Evaluated Preference Genetic Algorithm and its Engineering Applications

This paper proposes a novel multiobjective genetic algorithm (MOGA), Evaluated Preference Genetic Algorithm (EPGA), for efficiently solving engineering multiobjective optimization problems. EPGA utilizes a preferred objective vector to perform a fast multiobjective ranking schema within a low comput...

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Bibliographic Details
Published in:Key engineering materials 2011-01, Vol.467-469 (SUPPL.3), p.2129-2136
Main Authors: Liu, Tung Kuan, Wu, Wen Ping, Ho, Min Rong, Chen, Chiu Hung, Chang, Hsin Yuan
Format: Article
Language:English
Subjects:
Algorithms
Genetic algorithms
Genetics
Mathematical analysis
Mathematical models
Optimization
Pareto optimality
Vectors (mathematics)
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Summary:This paper proposes a novel multiobjective genetic algorithm (MOGA), Evaluated Preference Genetic Algorithm (EPGA), for efficiently solving engineering multiobjective optimization problems. EPGA utilizes a preferred objective vector to perform a fast multiobjective ranking schema within a low computation complexity O(MNlogN) where N is the size of genetic population and M is the number of objectives. For verifying the proposed algorithms, this paper studies two engineering problems in which multiple mutual-conflicted objectives should be considered. According to the experimental results, the proposed EPGA can efficiently explore the Pareto front and provide very good solution capabilities for the engineering optimization problems.
ISSN:1013-9826
1662-9795
1662-9795
DOI:10.4028/www.scientific.net/KEM.467-469.2129