An orthogonal opposition-based-learning Yin–Yang-pair optimization algorithm for engineering optimization

Abstract

Yin–Yang-pair Optimization (YYPO) is a recently developed philosophy-inspired meta-heuristic algorithm, which works with two main points for exploitation and exploration, respectively, and then generates more points via splitting to search the global optimum. However, it suffers from low quality of candidate solutions in its exploration process owing to the lack of elitism. Inspired by this, a new modified algorithm named orthogonal opposition-based-learning Yin–Yang-pair Optimization (OOYO) is proposed to enhance the performance of YYPO. First, the OOYO retains the normalization operation in YYPO and starts with a single point to exploit. A set of opposite points is designed by a method of opposition-based learning with split points generated from the current optimum for exploration. Then, the points, i.e., candidate solutions, are constructed by the randomly selected split point and opposite points through the idea of orthogonal experiment design to make full use of information from the space. The proposed OOYO does not add additional time complexity and eliminates a user-defined parameter in YYPO, which facilitates parameter adjustment. The novel orthogonal opposition-based learning strategy can provide inspirations for the improvement of other optimization algorithms. Extensive test functions containing a classic test suite of 23 standard benchmark functions and 2 test suites of Swarm Intelligence Symposium 2005 and Congress on Evolutionary Computation 2020 from Institute of Electrical and Electronics Engineers are employed to evaluate the proposed algorithm. Non-parametric statistical results demonstrate that OOYO outperforms YYPO and furnishes strong competitiveness compared with other state-of-the-art algorithms. In addition, we apply OOYO to solve four well-known constrained engineering problems and a practical problem of parameters optimization in a rainstorm intensity model.

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Acknowledgements

The authors are grateful to the Henan province university scientific and technological innovation team (No: 18IRTSTHN009), Support of National Natural Science Foundation of China (No: 51509088), Project of key science and technology of the Henan province (202102310259; 202102310588). We gratefully acknowledge the critical comments and corrections of the anonymous reviewers, which improved the presentation of the paper considerably.

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Wang, Wc., Xu, L., Chau, Kw. et al. An orthogonal opposition-based-learning Yin–Yang-pair optimization algorithm for engineering optimization. Engineering with Computers (2021). https://doi.org/10.1007/s00366-020-01248-9

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Keywords

  • Meta-heuristic algorithm
  • Yin–Yang-pair optimization
  • Opposition-based learning
  • Orthogonal experiment design
  • Benchmark functions
  • Engineering optimization