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Comprehensive learning gravitational search algorithm for global optimization of multimodal functions

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Abstract

In this paper, a new comprehensive learning gravitational search algorithm (CLGSA) is proposed to enhance the performance of basic GSA. The proposed algorithm is a new kind of intelligent optimization algorithm which has better ability to choose good elements. An intensive comprehensive learning methodology is proposed to enrich the optimization ability of the GSA. The efficiency of the proposed algorithm was evaluated by 28 benchmark functions which have been proposed in IEEE-CEC 2013 sessions. The results are compared with eight state-of-the-art algorithms IPOP, BIPOP, NIPOP, NBIPOP, DE/rand, SPSRDEMMS, SPSO-2011 and GSA. A variety of ways are considered to examine the ability of the proposed technique in terms of convergence ability, success rate and statistical behavior of algorithm over dimensions 10, 30 and 50. Apart from experimental studies, theoretical stability of the proposed CLGSA is also proved. It was concluded that the proposed algorithm performed efficiently with good results.

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Acknowledgement

This work was supported by the National Institute of Technology Jalandhar, India. We would like to express our gratitude toward the unknown potential reviewers who have agreed to review this article and who have provided valuable suggestions to improve the quality of the article.

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Authors and Affiliations

  1. Department of Science and Humanities, Northcap University Gurgaon, Gurgaon, Haryana, 122017, India

    Indu Bala

  2. Department of Mathematics, Dr. BR Ambedkar National Institute of Technology Jalandhar, Jalandhar, 144011, India

    Anupam Yadav

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  1. Indu Bala
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  2. Anupam Yadav
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Correspondence to Anupam Yadav.

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Bala, I., Yadav, A. Comprehensive learning gravitational search algorithm for global optimization of multimodal functions. Neural Comput & Applic 32, 7347–7382 (2020). https://doi.org/10.1007/s00521-019-04250-5

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