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A prediction-based adaptive grouping differential evolution algorithm for constrained numerical optimization

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Abstract

In this paper, a new adaptive grouping differential evolution (AGDE) algorithm is proposed to improve the optimization performance by implementing a prediction strategy of the constraints for constrained optimization problems. It is unnecessary to calculate the constraint values when dealing with the constraints in this method. The constraints are handled after a simple prediction according to the Lipschitz condition. When the constraints are very complex, the load arisen from the calculation of the constraint values is reduced dramatically and the feasibility of the solutions remains with great probability. In AGDE algorithm, the population is dynamically grouped to three subpopulations with respective newly-designed mutation strategy. Meanwhile, the mutation factor and crossover probability are adopted associated with the evolutionary process according to the information of the entire population. Both of the above improvements not only increase the diversity of population and speed up the convergence, but also reduce the complexity of the parameter selection. Four sets of comparative experiments are carried out to evaluate the feasibility and effectiveness of the proposed method that deals with the constraints.

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Authors and Affiliations

  1. School of Information Science and Engineering, Northeastern University, Shenyang, 110819, China

    Xiangyong Kong & Haibin Ouyang

  2. School of Computing, Queen’s University, Kingston, ON, K7L 3N6, Canada

    Xiaoxue Piao

Authors
  1. Xiangyong Kong
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  2. Haibin Ouyang
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  3. Xiaoxue Piao
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Correspondence to Xiangyong Kong.

Additional information

Communicated by G. Acampora.

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Kong, X., Ouyang, H. & Piao, X. A prediction-based adaptive grouping differential evolution algorithm for constrained numerical optimization. Soft Comput 17, 2293–2309 (2013). https://doi.org/10.1007/s00500-013-1090-y

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