A Survey of Nearest-Better Clustering in Swarm and Evolutionary Computation

Wenjian Luo; Xin Lin; Jiajia Zhang; Mike Preuss

doi:10.1109/CEC45853.2021.9505008

A Survey of Nearest-Better Clustering in Swarm and Evolutionary Computation

Wenjian Luo^*
, Xin Lin
, Jiajia Zhang
, Mike Preuss

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

13 Scopus citations

Abstract

Nearest-Better Clustering (NBC) is an emergent niching technique in Swarm and Evolutionary Computation for optimization, which does not need to fix the number or radius of clusters in advance. The key idea of NBC is to first link each individual to its nearest better neighbor to form a spanning tree of all individuals in the population, and then partition all individuals into clusters by deleting the longer edges in the spanning tree. In this paper, a survey on the Nearest-Better Clustering algorithms and applications in multimodal and dynamic optimization is provided. First, the basic NBC algorithm is introduced. Second, the improvements of the basic NBC are detailed. Third, multimodal and dynamic optimization algorithms powered by NBC are enlisted and discussed.

Original language	English
Title of host publication	2021 IEEE Congress on Evolutionary Computation, CEC 2021 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1961-1967
Number of pages	7
ISBN (Electronic)	9781728183923
DOIs	https://doi.org/10.1109/CEC45853.2021.9505008
State	Published - 2021
Externally published	Yes
Event	2021 IEEE Congress on Evolutionary Computation, CEC 2021 - Virtual, Krakow, Poland Duration: 28 Jun 2021 → 1 Jul 2021

Publication series

Name	2021 IEEE Congress on Evolutionary Computation, CEC 2021 - Proceedings

Conference

Conference	2021 IEEE Congress on Evolutionary Computation, CEC 2021
Country/Territory	Poland
City	Virtual, Krakow
Period	28/06/21 → 1/07/21

Keywords

Evolutionary algorithms
Nearest better clustering
Swarm intelligence

Access to Document

10.1109/CEC45853.2021.9505008

Cite this

Luo, W., Lin, X., Zhang, J., & Preuss, M. (2021). A Survey of Nearest-Better Clustering in Swarm and Evolutionary Computation. In 2021 IEEE Congress on Evolutionary Computation, CEC 2021 - Proceedings (pp. 1961-1967). (2021 IEEE Congress on Evolutionary Computation, CEC 2021 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CEC45853.2021.9505008

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abstract = "Nearest-Better Clustering (NBC) is an emergent niching technique in Swarm and Evolutionary Computation for optimization, which does not need to fix the number or radius of clusters in advance. The key idea of NBC is to first link each individual to its nearest better neighbor to form a spanning tree of all individuals in the population, and then partition all individuals into clusters by deleting the longer edges in the spanning tree. In this paper, a survey on the Nearest-Better Clustering algorithms and applications in multimodal and dynamic optimization is provided. First, the basic NBC algorithm is introduced. Second, the improvements of the basic NBC are detailed. Third, multimodal and dynamic optimization algorithms powered by NBC are enlisted and discussed.",

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author = "Wenjian Luo and Xin Lin and Jiajia Zhang and Mike Preuss",

note = "Publisher Copyright: {\textcopyright} 2021 IEEE; 2021 IEEE Congress on Evolutionary Computation, CEC 2021 ; Conference date: 28-06-2021 Through 01-07-2021",

year = "2021",

doi = "10.1109/CEC45853.2021.9505008",

language = "英语",

series = "2021 IEEE Congress on Evolutionary Computation, CEC 2021 - Proceedings",

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Luo, W, Lin, X, Zhang, J & Preuss, M 2021, A Survey of Nearest-Better Clustering in Swarm and Evolutionary Computation. in 2021 IEEE Congress on Evolutionary Computation, CEC 2021 - Proceedings. 2021 IEEE Congress on Evolutionary Computation, CEC 2021 - Proceedings, Institute of Electrical and Electronics Engineers Inc., pp. 1961-1967, 2021 IEEE Congress on Evolutionary Computation, CEC 2021, Virtual, Krakow, Poland, 28/06/21. https://doi.org/10.1109/CEC45853.2021.9505008

A Survey of Nearest-Better Clustering in Swarm and Evolutionary Computation. / Luo, Wenjian; Lin, Xin; Zhang, Jiajia et al.
2021 IEEE Congress on Evolutionary Computation, CEC 2021 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2021. p. 1961-1967 (2021 IEEE Congress on Evolutionary Computation, CEC 2021 - Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - Nearest-Better Clustering (NBC) is an emergent niching technique in Swarm and Evolutionary Computation for optimization, which does not need to fix the number or radius of clusters in advance. The key idea of NBC is to first link each individual to its nearest better neighbor to form a spanning tree of all individuals in the population, and then partition all individuals into clusters by deleting the longer edges in the spanning tree. In this paper, a survey on the Nearest-Better Clustering algorithms and applications in multimodal and dynamic optimization is provided. First, the basic NBC algorithm is introduced. Second, the improvements of the basic NBC are detailed. Third, multimodal and dynamic optimization algorithms powered by NBC are enlisted and discussed.

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A Survey of Nearest-Better Clustering in Swarm and Evolutionary Computation

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