Solving mixed-integer nonlinear programming problems using improved genetic algorithms

This paper proposes a method for solving mixed-integer nonlinear programming problems to achieve or approach the optimal solution by using modified genetic algorithms. The representation scheme covers both integer and real variables for solving mixed-integer nonlinear programming, nonlinear programm...

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Bibliographic Details
Published in:The Korean journal of chemical engineering Vol. 28; no. 1; pp. 32 - 40
Main Authors: Wasanapradit, Tawan, Mukdasanit, Nalinee, Chaiyaratana, Nachol, Srinophakun, Thongchai
Format: Journal Article
Language:English
Published: Boston Springer US 01.01.2011
한국화학공학회
Subjects:
Biotechnology
Catalysis
Chemistry
Chemistry and Materials Science
Industrial Chemistry/Chemical Engineering
Materials Science
화학공학
Mixed Integer Nonlinear Programming
CPSS
Modified Genetic Algorithms
Genetic Algorithms
Repairing Strategy
ISSN:0256-1115, 1975-7220
Online Access:Get full text
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Summary:This paper proposes a method for solving mixed-integer nonlinear programming problems to achieve or approach the optimal solution by using modified genetic algorithms. The representation scheme covers both integer and real variables for solving mixed-integer nonlinear programming, nonlinear programming, and nonlinear integer programming. The repairing strategy, a secant method incorporated with a bisection method, plays an important role in converting infeasible chromosomes to feasible chromosomes at the constraint boundary. To prevent premature convergence, the appropriate diversity of the structures in the population must be controlled. A cross-generational probabilistic survival selection method (CPSS) is modified for real number representation corresponding to the representation scheme. The efficiency of the proposed method was validated with several numerical test problems and showed good agreement.
Bibliography:G704-000406.2011.28.1.040
http://www.cheric.org/article/855516
ISSN:0256-1115
1975-7220
DOI:10.1007/s11814-010-0323-3