Global optimization over linear constraint non-convex programming problem

被引：1

作者：

张贵军 ^{[1
]}

吴惕华 ^{[1
,2
]}

叶蓉 ^{[2
]}

杨海清 ^{[1
]}

机构：

[1] College of Information, Zhejiang University of Technology, Hangzhou 310032, China

[2] Hebei Academy of Sciences, Shijiazhuang 050081, China

来源：

Journal of Harbin Institute of Technology(New series) | 2005年 / 06期

关键词：

global optimization; linear constraint; steady state genetic algorithms; extremes encode; convex crossover;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

A improving Steady State Genetic Algorithm for global optimization over linear constraint non-convex programming problem is presented. By convex analyzing, the primal optimal problem can be converted to an equivalent problem, in which only the information of convex extremes of feasible space is included, and is more easy for GAs to solve. For avoiding invalid genetic operators, a redesigned convex crossover operator is also performed in evolving. As a integrality, the quality of two problem is proven, and a method is also given to get all extremes in linear constraint space. Simulation result show that new algorithm not only converges faster, but also can maintain an diversity population, and can get the global optimum of test problem.

引用

页码：650 / 655

页数：6

共 7 条

[1] Numerical Analysis. YANQingjin. . 1991
[2] Hybrid methods using ge-netic algorithms for global optimization. RENDERS J M,FLASSE S P. IEEE Trans-actions on System,Man,and Cybernetics(Part B) . 1996
[3] Optimal Theory and Algorithms. CHEN Baolin. . 1989
[4] Con-strained optimization of test intervals using a steady-state genetic algorithm. MARTORELL S,CARLOS S,SANCHEZ A,et al. Reliability Engineering and System Safety . 2000
[5] Linear Programming and Network Flows. MOKHTAR S,JARVIS J J. . 1977
[6] Nonlinear Programming Analysis and Meth-ods. AVRIEL M. . 1976
[7] Genetic Algorithms:Theory and Applica-tions. ZHOU Ming. . 1999

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