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24-04-2017 | Original Article | Issue 3/2017 Open Access

Chinese Journal of Mechanical Engineering 3/2017

Improved Differential Evolution with Shrinking Space Technique for Constrained Optimization

Journal:
Chinese Journal of Mechanical Engineering > Issue 3/2017
Authors:
Chunming FU, Yadong XU, Chao JIANG, Xu HAN, Zhiliang HUANG
Important notes
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).

Abstract

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.

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