Clonal and Cauchy-mutation Evolutionary Algorithm for Global Numerical Optimization

Authors:
Jing Guan;Ming Yang
Affiliations:
Institute for Pattern Recognition and Artificial Intelligence, Huazhong University of Science and Technology, Wuhan, China 430074;School of Computer Science, China University of Geosciences, Wuhan, China 430074
Venue:
ISICA '09 Proceedings of the 4th International Symposium on Advances in Computation and Intelligence
Year:
2009

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Abstract

Many real-life problems can be formulated as numerical optimization of certain objective functions. However, for an objective function possesses numerous local optima, many evolutionary algorithms (EAs) would be trapped in local solutions. To improve the search efficiency, this paper presents a clone and Cauchy-mutation evolutionary algorithm (CCEA), which employs dynamic clone and Cauchy mutation methods, for numerical optimization. For a suit of 23 benchmark test functions, CCEA is able to locate the near-optimal solutions for almost 23 test functions with relatively small variance. Especially, for f 14 -f 23 , CCEA can get better solutions than other algorithms.