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Dynamic partition search algorithm for global numerical optimization

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Abstract

This paper presents a novel evolutionary algorithm entitled Dynamic Partition Search Algorithm (DPSA) for global optimization problems with continuous variables. The DPSA is a population-based stochastic search algorithm in nature, which mainly consists of initialization process and evolution process. In the initialization process, the DPSA randomly generates an initial population of members from a specific search space and finds a leader. In the evolution process, the DPSA applies two groups to balance exploration ability and exploitation ability, in which one group is in charge of exploring new region via a dynamic partition strategy, and the other group relies on Cauchy distributions to exploit the region around the best member. Later, numerical experiments are conducted for 24 classical benchmark functions with 100, 1000 or even 10000 dimensions. And the performance of the proposed DPSA is compared with a state-of-the-art cooperative coevolving particle swarm optimization (CCPSO2), and two existing differential evolution (DE) algorithms. The experimental results show that DPSA has a better performance than the algorithms used for comparison, especially for high dimensional optimization problems. In addition, the numerical computational results also demonstrate that the DPSA has good scalability, and it is an effective evolutionary algorithm for solving large-scale global optimization problems.

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Acknowledgments

The authors wish to thank the anonymous reviewers, whose valuable comments led to an improved version of the paper. This work was supported by the National Natural Science Foundation of China under Grant Nos. 71271151, 71301114 and 71471126, and the Specialized Research Fund for the Doctoral Program of Higher Education of China under Grant No.20130032110015.

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Authors and Affiliations

  1. Institute of Systems Engineering, Tianjin University, Tianjin, 300072, China

    Gaoji Sun & Ruiqing Zhao

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  1. Gaoji Sun
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  2. Ruiqing Zhao
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Correspondence to Ruiqing Zhao.

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Sun, G., Zhao, R. Dynamic partition search algorithm for global numerical optimization. Appl Intell 41, 1108–1126 (2014). https://doi.org/10.1007/s10489-014-0587-0

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