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Quotient Space-Based Boundary Condition for Particle Swarm Optimization Algorithm

Quotient Space-Based Boundary Condition for Particle Swarm Optimization Algorithm

Yuhong Chi, Fuchun Sun, Langfan Jiang, Chunyang Yu, Chunli Chen
Copyright: © 2011 |Volume: 3 |Issue: 1 |Pages: 12
ISSN: 1942-9045|EISSN: 1942-9037|EISBN13: 9781613509173|DOI: 10.4018/jssci.2011010106
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MLA

Chi, Yuhong, et al. "Quotient Space-Based Boundary Condition for Particle Swarm Optimization Algorithm." IJSSCI vol.3, no.1 2011: pp.78-89. http://doi.org/10.4018/jssci.2011010106

APA

Chi, Y., Sun, F., Jiang, L., Yu, C., & Chen, C. (2011). Quotient Space-Based Boundary Condition for Particle Swarm Optimization Algorithm. International Journal of Software Science and Computational Intelligence (IJSSCI), 3(1), 78-89. http://doi.org/10.4018/jssci.2011010106

Chicago

Chi, Yuhong, et al. "Quotient Space-Based Boundary Condition for Particle Swarm Optimization Algorithm," International Journal of Software Science and Computational Intelligence (IJSSCI) 3, no.1: 78-89. http://doi.org/10.4018/jssci.2011010106

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Abstract

To control particles to fly inside the limited search space and deal with the problems of slow search speed and premature convergence of particle swarm optimization algorithm, this paper applies the theory of topology, and proposed a quotient space-based boundary condition named QsaBC by using the properties of quotient space and homeomorphism. In QsaBC, Search space-zoomed factor and Attractor are introduced according to 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 are selected to evaluate the performance of QsaBC. Comparative experiments show that QsaBC can achieve the satisfactory optimization solution with fast convergence speed. Furthermore, QsaBC is more effective with errant particles, and has easier calculation and better robustness than other methods.

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