Cooperatively Coevolving Particle Swarms for Large Scale Optimization

• Published in: IEEE transactions on evolutionary computation Vol. 16; no. 2; pp. 210 - 224
• Main Authors: Xiaodong Li, Xin Yao
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.04.2012; Institute of Electrical and Electronics Engineers
• Subjects: 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; Unimodality; Scalability; Evolutionary algorithm; Multiobjective programming; evolutionary algorithms; Gaussian distribution; Updating; Competitive algorithms; Multiple solution; Multidimensional analysis; large-scale optimization; Cooperation; Ecological niche; Swarm intelligence; Random variable; Divide and conquer method; Mathematical programming; Large scale system; Grouping; Coevolution; Blind equalization; Cauchy distribution; Particle swarm optimization; Cooperative coevolution; Large scale; Artificial intelligence; Parallelization
• ISSN: 1089-778X; 1941-0026
• DOI: 10.1109/TEVC.2011.2112662

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.