A Global Convergence Algorithm with Stochastic Search for Constrained Optimization Problems

被引：0
|
作者：
Zhou Changyin [1 ]
He Guoping [1 ]
机构：
[1] Shandong Univ Sci & Technol, Coll Informat Sci & Engn, Qingdao 266510, Peoples R China
来源：
SECOND INTERNATIONAL CONFERENCE ON GENETIC AND EVOLUTIONARY COMPUTING: WGEC 2008, PROCEEDINGS | 2008年
关键词：
D O I：
10.1109/WGEC.2008.40
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this paper we investigate the global optimization problems with bounded variables and linear equality constraints. We suggest an approach to drawing sample points randomly from the feasible region. A new population-based global optimization algorithm is proposed. We also show that the algorithm converges to the global optimal solution with probability one. The method is easily extended to global optimization problems with general constraints.
引用
收藏
页码：75 / 78
页数：4
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