A mixed-coding scheme of evolutionary algorithms to solve mixed-integer nonlinear programming problems

In this paper, mixed-integer hybrid differential evolution (MIHDE) is developed to deal with the mixed-integer optimization problems. This hybrid algorithm contains the migration operation to avoid candidate individuals clustering together. We introduce the population diversity measure to inspect wh...

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Bibliographic Details
Published in:Computers & mathematics with applications (1987) Vol. 47; no. 8; pp. 1295 - 1307
Main Authors: Lin, Yung-Chien, Hwang, Kao-Shing, Wang, Feng-Sheng
Format: Journal Article
Language:English
Published: Elsevier Ltd 01.04.2004
Subjects:
Differential evolution
Evolutionary algorithms
Genetic algorithms
Hybrid method
Mixed-integer nonlinear programming
Differential evolution
Hybrid method
Evolutionary algorithms
Mixed-integer nonlinear programming
Genetic algorithms
ISSN:0898-1221, 1873-7668
Online Access:Get full text
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Summary:In this paper, mixed-integer hybrid differential evolution (MIHDE) is developed to deal with the mixed-integer optimization problems. This hybrid algorithm contains the migration operation to avoid candidate individuals clustering together. We introduce the population diversity measure to inspect when the migration operation should be performed so that the user can use a smaller population size to obtain a global solution. A mixed coding representation and a rounding operation are introduced in MIHDE so that the hybrid algorithm is not only used to solve the mixed-integer nonlinear optimization problems, but also used to solve the real and integer nonlinear optimization problems. Some numerical examples are tested to illustrate the performance of the proposed algorithm. Numerical examples show that the proposed algorithm converges to better solutions than the conventional genetic algorithms.
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ISSN:0898-1221
1873-7668
DOI:10.1016/S0898-1221(04)90123-X