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

Xiangyong Kong

doi:10.1007/s00500-013-1090-y

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

2013-07-28

PRE

孔祥勇 (Xiangyong Kong) *

Haibin Ouyang

Xiaoxue Piao

DOI：10.1007/s00500-013-1090-y

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Abstract

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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.

Keywords:

Constrained optimization

Prediction of the constraint values

Differential evolution algorithm

Self-adaptive

Grouping