Introductory Chapter: Clustering with Nature-Inspired Optimization Algorithms-Reference-Cited by-同舟云学术

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Introductory Chapter: Clustering with Nature-Inspired Optimization Algorithms

Published:2020-03-25 Issue: Volume: Page:
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Container-title:Introduction to Data Science and Machine Learning
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Author:

Erdogmus Pakize,Kayaalp Fatih

Publisher

IntechOpen

Link

http://www.intechopen.com/download/pdf/71262

Reference30 articles.

1. Available from: https://www.gtac.edu.au/the-kingfisher-and-the-bullet-train-in-the-news/. Latest access

2. Goldberg DE, Deb K. A comparative analysis of selection schemes used in genetic algorithms. Foundations of Genetic Algorithms. Vol. 1. Elsevier; 1991. pp. 69-93

3. Kennedy J, Eberhart R. Particle Swarm Optimization. 1995. pp. 1942-1948

4. Simon D. Biogeography-based optimization. IEEE Transactions on Evolutionary Computation. 2008;12(6):702-713

5. Mirjalili S, Mirjalili SM, Lewis A. Grey wolf optimizer. Advances in Engineering Software. 2014;69:46-61

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1. HCDQN-ORA: a novel hybrid clustering and deep Q-network technique for dynamic user location-based optimal resource allocation in a fog environment;The Journal of Supercomputing;2024-01-17

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