Difference-genetic co-evolutionary algorithm for nonlinear mixed integer programming problems
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Authors
Yuelin Gao
- Institute of Information and System Science, Beifang University of Nationalities, Yinchuan, 750021, China.
- School of Computer Science and Information Engineering, Hefei University of Technology, Hefei, 230009, China.
Ying Sun
- School of Computer Science and Information Engineering, Hefei University of Technology, Hefei, 230009, China.
Jun Wu
- Institute of Information and System Science, Beifang University of Nationalities, Yinchuan, 750021, China.
Abstract
In this paper, the difference genetic co-evolutionary algorithm (D-GCE) is proposed for the mixed integer
programming problems. First, the mixed integer programming problem with constrains converted to
unconstrained bi-objective optimization problems. Secondly, selection mechanism combines the Pareto dominance
and superiority of feasible solution methods to choose the excellent individual as the next generation.
Final, differential evolution algorithm and genetic algorithm handle the continuous part and discrete part,
respectively. Numerical experiments on 24 test functions have shown that the new approach is efficient.
The comparison results among the D-GCE and other evolutionary algorithms indicate that the proposed
D-GCE algorithm is competitive with and in some cases superior to, other existing algorithms in terms of
the quality, efficiency, convergence rate, and robustness of the final solution.
Share and Cite
ISRP Style
Yuelin Gao, Ying Sun, Jun Wu, Difference-genetic co-evolutionary algorithm for nonlinear mixed integer programming problems, Journal of Nonlinear Sciences and Applications, 9 (2016), no. 3, 1261--1284
AMA Style
Gao Yuelin, Sun Ying, Wu Jun, Difference-genetic co-evolutionary algorithm for nonlinear mixed integer programming problems. J. Nonlinear Sci. Appl. (2016); 9(3):1261--1284
Chicago/Turabian Style
Gao, Yuelin, Sun, Ying, Wu, Jun. "Difference-genetic co-evolutionary algorithm for nonlinear mixed integer programming problems." Journal of Nonlinear Sciences and Applications, 9, no. 3 (2016): 1261--1284
Keywords
- Mixed integer programming
- differential evolution
- genetic algorithm
- co-evolution
- constrained optimization.
MSC
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