Augmenting SPEA2 with K-Random competitive coevolution for enhanced evolutionary multi-objective optimization

One new algorithm is being proposed, which is the integration between one multiobjective evolutionary algorithm (MOEA): strength Pareto evolutionary algorithm 2 (SPEA2) and competitive coevolution using k-random opponents competitive fitness strategy. The resulting algorithm is referred to as SPEA2-...

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Bibliographic Details
Main Authors: Tse Guan Tan, Teo, J., Hui Keng Lau
Format: Conference Proceeding
Language:English
Subjects:
Benchmark testing
Evolutionary computation
Genetic algorithms
Laboratories
Optimization
Polynomials
Round robin
Online Access:Request full text
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Summary:One new algorithm is being proposed, which is the integration between one multiobjective evolutionary algorithm (MOEA): strength Pareto evolutionary algorithm 2 (SPEA2) and competitive coevolution using k-random opponents competitive fitness strategy. The resulting algorithm is referred to as SPEA2-CE-KR. This proposed algorithm was benchmarked against the original SPEA2 using seven DTLZ test problems having 3 to 5 objectives. Overall, reveal that SPEA2-CE-KR performed well for the spacing and coverage metrics.
ISSN:2155-8973
DOI:10.1109/ITSIM.2008.4631990