Please use this identifier to cite or link to this item: http://buratest.brunel.ac.uk/handle/2438/5882
Title: Fast multi-swarm optimization for dynamic optimization problems
Authors: Li, C
Yang, S
Keywords: Application software;Benchmark testing;Computer science;Convergence;Equations;Evolutionary computation;Particle swarm optimization;Particle tracking;Search methods;Trajectory
Issue Date: 2008
Publisher: IEEE
Citation: 4th International Conference on Natural Computation, Jinan, 7: 624 - 628, 18 -20 Oct 2008
Abstract: In the real world, many applications are non-stationary optimization problems. This requires that the 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 multi-swarm algorithm based on fast particle swarm optimization for dynamic optimization problems. The algorithm employs a mechanism to track multiple peaks by preventing overcrowding at a peak and a fast particle swarm optimization algorithm as a local search method to find the near optimal solutions in a local promising region in the search space. The moving peaks benchmark function is used to test the performance of the proposed algorithm. The numerical experimental results show the efficiency of the proposed algorithm for dynamic optimization problems.
Description: This article is posted here with permission of IEEE - Copyright @ 2008 IEEE
URI: http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=4668051&tag=1
http://bura.brunel.ac.uk/handle/2438/5882
DOI: http://dx.doi.org/10.1109/ICNC.2008.313
ISBN: 978-0-7695-3304-9
Appears in Collections:Publications
Computer Science
Dept of Computer Science Research Papers

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