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Conflict of Interest: In relation to this article, we declare that there is no conflict of interest.

Publication history: Received March 6, 2010
Accepted May 13, 2010

This is an Open-Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/bync/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Solving mixed-integer nonlinear programming problems using improved genetic algorithms

Tawan Wasanapradit¹ Nalinee Mukdasanit² Nachol Chaiyaratana³ Thongchai Srinophakun^{2 4†}

¹Department of Chemical Engineering, Faculty of Engineering, King Mongkut’s University of Technology Thonburi, Bangkok 10140, Thailand ²Department of Chemical Engineering, Faculty of Engineering, Kasetsart University, Bangkean, Bangkok 10900, Thailand ³Department of Chemical Engineering, Faculty of Engineering, King Mongkut’s University of Technology North Bangkok, Bang Sue, Bangkok 10800, Thailand ⁴National Center of Excellence for Petroleum, Petrochemicals, and Advanced Materials, Pathumwan, Bangkok 10330, Thailand

fengtcs@ku.ac.th

Korean Journal of Chemical Engineering, January 2011, 28(1), 32-40(9), 10.1007/s11814-010-0323-3

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Abstract

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.

Keywords

Genetic Algorithms Mixed Integer Nonlinear Programming Repairing Strategy CPSS Modified Genetic Algorithms

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