Paper Info
Title
Multi-start JADE with knowledge transfer for numerical optimization
Abstract
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.
Year
DOI
Venue
2009
10.1109/CEC.2009.4983171
IEEE Congress on Evolutionary Computation
Keywords
Field
DocType
weighting strategy,differential evolution,important characteristic,multi-start jade,knowledge transfer,numerical optimization,benchmark function,experimental study,new algorithm,original jade,proposed rjade,control parameter adaptation strategy,reliability,computer applications,distributed algorithms,computer science,convergence,particle swarm optimization,chromium,application software,stochastic processes,strontium,evolutionary computation
Stochastic algorithms,Evolution biology,Convergence (routing),Mathematical optimization,Computer science,Knowledge transfer,Evolutionary computation,Differential evolution,Artificial intelligence,A-weighting,Machine learning
Conference
Citations 
PageRank 
References 
22
1.18
13
Authors
4
Name
Order
Citations
PageRank
Fei Peng1221.18
Tang Ke22798139.09
Chen Guoliang338126.16
Xin Yao414858945.63