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Cooperatively Coevolving Particle Swarms for Large Scale Optimization

IEEE transactions on evolutionary computation, 2012-04, Vol.16 (2), p.210-224 [Peer Reviewed Journal]

ISSN: 1089-778X ;EISSN: 1941-0026 ;DOI: 10.1109/TEVC.2011.2112662 ;CODEN: ITEVF5

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  • Title:
    Cooperatively Coevolving Particle Swarms for Large Scale Optimization
  • Author: Xiaodong Li ; Xin Yao
  • Subjects: Algorithm design and analysis ; Cooperative coevolution ; evolutionary algorithms ; Gaussian distribution ; Heuristic algorithms ; large-scale optimization ; Optimization ; Particle swarm optimization ; Shape ; swarm intelligence ; Topology
  • Is Part Of: IEEE transactions on evolutionary computation, 2012-04, Vol.16 (2), p.210-224
  • Description: This paper presents a new cooperative coevolving particle swarm optimization (CCPSO) algorithm in an attempt to address the issue of scaling up particle swarm optimization (PSO) algorithms in solving large-scale optimization problems (up to 2000 real-valued variables). The proposed CCPSO2 builds on the success of an early CCPSO that employs an effective variable grouping technique random grouping. CCPSO2 adopts a new PSO position update rule that relies on Cauchy and Gaussian distributions to sample new points in the search space, and a scheme to dynamically determine the coevolving subcomponent sizes of the variables. On high-dimensional problems (ranging from 100 to 2000 variables), the performance of CCPSO2 compared favorably against a state-of-the-art evolutionary algorithm sep-CMA-ES, two existing PSO algorithms, and a cooperative coevolving differential evolution algorithm. In particular, CCPSO2 performed significantly better than sep-CMA-ES and two existing PSO algorithms on more complex multimodal problems (which more closely resemble real-world problems), though not as well as the existing algorithms on unimodal functions. Our experimental results and analysis suggest that CCPSO2 is a highly competitive optimization algorithm for solving large-scale and complex multimodal optimization problems.
  • Publisher: IEEE
  • Language: English
  • Identifier: ISSN: 1089-778X
    EISSN: 1941-0026
    DOI: 10.1109/TEVC.2011.2112662
    CODEN: ITEVF5
  • Source: IEEE Open Access Journals
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