Title :
A complex-genetic algorithm for solving constrained optimization problems
Author :
Li, Ming-song ; Zeng, Pu-Hua ; Zhong, Ruo-wu ; Wang, Hui-ping ; Zhang, Fen-Fen
Author_Institution :
Comput. Center, Shaoguan Univ., Shaoguan
Abstract :
Constrained optimization problems (COPs) are a kind of mathematic programming problem frequently encountered in the disciplines of science and engineering application. After analyzing weaknesses of existing constrained optimization evolutionary algorithms (COEAs), a novel improved algorithm called complex-GA, which converts COPs into multi-objective optimization problems (MOPs) and effectively combines multi-objective optimization concept with global and local search, was proposed to handle COPs. Complex-GA increases the speed of optima search noticeably by combining the advantages of the two methods and overcomes the disadvantages of them.
Keywords :
genetic algorithms; mathematical programming; search problems; complex genetic algorithm; constrained optimization evolutionary algorithms; constrained optimization problems; global search; local search; mathematic programming problem; multiobjective optimization problems; Constraint optimization; Cybernetics; Electronic mail; Genetics; Machine learning; Machine learning algorithms; Mathematical programming; Mathematics; Optimization methods; Shape; Complex method; Constrained optimization; Genetic algorithm(GA); Multi-objective optimization;
Conference_Titel :
Machine Learning and Cybernetics, 2008 International Conference on
Conference_Location :
Kunming
Print_ISBN :
978-1-4244-2095-7
Electronic_ISBN :
978-1-4244-2096-4
DOI :
10.1109/ICMLC.2008.4620526
