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A hybrid teaching–learning slime mould algorithm for global optimization and reliability-based design optimization problems

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A Correction to this article was published on 24 August 2022

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Abstract

Slime mould algorithm (SMA) is a novel metaheuristic algorithm with good performance for optimization problems, but it may encounter premature or low accuracy in complex optimization problems. This paper presents a hybrid SMA using teaching–learning based optimization (TLBO), called TLSMA, for solving global optimization and reliability-based design optimization (RBDO) problems. The key point of TLSMA is to combine the capacities of exploration and exploitation from SMA and TLBO, which can enhance the convergence ability of SMA. Moreover, the TLSMA is extended for solving RBDO problems under probabilistic constraints, which are handled by the adaptive chaos control method. The proposed algorithm is tested by a series of experiments with two parts. First, TLSMA is verified by 24 well-known benchmark optimization problems with unimodal and multimodal functions, and is compared with several state-of-the-art metaheuristic algorithms. The results of benchmark optimization problems show that TLSMA outperforms PSO, BBO, GWO, WOA, SSA, TLBO and SMA. Then, TLSMA-RBDO is tested by five RBDO problems, including a numerical and four engineering problems. The results illustrate that the proposed algorithm has high performance in the RBDO problems, which is significantly superior to the compared algorithms.

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Acknowledgements

The supports of the National Key Research and Development Program (Grant No. 2019YFA0706803), the National Natural Science Foundation of China (Grant Nos. 11872142 and 11972143), the Foundation of State Key Laboratory of Structural Analysis for Industrial Equipment from Dalian University of Technology (Grant No. GZ21101) are greatly appreciated. The authors gratefully appreciate for useful comments from editor and reviewers to improve the quality of this paper.

Funding

National Basic Research Program of China (973 Program), 2019YFA0706803, Gang Li, National Natural Science Foundation of China, 11872142, Gang Li, 11972143, Zeng Meng, State Key Laboratory of Structural Analysis for Industrial Equipment, GZ21101, Zeng Meng

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Authors and Affiliations

  1. Department of Engineering Mechanics, State Key Laboratory of Structural Analyses for Industrial Equipment, Dalian University of Technology, Dalian, 116024, China

    Changting Zhong, Gang Li & Zeng Meng

  2. School of Civil Engineering, Hefei University of Technology, Hefei, 230009, China

    Zeng Meng

Authors
  1. Changting Zhong
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  2. Gang Li
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  3. Zeng Meng
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Contributions

CZ: Conceptualization, Methodology, Software, Validation, Writing – original draft, Writing—review & editing. GL: Conceptualization, Methodology, Supervision, Funding acquisition, Writing—review & editing. ZM: Conceptualization, Methodology, Software, Supervision, Funding acquisition, Writing—review & editing.

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Correspondence to Gang Li or Zeng Meng.

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Zhong, C., Li, G. & Meng, Z. A hybrid teaching–learning slime mould algorithm for global optimization and reliability-based design optimization problems. Neural Comput & Applic 34, 16617–16642 (2022). https://doi.org/10.1007/s00521-022-07277-3

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