DocumentCode
3005949
Title
A Global Convergence Algorithm with Stochastic Search for Constrained Optimization Problems
Author
Changyin Zhou ; Guoping He
Author_Institution
Coll. of Inf. Sci. & Eng., Shandong Univ. of Sci. & Technol., Qingdao
fYear
2008
fDate
25-26 Sept. 2008
Firstpage
75
Lastpage
78
Abstract
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.
Keywords
convergence; optimisation; search problems; stochastic processes; constrained optimization problem; global convergence algorithm; global optimization problem; linear equality constraint; stochastic search; Constraint optimization; Convergence; Educational institutions; Engineering drawings; Equations; Genetic engineering; Helium; Information science; Sampling methods; Stochastic processes; convergence; global optimization; random search methods;
fLanguage
English
Publisher
ieee
Conference_Titel
Genetic and Evolutionary Computing, 2008. WGEC '08. Second International Conference on
Conference_Location
Hubei
Print_ISBN
978-0-7695-3334-6
Type
conf
DOI
10.1109/WGEC.2008.40
Filename
4637398
Link To Document