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An improved atom search optimization for optimization tasks

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Abstract

Atom search optimization (ASO) is a newly developed metaheuristic algorithm inspired by molecular basis dynamics. The paramount challenge in ASO is that it is easily trapped into the local optima and premature convergence. To address these issues, this paper presented an improved atom search optimization with three strategies, global topology with secant factor, non-linear inertia weight and update learning. First, the global topology provides the best solution for each individual and enriches the information exchange of the population, and prevents premature convergence under the effect of the secant factor. Second, smooth properties of non-linear inertia weights are introduced to balance exploration and exploitation. Third, update learning provides more opportunities to jump out of the local optima. Thus, these three strategies are used to improve the performance of ASO, which is called GNUASO. Finally, the proposed GNUASO algorithm was evaluated in the CEC2017 benchmark functions and two real-world engineering problems, compared with some excellent algorithms to confirm the performance of the proposed GNUASO. Experimental results and statistical analysis show that the proposed GNUASO algorithm outperforms the other selected algorithms in CEC2017 benchmark functions and engineering design problems.

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Acknowledgements

This work is supported by National Science Foundation of China under Grant No. 61473054. The authors also gratefully acknowledge the helpful comments and suggestions of the reviewers, which have improved the presentation.

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  1. School of Electronic and Information Engineering, University of Science and Technology Liaoning, Anshan, 114051, People’s Republic of China

    Jie Bi & Yong Zhang

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  1. Jie Bi
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Jie Bi participated in the draft writing and critical revision of this paper and participated in the data collection, analysis, and algorithm simulation. Yong Zhang participated in the concept, design, and interpretation of results and commented on the manuscript;

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Correspondence to Yong Zhang.

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Bi, J., Zhang, Y. An improved atom search optimization for optimization tasks. Multimed Tools Appl 82, 6375–6429 (2023). https://doi.org/10.1007/s11042-022-13171-w

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