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Multi-population co-genetic algorithm with double chain-like agents structure for parallel global numerical optimization

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Abstract

For the low optimization precision and long optimization time of genetic algorithm, this paper proposed a multi-population agent co-genetic algorithm with chain-like agent structure (MPAGA). This algorithm adopted multi-population parallel searching mode, close chain-like agent structure, cycle chain-like agent structure, dynamic neighborhood competition and orthogonal crossover strategy to realize parallel optimization, and has the characteristics of high optimization precision and short optimization time. In order to verify the optimization precision of this algorithm, some popular benchmark test functions were used for comparing this algorithm and a popular agent genetic algorithm (MAGA). The experimental results show that MPAGA has higher optimization precision and shorter optimization time than MAGA.

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Authors and Affiliations

  1. College of Communication Engineering, Chongqing University, Chongqing, 400030, China

    Yongming Li & Xiaoping Zeng

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  1. Yongming Li
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  2. Xiaoping Zeng
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Correspondence to Yongming Li.

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Li, Y., Zeng, X. Multi-population co-genetic algorithm with double chain-like agents structure for parallel global numerical optimization. Appl Intell 32, 292–310 (2010). https://doi.org/10.1007/s10489-008-0146-7

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  • DOI: https://doi.org/10.1007/s10489-008-0146-7

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