Improving the performance of differential evolution algorithm using Cauchy mutation

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 r...

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Bibliographic Details
Published in:Soft computing (Berlin, Germany) Vol. 15; no. 5; pp. 991 - 1007
Main Authors: Ali, Musrrat, Pant, Millie
Format: Journal Article
Language:English
Published: Berlin/Heidelberg Springer-Verlag 01.05.2011
Springer Nature B.V
Subjects:
Adaptation
Artificial Intelligence
Computational Intelligence
Control
Convergence
Efficiency
Engineering
Evolutionary algorithms
Evolutionary computation
Genetic algorithms
Investigations
Literature reviews
Mathematical Logic and Foundations
Mechatronics
Mutation
Optimization
Optimization techniques
Original Paper
Robotics
Robustness (mathematics)
Statistical analysis
Differential evolution
Cauchy mutation
Global optimization
ISSN:1432-7643, 1433-7479
Online Access:Get full text
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Summary: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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ISSN:1432-7643
1433-7479
DOI:10.1007/s00500-010-0655-2