Differential evolution with improved elite archive mutation and dynamic parameter adjustment-Reference-Cited by-同舟云学术

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Differential evolution with improved elite archive mutation and dynamic parameter adjustment

Published:2018-03-14 Issue:S4 Volume:22 Page:9347-9356
ISSN:1386-7857
Container-title:Cluster Computing
language:en
Short-container-title:Cluster Comput

Author:

Lu Zengquan,Zhang Lilun,Wang Dezhi

Funder

the National Key Research and Development Project of China

the Scientific Research Project of NUDT

Publisher

Springer Science and Business Media LLC

Subject

Computer Networks and Communications,Software

Link

http://link.springer.com/content/pdf/10.1007/s10586-018-2163-6.pdf

Reference23 articles.

1. Gong, Y.J., Chen, W.N., Zhan, Z.H., Zhang, J., Li, Y., Zhang, Q.: Distributed evolutionary algorithms and their models: a survey of the state-of-the-art. Appl. Soft Comput. 34, 286–300 (2015)

2. Wang, L., Yang, B., Orchard, J.: Particle swarm optimization using dynamic tournament topology. Appl. Soft Comput. 48, 584–596 (2016)

3. Li, H., Demeulemeester, E.: A genetic algorithm for the robust resource leveling problem. J. Sched. 19(1), 43–60 (2016)

4. Das, S., Mullick, S.S., Suganthan, P.N.: Recent advances in differential evolution—an updated survey. Swarm Evolut. Comput. 27, 1–30 (2016)

5. Xu, Y., Wang, L., Wang, S.Y., Liu, M.: An effective teaching–learning-based optimization algorithm for the flexible job-shop scheduling problem with fuzzy processing time. Neurocomputing 148, 260–268 (2015)

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