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A hybrid learning-based genetic and grey-wolf optimizer for global optimization

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Abstract

The grey-wolf optimizer (GWO) is a comparatively recent and competent algorithm in Swarm Intelligence (SI) to solve numerical and real-world optimization problems. However, the biggest challenge is the quick stabilization of its search agents to the local optima. Therefore, to bring effectiveness in the global search, it is imperative to relocate the leading agents through the procreation of their positions in the search space. This paper proposes GL-GWO, a genetic learning (GL)-based GWO, which imitates the genetic offspring generation scheme to improve the intelligence of GWO’s leading agents. The GL scheme expedites the global effectiveness of leading agents by constructing the exemplars for them through genetic operators using their historical information. The obtained exemplars are well diversified and highly intelligent; therefore, the rest of the population’s global searchability and search efficiency are enhanced under their guidance. The GL-GWO is tested on widely adopted 20 benchmark functions from the IEEE-CEC-2005 dataset and 38 functions from the IEEE-CEC-2014 dataset. The efficacy of GL-GWO is tested on four real-world engineering problems, namely recommendation systems, face image super-resolution, tension/compression spring, and welded beam. The obtained results on benchmark functions and considered engineering problems conclude that the GL-GWO is an efficient, effective, and reliable algorithm for solving real-world optimization problems.

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Data Availability

Enquiries about data availability should be directed to the authors.

Notes

  1. r-1 refers to the algorithm that obtains the best values on the maximum number of performance measures.

  2. r-2 refers to the algorithm that shows the best values on most of the performance measures after r-1.

  3. It is a function that accumulates all the error terms induced during the representation of LR position-patches through the weighted linear combination of their respective LR training samples.

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Authors and Affiliations

  1. Department of Artificial Intelligence and Data Sciences, Indira Gandhi Delhi Technical University for Women, Delhi, 110006, India

    Ankush Jain

  2. School of Computer Science Engineering and Technology, Bennett University, Greater Noida, Uttar Pradesh, 201310, India

    Surendra Nagar

  3. Department of Computer Science and Engineering, ABV- Indian Institute of Information Technology and Management, Gwalior, Madhya Pradesh, 474015, India

    Pramod Kumar Singh

  4. Department of Applied Sciences, ABV- Indian Institute of Information Technology and Management, Gwalior, Madhya Pradesh, 474015, India

    Joydip Dhar

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  1. Ankush Jain
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  2. Surendra Nagar
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Contributions

AJ: Conceptualization, Data curation, Formal analysis, Methodology, Validation, Visualization, and Paper writing. SN: Formal analysis and Paper writing. PKS: Conceptualization, Formal analysis, and Supervision JD: Conceptualization, Formal analysis, and Supervision.

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A Test function definitions and their respective results for different competitive algorithms

A Test function definitions and their respective results for different competitive algorithms

Table 10 Benchmark test functions of IEEE-CEC-2005 dataset
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Table 11 Benchmark test functions of IEEE-CEC-2014 dataset
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Table 12 Results obtained on benchmark functions of the IEEE-CEC-2005 dataset
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Table 13 Results obtained on benchmark functions of the IEEE-CEC-2014 dataset
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Jain, A., Nagar, S., Singh, P.K. et al. A hybrid learning-based genetic and grey-wolf optimizer for global optimization. Soft Comput 27, 4713–4759 (2023). https://doi.org/10.1007/s00500-022-07604-9

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