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Chinese Journal of Mechanical Engineering

, Volume 30, Issue 3, pp 553–565

First Online: 24 April 2017Received: 04 May 2016Revised: 10 February 2017Accepted: 02 April 2017DOI: 10.1007-s10033-017-0130-4

Cite this article as: FU, C., XU, Y., JIANG, C. et al. Chin. J. Mech. Eng. 2017 30: 553. doi:10.1007-s10033-017-0130-4


Most of the current evolutionary algorithms for constrained optimization algorithm are low computational efficiency. In order to improve efficiency, an improved differential evolution with shrinking space technique and adaptive trade-off model, named ATMDE, is proposed to solve constrained optimization problems. The proposed ATMDE algorithm employs an improved differential evolution as the search optimizer to generate new offspring individuals into evolutionary population. For the constraints, the adaptive trade-off model as one of the most important constraint-handling techniques is employed to select better individuals to retain into the next population, which could effectively handle multiple constraints. Then the shrinking space technique is designed to shrink the search region according to feedback information in order to improve computational efficiency without losing accuracy. The improved DE algorithm introduces three different mutant strategies to generate different offspring into evolutionary population. Moreover, a new mutant strategy called -DE-rand-best-1- is constructed to generate new individuals according to the feasibility proportion of current population. Finally, the effectiveness of the proposed method is verified by a suite of benchmark functions and practical engineering problems. This research presents a constrained evolutionary algorithm with high efficiency and accuracy for constrained optimization problems.

KeywordsConstrained optimization Differential evolution Adaptive trade-off model Shrinking space technique Supported by National Science Foundation for Excellent Young Scholars, China Grant No. 51222502, Funds for Distinguished Young Scientists of Hunan Province, China Grant No. 14JJ1016, and Major Program of National Natural Science Foundation of China Grant No. 51490662.

Autor: Chunming FU - Yadong XU - Chao JIANG - Xu HAN - Zhiliang HUANG


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