Adaptive learning differential evolution for numeric optimization

Authors:
Yi Liu;Shengwu Xiong;Hui Li;Shuzhen Wan
Affiliations:
School of Computer Science and Technology, Wuhan University of Technology, Wuhan, China;School of Computer Science and Technology, Wuhan University of Technology, Wuhan, China;School of Computer Science and Technology, Wuhan University of Technology, Wuhan, China;School of Computer Science and Technology, Wuhan University of Technology, Wuhan, China
Venue:
ICSI'11 Proceedings of the Second international conference on Advances in swarm intelligence - Volume Part I
Year:
2011

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Abstract

Differential Evolution algorithm is a simple yet reliable and robust evolutionary algorithm for numeric optimization. However, fine-tuning control parameters of DE algorithm is a tedious and time-consuming task thus became a major challenge for its application. This paper introduces a novel self-adaptive method for tuning the amplification parameters F of DE dynamically. This method sampled appropriate F value from a probabilistic model build on periodic learning experience. The performance of proposed MSDE is investigated and compared with other state-of-art self-adaptive approaches. Moreover, the influence of learning frequency of MSDE is investigated.