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CEC
2009
IEEE

Multi-start JADE with knowledge transfer for numerical optimization

10 years 10 months ago
Multi-start JADE with knowledge transfer for numerical optimization
— JADE is a recent variant of Differential Evolution (DE) for numerical optimization, which has been reported to obtain some promising results in experimental study. However, we observed that the reliability, which is an important characteristic of stochastic algorithms, of JADE still needs to be improved. In this paper we apply two strategies together on the original JADE, to dedicatedly improve the reliability of it. We denote the new algorithm as rJADE. In rJADE, we first modify the control parameter adaptation strategy of JADE by adding a weighting strategy. Then, a “restart with knowledge transfer” strategy is applied by utilizing the knowledge obtained from previous failures to guide the subsequent search. Experimental studies show that the proposed rJADE achieved significant improvements on a set of widely used benchmark functions.
Fei Peng, Ke Tang, Guoliang Chen, Xin Yao
Added 20 May 2010
Updated 20 May 2010
Type Conference
Year 2009
Where CEC
Authors Fei Peng, Ke Tang, Guoliang Chen, Xin Yao
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