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A novel complex-valued bat algorithm

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Abstract

Bat algorithm is a recent optimization algorithm with quick convergence, but its population diversity can be limited in some applications. This paper presents a new bat algorithm based on complex-valued encoding where the real part and the imaginary part will be updated separately. This approach can increase the diversity of the population and expands the dimensions for denoting. The simulation results of fourteen benchmark test functions show that the proposed algorithm is effective and feasible. Compared to the real-valued bat algorithm or particle swarm optimization, the proposed algorithm can get high precision and can almost reach the theoretical value.

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Acknowledgments

This work is supported by National Science Foundation of China under Grant No. 61165015. Key Project of Guangxi Science Foundation under Grant No. 2012GXNSFDA053028, Key Project of Guangxi High School Science Foundation under Grant Nos. 20121ZD008, 201203YB072, funded by Open Research Fund Program of Key Lab of Intelligent Perception and Image Understanding of Ministry of Education of China under Grant No. IPIU01201100 and the Innovation Project of Guangxi Graduate Education under Grant No. gxun-chx2012103.

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Authors and Affiliations

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

    Liangliang Li & Yongquan Zhou

  2. Guangxi Key Laboratory of Hybrid Computation and Integrated Circuit Design Analysis, Nanning, 530006, Guangxi, China

    Yongquan Zhou

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  1. Liangliang Li
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  2. Yongquan Zhou
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Correspondence to Yongquan Zhou.

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Li, L., Zhou, Y. A novel complex-valued bat algorithm. Neural Comput & Applic 25, 1369–1381 (2014). https://doi.org/10.1007/s00521-014-1624-y

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  • DOI: https://doi.org/10.1007/s00521-014-1624-y

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