Synergy between convergence and divergence: review of concepts and methods

Kalin Penev

doi:10.1007/978-3-030-97549-4_29

Synergy between convergence and divergence: review of concepts and methods

Kalin Penev

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

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Abstract

Modern Industry 4.0 technologies face a challenge in dealing with billions of connected devices, petabyte-scale of generated data, and exponentially growing internet traffic. Artificial Intelligence and Evolutionary algorithms can resolve variety of large optimisation problems. Many methods employed in search for solutions often fall in stagnation or in unacceptable results, which reminds for classical dilemma exploration versus exploitations closely related with convergence and diversity of the explored solutions. This article reviews convergence and divergence centred algorithms and discuses synergy between convergence and divergence in adaptive heuristics.

Original language	English
Title of host publication	Large-Scale Scientific Computing
Subtitle of host publication	LSSC 2021
Editors	I. Lirkov, S. Margenov
Publisher	Springer
Pages	250-256
Number of pages	7
Volume	13127
ISBN (Electronic)	978-3-030-97549-4
ISBN (Print)	978-3-030-97548-7
DOIs	https://doi.org/10.1007/978-3-030-97549-4_29
Publication status	Published - 18 Mar 2022
Event	13th International Conference on "Large Scale Scientific Computations" - Sozopol, Bulgaria Duration: 7 Jun 2021 → 11 Jun 2021 Conference number: 13 http://parallel.bas.bg/Conferences/SciCom21/

Publication series

Name	Lecture Notes in Computer Science
Publisher	Springer
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	13th International Conference on "Large Scale Scientific Computations"
Abbreviated title	LSSC 2021
Country/Territory	Bulgaria
City	Sozopol
Period	7/06/21 → 11/06/21
Internet address	http://parallel.bas.bg/Conferences/SciCom21/

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 9 Industry, Innovation, and Infrastructure

Access to Document

10.1007/978-3-030-97549-4_29

01_LSSC21_Kalin_Penev v08_06_21Accepted author manuscript, 247 KB
PowerPoint slidesAccepted author manuscript, 2.55 MB

Cite this

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title = "Synergy between convergence and divergence: review of concepts and methods",

abstract = "Modern Industry 4.0 technologies face a challenge in dealing with billions of connected devices, petabyte-scale of generated data, and exponentially growing internet traffic. Artificial Intelligence and Evolutionary algorithms can resolve variety of large optimisation problems. Many methods employed in search for solutions often fall in stagnation or in unacceptable results, which reminds for classical dilemma exploration versus exploitations closely related with convergence and diversity of the explored solutions. This article reviews convergence and divergence centred algorithms and discuses synergy between convergence and divergence in adaptive heuristics. ",

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booktitle = "Large-Scale Scientific Computing",

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Penev, K 2022, Synergy between convergence and divergence: review of concepts and methods. in I Lirkov & S Margenov (eds), Large-Scale Scientific Computing: LSSC 2021. vol. 13127, Lecture Notes in Computer Science, Springer, pp. 250-256, 13th International Conference on "Large Scale Scientific Computations", Sozopol, Bulgaria, 7/06/21. https://doi.org/10.1007/978-3-030-97549-4_29

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AB - Modern Industry 4.0 technologies face a challenge in dealing with billions of connected devices, petabyte-scale of generated data, and exponentially growing internet traffic. Artificial Intelligence and Evolutionary algorithms can resolve variety of large optimisation problems. Many methods employed in search for solutions often fall in stagnation or in unacceptable results, which reminds for classical dilemma exploration versus exploitations closely related with convergence and diversity of the explored solutions. This article reviews convergence and divergence centred algorithms and discuses synergy between convergence and divergence in adaptive heuristics.

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