Solving mixed-integer nonlinear programming problems using improved genetic algorithms

作     者：Wasanapradit, Tawan Mukdasanit, Nalinee Chaiyaratana, Nachol Srinophakun, Thongchai 

作者机构：Kasetsart Univ Dept Chem Engn Fac Engn Bangkok 10900 Thailand King Mongkuts Univ Technol Fac Engn Dept Chem Engn Bangkok 10140 Thailand King Mongkuts Univ Technol Fac Engn Dept Chem Engn Bangkok 10800 Thailand Natl Ctr Excellence Petr Petrochem & Adv Mat Bangkok 10330 Thailand 

出 版 物：《KOREAN JOURNAL OF CHEMICAL ENGINEERING》 (韩国化工杂志)

年 卷 期：2011年第28卷第1期

页      面：32-40页

核心收录：

学科分类：081704[工学-应用化学] 0817[工学-化学工程与技术] 08[工学] 0703[理学-化学] 

基　　金：National Center of Excellence for Petroleum, Petrochemicals and Advanced Materials Graduate School, Kasetsart University 

主　　题：Genetic Algorithms Mixed Integer Nonlinear Programming Repairing Strategy CPSS Modified Genetic Algorithms 

摘      要：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.