An evolutionary programming approach to mixed-variable optimization problems

Many engineering optimization problems frequently encounter discrete variables as well as continuous variables and the presence of nonlinear discrete variables considerably adds to the solution complexity. Very few of the existing methods can find a globally optimal solution when the objective funct...

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Published in:	Applied mathematical modelling Vol. 24; no. 12; pp. 931 - 942
Main Authors:	Cao, Y.J., Jiang, L., Wu, Q.H.
Format:	Journal Article
Language:	English
Published:	Elsevier Inc 01.10.2000
Subjects:	Engineering design optimization Evolutionary programming Global optimization Mixed variables Global optimization Mixed variables Evolutionary programming Engineering design optimization
ISSN:	0307-904X
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Abstract	Many engineering optimization problems frequently encounter discrete variables as well as continuous variables and the presence of nonlinear discrete variables considerably adds to the solution complexity. Very few of the existing methods can find a globally optimal solution when the objective functions are non-convex and non-differentiable. In this paper, we present a mixed-variable evolutionary programming (MVEP) technique for solving these nonlinear optimization problems which contain integer, discrete, zero-one and continuous variables. The MVEP provides an improvement in global search reliability in a mixed-variable space and converges steadily to a good solution. An approach to handle various kinds of variables and constraints is discussed. Some examples of mixed-variable optimization problems in the literature are tested, which demonstrate that the proposed approach is superior to current methods for finding the best solution, in terms of both solution quality and algorithm robustness.
AbstractList	Many engineering optimization problems frequently encounter discrete variables as well as continuous variables and the presence of nonlinear discrete variables considerably adds to the solution complexity. Very few of the existing methods can find a globally optimal solution when the objective functions are non-convex and non-differentiable. In this paper, we present a mixed-variable evolutionary programming (MVEP) technique for solving these nonlinear optimization problems which contain integer, discrete, zero-one and continuous variables. The MVEP provides an improvement in global search reliability in a mixed-variable space and converges steadily to a good solution. An approach to handle various kinds of variables and constraints is discussed. Some examples of mixed-variable optimization problems in the literature are tested, which demonstrate that the proposed approach is superior to current methods for finding the best solution, in terms of both solution quality and algorithm robustness.
Author	Wu, Q.H. Jiang, L. Cao, Y.J.
Author_xml	– sequence: 1 givenname: Y.J. surname: Cao fullname: Cao, Y.J. email: yijiacao@yahoo.com.cn organization: Department of Electrical Engineering, Huazhong University of Science and Technology, Wuhan, Hubei 430074, People's Republic of China – sequence: 2 givenname: L. surname: Jiang fullname: Jiang, L. organization: Department of Electrical Engineering and Electronics, The University of Liverpool, Liverpool L69 3GJ, UK – sequence: 3 givenname: Q.H. surname: Wu fullname: Wu, Q.H. organization: Department of Electrical Engineering and Electronics, The University of Liverpool, Liverpool L69 3GJ, UK
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SubjectTerms	Engineering design optimization Evolutionary programming Global optimization Mixed variables
Title	An evolutionary programming approach to mixed-variable optimization problems
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