Open AccessAn Adaptive Differential Evolution Algorithm to Solve Constrained Optimization Problems in Engineering Design
Author(s) -
Youyun Ao,
Hongmei Chi
Publication year - 2010
Publication title -
engineering
Language(s) - English
Resource type - Journals
eISSN - 1947-3931
pISSN - 1947-394X
DOI - 10.4236/eng.2010.21009
Subject(s) - crossover , benchmark (surveying) , differential evolution , mathematical optimization , evolutionary algorithm , computer science , set (abstract data type) , operator (biology) , boundary (topology) , optimization problem , engineering optimization , meta optimization , algorithm , mutation , mathematics , artificial intelligence , mathematical analysis , biochemistry , chemistry , geodesy , repressor , transcription factor , gene , programming language , geography
Differential evolution (DE) algorithm has been shown to be a simple and efficient evolutionary algorithm for global optimization over continuous spaces, and has been widely used in both benchmark test functions and real-world applications. This paper introduces a novel mutation operator, without using the scaling factor F, a conventional control parameter, and this mutation can generate multiple trial vectors by incorporating different weighted values at each generation, which can make the best of the selected multiple parents to improve the probability of generating a better offspring. In addition, in order to enhance the capacity of adaptation, a new and adaptive control parameter, i.e. the crossover rate CR, is presented and when one variable is beyond its boundary, a repair rule is also applied in this paper. The proposed algorithm ADE is validated on several constrained engineering design optimization problems reported in the specialized literature. Compared with respect to algorithms representative of the state-of-the-art in the area, the experimental results show that ADE can obtain good solutions on a test set of constrained optimization problems in engineering design