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An improved hybrid grey wolf optimization algorithm

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Abstract

The existing grey wolf optimization algorithm has some disadvantages, such as slow convergence speed, low precision and so on. So this paper proposes a grey wolf optimization algorithm combined with particle swarm optimization (PSO_GWO). In this new algorithm, the Tent chaotic sequence is used to initiate the individuals’ position, which can increase the diversity of the wolf pack. And the nonlinear control parameter is used to balance the global search and local search ability of the algorithm and improve the convergence speed of the algorithm. At the same time, the idea of PSO is introduced, which utilize the best value of the individual and the best value of the wolf pack to update the position information of each grey wolf. This method preserves the best position information of the individual and avoids the algorithm falling into a local optimum. To verify the performance of this algorithm, the proposed method is tested on 18 benchmark functions and compared with some other improved algorithms. The simulation results show that the proposed algorithm can better search global optimal solution and better robustness than other algorithm.

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Acknowledgements

The authors would like to thank the anonymous reviewers for their valuable comments and suggestions to improve this paper. Besides, this work is supported by the National Natural Science Foundation of China (No. 51277023), National Natural Science Foundation Youth Science Foundation Project (No. 61501107), “13th Five-Year” Scientific Research Planning Project of Jilin Province Department of Education (No. JJKH20180439KJ).

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Authors and Affiliations

  1. Department of Information Engineering, Northeast Electric Power University, Jilin City, 132012, Jilin Province, China

    Zhi-jun Teng, Jin-ling Lv & Li-wen Guo

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  1. Zhi-jun Teng
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  2. Jin-ling Lv
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  3. Li-wen Guo
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Correspondence to Jin-ling Lv.

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Communicated by V. Loia.

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Teng, Zj., Lv, Jl. & Guo, Lw. An improved hybrid grey wolf optimization algorithm. Soft Comput 23, 6617–6631 (2019). https://doi.org/10.1007/s00500-018-3310-y

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