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An adaptive variable strategy pareto differential evolution algorithm for multi-objective optimization

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5 Author(s)
Jian Fu ; Sch. of Autom., Wuhan Univ. of Technol., Wuhan ; Qing Liu ; Xinmin Zhou ; Kui Xiang
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In the paper, we propose an adaptive variable strategy Pareto differential evolution algorithm for multi-objective optimization (AVSPDE). It is different from the general adaptive DE methods which are regulated by variable parameters and applied in single-objective area. Based on the real-time information from the tournament selection set (TSS), there are two DE variants to switch dynamically during the run, in which one aims at fast convergence and the other focus on the diverse spread The theoretical analysis and the digital simulation show the presented method can achieved better performance.

Published in:

Evolutionary Computation, 2008. CEC 2008. (IEEE World Congress on Computational Intelligence). IEEE Congress on

Date of Conference:

1-6 June 2008