A complex-genetic algorithm for solving constrained optimization problems

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 c...

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Bibliographic Details
Published in:2008 International Conference on Machine Learning and Cybernetics Vol. 2; pp. 869 - 873
Main Authors: Ming-Song Li, Pu-Hua Zeng, Ruo-Wu Zhong, Hui-Ping Wang, Fen-Fen Zhang
Format: Conference Proceeding
Language:English
Published: IEEE 01.07.2008
Subjects:
Algorithm design and analysis
Complex method
Constrained optimization
Distance measurement
Genetic algorithm(GA)
Genetic algorithms
Genetics
Machine learning
Multi-objective optimization
Optimization
Optimization methods
ISBN:1424420954, 9781424420957
ISSN:2160-133X
Online Access:Get full text
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Summary: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.
ISBN:1424420954
9781424420957
ISSN:2160-133X
DOI:10.1109/ICMLC.2008.4620526