A new differential evolution algorithm with random mutation

Authors:
Yuelin Gao;Junmei Liu
Affiliations:
Institute of Information and System Science, North National University, Yinchuan, China;Institute of Information and System Science, North National University, Yinchuan, China
Venue:
ICIC'09 Proceedings of the Intelligent computing 5th international conference on Emerging intelligent computing technology and applications
Year:
2009

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Differential Evolution – A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces

Journal of Global Optimization

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Abstract

This paper suggests a new differential evolution algorithm with a random mutation. In this algorithm, a new dynamically mutation rule is given through a linear descending weighted convex combination of two different mutation strategies of DE/rand/1 and DE/best/1, so as to use dynamically DE/rand/1 and DE/best/1 advantages, and an exponent increased crossover probability and a random mutation is introduced to further improve the global optimal capacity. The standard test functions tests show that the new algorithm has fast convergence and high accuracy, robustness stronger, more suitable for solving complex high-dimensional global optimization problem.