Levy-based antlion-inspired optimizers with orthogonal learning scheme

Abstract

Antlion optimization (ALO) is an efficient metaheuristic paradigm that imitates antlion’s foraging behavior when they search for the ants. However, the conventional variant appears to encounter difficulties in avoiding local optima stagnation and slow convergence speed in dealing with complex problems. Hence, there are problems in the performance that need to be mitigated. To alleviate these shortcomings, an improved variant called Lévy orthogonal learning ALO is developed, which enhances the efficacy of the core method with orthogonal learning strategy, Levy flight, and primary core mechanisms. To measure the effectiveness of the new method, it is compared with the basic version, variant called Levy flight ALO, and variant called orthogonal learning ALO using thirty benchmark functions from IEEE CEC 2017. Also, it is compared with 15 well-known metaheuristic algorithms. Empirical results have shown the superiority of the proposed algorithm in solving the majority of test functions in terms of solution quality and convergence speed. To further validate the efficacy of the enhanced algorithm, it is applied to common practical engineering problems with constrained and unknown search spaces. The obtained results vividly demonstrate that the proposed algorithm provides satisfactory results for solving these problems.

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Acknowledgements

This research was financially supported by the Natural Science Foundation of China (61702376), the Zhejiang Provincial Natural Science Foundation of China (LSZ19F020001), and was partially supported by Science and Technology Plan Project of Wenzhou (2018ZG012), Wenzhou Major Scientific and Technological Innovation Project (ZY2019019).

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Correspondence to Hui Huang or Huiling Chen or Xueding Cai.

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Ba, A.F., Huang, H., Wang, M. et al. Levy-based antlion-inspired optimizers with orthogonal learning scheme. Engineering with Computers (2020). https://doi.org/10.1007/s00366-020-01042-7

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Keywords

  • Antlion optimization algorithm
  • Levy flight
  • Global optimization
  • Engineering design