A reference points and intuitionistic fuzzy dominance based particle swarm algorithm for multi/many-objective optimization-Reference-Cited by-同舟云学术

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A reference points and intuitionistic fuzzy dominance based particle swarm algorithm for multi/many-objective optimization

Published:2019-12-19 Issue:4 Volume:50 Page:1133-1154
ISSN:0924-669X
Container-title:Applied Intelligence
language:en
Short-container-title:Appl Intell

Author:

Yang Wusi,Chen Li,Wang Yi,Zhang Maosheng

Funder

National Key Scientific Instrument and Equipment Development Projects of China

Natural Science Foundation of Shaanxi Province

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence

Link

http://link.springer.com/content/pdf/10.1007/s10489-019-01569-3.pdf

Reference45 articles.

1. Herrero JG, Berlanga A, Lopez JMM (2009) Effective evolutionary algorithms for many-specifications attainment: Application to air traffic control tracking filters. IEEE Trans Evol Comput 13(1):151–168. https://doi.org/10.1109/TEVC.2008.920677

2. Sülflow A, Drechsler N, Drechsler R (2007) Robust multi-objective optimization in high dimensional spaces. Proc. Evol. Multi-Criter. Optim, Matsushima, pp 715–726. https://doi.org/10.1007/978-3-540-70928-2_54

3. Sayyad AS, Menzies T, Ammar H (2013) On the value of user preferences in search-based software engineering: A case study in software product lines. In: Proc. 35th Int. Conf. Softw. Eng., San Francisco, CA, USA, pp. 492–501. https://doi.org/10.1109/ICSE.2013.6606595

4. Colleo CC, Lamont GB et al (2002) Evolutionary algorithms for solving multiobjective problems. Springer. https://doi.org/10.1007/978-0-387-36797-2

5. Zheng D, Gen M, Cheng R (1999) Multi-objective optimization using genetic algorithms. Engineering Valuation and Cost Analysis 2:303–310. https://doi.org/10.1016/j.ress.2005.11.018

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