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.
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Guan, J., Yang, M. (2009). Clonal and Cauchy-mutation Evolutionary Algorithm for Global Numerical Optimization. In: Cai, Z., Li, Z., Kang, Z., Liu, Y. (eds) Advances in Computation and Intelligence. ISICA 2009. Lecture Notes in Computer Science, vol 5821. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04843-2_24
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DOI: https://doi.org/10.1007/978-3-642-04843-2_24
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