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Please use this identifier to cite or link to this item: http://hdl.handle.net/1812/347

Title: INVESTIGATING MULTIOBJECTIVE OPTIMIZATION USING AN AUGMENTED COEVOLUTIONARY SPEA2 ALGORITHM
Authors: Tse, Guan Tan
Teo, Jason
Hui, Keng Lau
Keywords: competitive coevolution
K-random opponents
multiobjective
Pareto optimization
SPEA2
Issue Date: 2007
Abstract: A new algorithm for solving multiobjective optimization problems with three to five objectives, namely SPEA2-CE-KR, is proposed. This introduced algorithm is an extension of a state of the art multiobjective evolutionary algorithm: Strength Pareto Evolutionary Algorithm 2 (SPEA2). SPEA2-CE-KR is a hybridization of SPEA2 and Competitive Coevolution (CE) concept with K-Random Opponents (KR) competitive fitness strategy. Comparison performance between SPEA2-CE-KR and SPEA2 are demonstrated with a set of DTLZ test problems, from DTLZ1 to DTLZ7. The experimental results reveal that, this presented algorithm outperforms SPEA2 in terms of convergence to the true Pareto front and the coverage of the obtained nondominated solutions.
Description: Proceeding of the 2nd International Conference on Informatics (Informatics 2007), 27th-28th November 2007, Hilton Petaling Jaya Hotel, Petaling Jaya, Selangor, Malaysia. Page T1-65 - T1-71
URI: http://dspace.fsktm.um.edu.my/handle/1812/347
ISBN: 978-983-43491-1-0
Appears in Collections:Informatics 2007

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