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A hybrid whale optimization algorithm with artificial bee colony

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Abstract

In this paper, the defects and deficiencies of the recently proposed whale optimization algorithm (WOA) are improved. A whale optimization algorithm mixed with an artificial bee colony (ACWOA) is proposed to solve the WOA problems of slow convergence, low precision, and easy to fall into local optimum. The ACWOA algorithm integrates the artificial bee colony algorithm and chaotic mapping, effectively avoiding the local optimal situation and improving the quality of the initial solution. Also, nonlinear convergence factors and adaptive inertia weight coefficients are added to accelerate the convergence rate. To verify the performance of the improved algorithm, 20 benchmark functions and CEC2019 multimodal multi-objective benchmark functions have been used to compare ACWOA with the classical intelligent population algorithms (PSO, MVO, and GWO) and the recent state-of-the-art algorithms (CWOA, HWPSO, and HIWOA) in recent years. The proposed algorithm is applied to two well-known engineering mathematical models and a real application (the quality process control). The experiments show that the ACWOA algorithm has strong competitiveness in convergence speed and solution accuracy and has certain practical value in complex mathematical model scenarios.

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Acknowledgements

This work is supported by the National Natural Science Foundation of China under Grant U1813205, Independent Research Project of State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body 71765003, and Hunan Key Laboratory of Intelligent Robot Technology in Electronic Manufacturing Open Foundation Grant 2017TP1011.

Funding

Funding was provided by Innovative Research Group Project of the National Natural Science Foundation of China (U1813205).

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Authors and Affiliations

  1. College of Electrical and Information Engineering, Hunan University, Changsha, 410082, China

    Chenjun Tang, Wei Sun, Min Xue, Xing Zhang, Hongwei Tang & Wei Wu

  2. Hunan Provincial Key Laboratory of Intelligent Robot Technology in Electronic Manufacturing, Hunan University, Changsha, 410082, China

    Chenjun Tang, Wei Sun, Min Xue, Xing Zhang, Hongwei Tang & Wei Wu

Authors
  1. Chenjun Tang
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  2. Wei Sun
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  3. Min Xue
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  4. Xing Zhang
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  5. Hongwei Tang
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  6. Wei Wu
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Correspondence to Wei Sun.

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Tang, C., Sun, W., Xue, M. et al. A hybrid whale optimization algorithm with artificial bee colony. Soft Comput 26, 2075–2097 (2022). https://doi.org/10.1007/s00500-021-06623-2

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