Please use this identifier to cite or link to this item: http://buratest.brunel.ac.uk/handle/2438/5821
Title: A clustering particle swarm optimizer for dynamic optimization
Authors: Li, C
Yang, S
Keywords: Particle swarm optimisation;Pattern clustering;Search problems
Issue Date: 2009
Publisher: IEEE
Citation: In Proceedings of the IEEE Congress on Evolutionary Computation, 2009 (CEC '09), Trondheim: 439 - 446, 2009-05-18 - 2009-05-21
Abstract: In the real world, many applications are nonstationary optimization problems. This requires that optimization algorithms need to not only find the global optimal solution but also track the trajectory of the changing global best solution in a dynamic environment. To achieve this, this paper proposes a clustering particle swarm optimizer (CPSO) for dynamic optimization problems. The algorithm employs hierarchical clustering method to track multiple peaks based on a nearest neighbor search strategy. A fast local search method is also proposed to find the near optimal solutions in a local promising region in the search space. Six test problems generated from a generalized dynamic benchmark generator (GDBG) are used to test the performance of the proposed algorithm. The numerical experimental results show the efficiency of the proposed algorithm for locating and tracking multiple optima in dynamic environments.
Description: This article is posted here with permission of the IEEE - Copyright @ 2009 IEEE
URI: http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=4982979&tag=1
http://bura.brunel.ac.uk/handle/2438/5821
DOI: http://dx.doi.org/10.1109/CEC.2009.4982979
ISBN: 978-1-4244-2958-5
Appears in Collections:Publications
Computer Science
Dept of Computer Science Research Papers

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