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Volume 9 Issue 7
Jul.  2022

IEEE/CAA Journal of Automatica Sinica

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J. Bi, H. T. Yuan, J. H. Zhai, M. C. Zhou, and H. V. Poor, “Self-adaptive bat algorithm with genetic operations,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 7, pp. 1284–1294, Jul. 2022. doi: 10.1109/JAS.2022.105695
Citation: J. Bi, H. T. Yuan, J. H. Zhai, M. C. Zhou, and H. V. Poor, “Self-adaptive bat algorithm with genetic operations,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 7, pp. 1284–1294, Jul. 2022. doi: 10.1109/JAS.2022.105695

Self-adaptive Bat Algorithm With Genetic Operations

doi: 10.1109/JAS.2022.105695
Funds:  This work was supported in part by the Fundamental Research Funds for the Central Universities (YWF-22-L-1203), the National Natural Science Foundation of China (62173013, 62073005), the National Key Research and Development Program of China (2020YFB1712203), and U.S. National Science Foundation (CCF-0939370, CCF-1908308)
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  • Swarm intelligence in a bat algorithm (BA) provides social learning. Genetic operations for reproducing individuals in a genetic algorithm (GA) offer global search ability in solving complex optimization problems. Their integration provides an opportunity for improved search performance. However, existing studies adopt only one genetic operation of GA, or design hybrid algorithms that divide the overall population into multiple subpopulations that evolve in parallel with limited interactions only. Differing from them, this work proposes an improved self-adaptive bat algorithm with genetic operations (SBAGO) where GA and BA are combined in a highly integrated way. Specifically, SBAGO performs their genetic operations of GA on previous search information of BA solutions to produce new exemplars that are of high-diversity and high-quality. Guided by these exemplars, SBAGO improves both BA’s efficiency and global search capability. We evaluate this approach by using 29 widely-adopted problems from four test suites. SBAGO is also evaluated by a real-life optimization problem in mobile edge computing systems. Experimental results show that SBAGO outperforms its widely-used and recently proposed peers in terms of effectiveness, search accuracy, local optima avoidance, and robustness.

     

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    Highlights

    • This work designs a novel self-adaptive bat algorithm with genetic operations (SBAGO)
    • SBAGO performs genetic operations on BA solutions to produce high-quality exemplars
    • Guided by exemplars, SBAGO improves both BA’s efficiency and global search capability
    • It is evaluated with 29 common problems, and a real-life problem in edge computing
    • SBAGO outperforms recent peers in terms of search accuracy and robustness

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