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Adaptive Barebones Salp Swarm Algorithm with Quasi-oppositional Learning for Medical Diagnosis Systems: A Comprehensive Analysis

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Abstract

The Salp Swarm Algorithm (SSA) may have trouble in dropping into stagnation as a kind of swarm intelligence method. This paper developed an adaptive barebones salp swarm algorithm with quasi-oppositional-based learning to compensate for the above weakness called QBSSA. In the proposed QBSSA, an adaptive barebones strategy can help to reach both accurate convergence speed and high solution quality; quasi-oppositional-based learning can make the population away from traping into local optimal and expand the search space. To estimate the performance of the presented method, a series of tests are performed. Firstly, CEC 2017 benchmark test suit is used to test the ability to solve the high dimensional and multimodal problems; then, based on QBSSA, an improved Kernel Extreme Learning Machine (KELM) model, named QBSSA–KELM, is built to handle medical disease diagnosis problems. All the test results and discussions state clearly that the QBSSA is superior to and very competitive to all the compared algorithms on both convergence speed and solutions accuracy.

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Notes

  1. https://aliasgharheidari.com/HHO.html.

  2. https://aliasgharheidari.com/SMA.html.

  3. https://aliasgharheidari.com/HGS.html.

  4. https://aliasgharheidari.com/RUN.html.

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Acknowledgements

This research is supported by the National Natural Science Foundation of China (62076185, U1809209). This research is also supported by Zhejiang Provincial Natural Science Foundation of China (LY21F020030), Wenzhou Major Scientific and Technological Innovation Project (ZY2019019), Wenzhou Science and Technology Bureau (2018ZG016). We thank Ali Asghar Heidari (https://aliasgharheidari.com) for his help in the preparation of this paper.

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Authors and Affiliations

  1. Department of General Surgery, The Second Affiliated Hospital of Shanghai University (Wenzhou Central Hospital), Wenzhou, 325000, Zhejiang, People’s Republic of China

    Jianfu Xia & Rizeng Li

  2. Soochow University, Suzhou, Jiangsu, People’s Republic of China

    Jianfu Xia

  3. Department of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, 325035, People’s Republic of China

    Hongliang Zhang, Zhennao Cai & Huiling Chen

  4. School of Artificial Intelligence, Jilin International Studies University, Changchun, 130000, People’s Republic of China

    Zhiyan Wang

  5. Wenzhou Polytechnic, Wenzhou, 325035, People’s Republic of China

    Zhiyang Gu

  6. The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, People’s Republic of China

    Zhifang Pan

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Xia, J., Zhang, H., Li, R. et al. Adaptive Barebones Salp Swarm Algorithm with Quasi-oppositional Learning for Medical Diagnosis Systems: A Comprehensive Analysis. J Bionic Eng 19, 240–256 (2022). https://doi.org/10.1007/s42235-021-00114-8

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