Improving the performance of differential evolution algorithm using Cauchy mutation |
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Authors: | Musrrat Ali Millie Pant |
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Affiliation: | (1) Department of Paper Technology, Indian Institute of Technology Roorkee, Roorkee, 247667, India |
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Abstract: | Differential evolution (DE) is a powerful yet simple evolutionary algorithm for optimization of real-valued, multimodal functions.
DE is generally considered as a reliable, accurate and robust optimization technique. However, the algorithm suffers from
premature convergence and/or slow convergence rate resulting in poor solution quality and/or larger number of function evaluation
resulting in large CPU time for optimizing the computationally expensive objective functions. Therefore, an attempt to speed
up DE is considered necessary. This research introduces a modified differential evolution (MDE) that enhances the convergence
rate without compromising with the solution quality. The proposed MDE algorithm maintains a failure_counter (FC) to keep a tab on the performance of the algorithm by scanning or monitoring the individuals. Finally, the individuals that
fail to show any improvement in the function value for a successive number of generations are subject to Cauchy mutation with
the hope of pulling them out of a local attractor which may be the cause of their deteriorating performance. The performance
of proposed MDE is investigated on a comprehensive set of 15 standard benchmark problems with varying degrees of complexities
and 7 nontraditional problems suggested in the special session of CEC2008. Numerical results and statistical analysis show
that the proposed modifications help in locating the global optimal solution in lesser numbers of function evaluation in comparison
with basic DE and several other contemporary optimization algorithms. |
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Keywords: | |
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