DocumentCode
2696955
Title
A genetic algorithm for solving multi-constrained function optimization problems based on KS function
Author
Xiao, Jianhua ; Xu, Jin ; Shao, Zehui ; Jiang, Congfeng ; Pan, Linqiang
Author_Institution
Huazhong Univ. of Sci. & Technol., Wuhan
fYear
2007
fDate
25-28 Sept. 2007
Firstpage
4497
Lastpage
4501
Abstract
In this paper, a new genetic algorithm for solving multi-constrained optimization problems based on KS function is proposed. Firstly, utilizing the agglomeration features of KS function, all constraints of optimization problems are agglomerated to only one constraint. Then, we use genetic algorithm to solve the optimization problem after the compression of constraints. Finally, the simulation results on benchmark functions show the efficiency of our algorithm.
Keywords
genetic algorithms; KS function; agglomeration features; genetic algorithm; multiconstrained function optimization; Constraint optimization; Genetic algorithms; Industrial engineering; Mathematical model; Mathematics; Operations research; Optimization methods; Quadratic programming; Robustness; Testing;
fLanguage
English
Publisher
ieee
Conference_Titel
Evolutionary Computation, 2007. CEC 2007. IEEE Congress on
Conference_Location
Singapore
Print_ISBN
978-1-4244-1339-3
Electronic_ISBN
978-1-4244-1340-9
Type
conf
DOI
10.1109/CEC.2007.4425060
Filename
4425060
Link To Document