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Complex-valued encoding symbiotic organisms search algorithm for global optimization

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Abstract

Symbiotic organisms search algorithm is a new meta-heuristic algorithm based on the symbiotic relationship between the biological which was proposed in recent years. In this paper, a novel complex-valued encoding symbiotic organisms search (CSOS) algorithm is proposed. The algorithm introduces the idea of complex coding diploid. Each individual is composed of real and imaginary parts and extends the search space from one dimension to two dimensions. This increases the diversity of the population, further enhances the ability of the algorithm to find the global optimal value, and improves the precision of the algorithm. CSOS has been tested with 23 standard benchmark functions and 2 engineering design problems. The results show that CSOS has better ability of finding global optimal value and higher precision.

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Acknowledgements

This work is supported by National Science Foundation of China under Grant Nos. 61463007, 61563008. Project of Guangxi University for Nationalities Science Foundation under Grant No. 2016GXNSFAA380264.

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Authors and Affiliations

  1. College of Information Science and Engineering, Guangxi University for Nationalities, Nanning, 530006, China

    Fahui Miao, Yongquan Zhou & Qifang Luo

  2. Key Laboratories of Guangxi High Schools Complex System and Computational Intelligence, Nanning, 530006, Guangxi, China

    Yongquan Zhou & Qifang Luo

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  1. Fahui Miao
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  2. Yongquan Zhou
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  3. Qifang Luo
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Correspondence to Yongquan Zhou.

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Miao, F., Zhou, Y. & Luo, Q. Complex-valued encoding symbiotic organisms search algorithm for global optimization. Knowl Inf Syst 58, 209–248 (2019). https://doi.org/10.1007/s10115-018-1158-1

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