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A probabilistic simplified sine cosine crow search algorithm for global optimization problems

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Abstract

Crow Search Algorithm (CSA) is a novel meta-heuristic optimizer that is based on the intelligent behavior of crows. There is rather simple with two adjustable parameters only, which in turn makes it very attractive for applications in different engineering areas. To compensate for the blindness of the location update perceived in CSA when being tracked, this paper introduces a probability simplified sine cosine algorithm to form a new hybrid algorithm called PSCCSA (Probabilistic Simplified Sine Cosine Crow Search Algorithm). In 16 well-known standard test functions, the proposed algorithm was compared with 5 meta-heuristic algorithms for evaluating the effectiveness of the algorithms (Crow Search Algorithm, standard Sine Cosine Algorithm, Probability Simplified Sine Cosine Algorithm, Multi-Verse Optimizer and Particle Swarm Optimization). In addition, PSCCSA has also been used to solve four classic engineering problems (pressure vessel design, speed reducer design, welded beam design and tension/compression spring design problem). The results show that the proposed algorithm is feasible and effective.

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Acknowledgements

This work is supported by National Natural Science Foundation of China (Grant No. 11961006) and Guangxi Science and Technology Program (Grant No. 2013ZD022).

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Authors and Affiliations

  1. School of Mathematics and Physics, Guangxi University for Nationalities, Nanning 530006, Guangxi, China

    Yundi Rao & Dengxu He

  2. College of Artificial Intelligence, Guangxi University for Nationalities, Nanning 530006, Guangxi, China

    Liangdong Qu

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  1. Yundi Rao
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  2. Dengxu He
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  3. Liangdong Qu
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Correspondence to Dengxu He.

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Rao, Y., He, D. & Qu, L. A probabilistic simplified sine cosine crow search algorithm for global optimization problems. Engineering with Computers 39, 1823–1841 (2023). https://doi.org/10.1007/s00366-021-01578-2

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