Hybrid differential evolution based on fuzzy C-means clustering

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Hybrid differential evolution based on fuzzy C-means clustering

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Genetic And Evolutionary Computation Conference archive
Proceedings of the 11th Annual conference on Genetic and evolutionary computation table of contents

Montreal, Québec, Canada

SESSION: Track 6: evolution strategies and evolutionary programming table of contents

Pages 523-530

Year of Publication: 2009

ISBN:978-1-60558-325-9

Authors

Wenyin Gong	China University of Geosciences, Wuhan, China
Zhihua Cai	China University of Geosciences, Wuhan, China
Charles X. Ling	The University of Western Ontario, London, Canada
Jun Du	The University of Western Ontario, London, Canada

Sponsors

SIGEVO: ACM Special Interest Group on Genetic and Evolutionary Computation
ACM: Association for Computing Machinery

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ACM New York, NY, USA

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ABSTRACT

In this paper, we propose a hybrid Differential Evolution (DE) algorithm based on the fuzzy C-means clustering algorithm, referred to as FCDE. The fuzzy C-means clustering algorithm is incorporated with DE to utilize the information of the population efficiently, and hence it can generate good solutions and enhance the performance of the original DE. In addition, the population-based algorithmgenerator is adopted to efficiently update the population with the clustering offspring. In order to test the performance of our approach, 13 high-dimensional benchmark functions of diverse complexities are employed. The results show that our approach is effective and efficient. Compared with other state-of-the-art DE approaches, our approach performs better, or at least comparably, in terms of the quality of the final solutions and the reduction of the number of fitness function evaluations (NFFEs).

REFERENCES

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