Cooperatively Coevolving Particle Swarms for Large Scale Optimization
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...
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| Published in | IEEE transactions on evolutionary computation Vol. 16; no. 2; pp. 210 - 224 |
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| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
New York, NY
IEEE
01.04.2012
Institute of Electrical and Electronics Engineers |
| Subjects | |
| Online Access | Get full text |
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| Abstract | 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. |
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| AbstractList | 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. |
| Author | Xiaodong Li Xin Yao |
| Author_xml | – sequence: 1 surname: Xiaodong Li fullname: Xiaodong Li email: xiaodong.li@rmit.edu.au organization: Sch. of Comput. Sci. & Inf. Technol., R. Melbourne Inst. of Technol., Melbourne, VIC, Australia – sequence: 2 surname: Xin Yao fullname: Xin Yao email: x.yao@cs.bham.ac.uk organization: Center of Excellence for Res. in Comput. Intell. & Applic., Univ. of Birmingham, Birmingham, UK |
| BackLink | http://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=25796679$$DView record in Pascal Francis |
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| Cites_doi | 10.1109/TEVC.2004.826069 10.1109/MHS.1995.494215 10.1109/CEC.2002.1004493 10.1023/A:1021956306041 10.1109/SIS.2003.1202251 10.1016/0303-2647(96)01621-8 10.1109/CEC.2009.4983126 10.1109/CEC.2010.5586127 10.1109/4235.771163 10.1109/CEC.2009.4983052 10.1109/CEC.2001.934314 10.1109/TEVC.2003.816583 10.1145/1143997.1144118 10.1162/106365601750190398 10.1115/1.1737780 10.1109/SIS.2007.368035 10.1109/CEC.2002.1006270 10.1109/CEC.2005.1554902 10.2514/2.764 10.1145/1830761.1830790 10.1109/CEC.2008.4631320 10.1016/j.ijheatmasstransfer.2005.08.032 10.1109/4235.985692 10.1016/j.ins.2008.02.017 10.1109/SIS.2003.1202268 |
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| SubjectTerms | Algorithm design and analysis Algorithmics. Computability. Computer arithmetics Applied sciences Artificial intelligence Computer science; control theory; systems Cooperative coevolution evolutionary algorithms Exact sciences and technology Gaussian distribution Heuristic algorithms large-scale optimization Mathematical programming Operational research and scientific management Operational research. Management science Optimization Particle swarm optimization Shape swarm intelligence Theoretical computing Topology |
| Title | Cooperatively Coevolving Particle Swarms for Large Scale Optimization |
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