A Genetic Simulated Annealing Algorithm to Optimize the Small-World Network Generating Process

A Genetic Simulated Annealing Algorithm to Optimize the Small-World Network Generating Process

Network structure is an important component of analysis in many parts of the natural and social sciences. Optimization of network structure in order to achieve specific goals has been a major research focus. The small-world network is known to have a high average clustering coefficient and a low ave...

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Main Authors: Haifeng Du, Jiarui Fan, Xiaochen He, Marcus W. Feldman
Format: Article
Language:English
Published: Hindawi-Wiley 2018-01-01
Series:Complexity
Online Access:http://dx.doi.org/10.1155/2018/1453898
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spelling doaj-f819cf9f70aa4b949fb22a78da47aedb2020-11-24T20:48:53ZengHindawi-WileyComplexity1076-27871099-05262018-01-01201810.1155/2018/14538981453898A Genetic Simulated Annealing Algorithm to Optimize the Small-World Network Generating ProcessHaifeng Du0Jiarui Fan1Xiaochen He2Marcus W. Feldman3Center for Administration and Complexity Science of Xi’an Jiaotong University, Xi’an, Shanxi Province 710049, ChinaCenter for Administration and Complexity Science of Xi’an Jiaotong University, Xi’an, Shanxi Province 710049, ChinaCenter for Administration and Complexity Science of Xi’an Jiaotong University, Xi’an, Shanxi Province 710049, ChinaCenter for Administration and Complexity Science of Xi’an Jiaotong University, Xi’an, Shanxi Province 710049, ChinaNetwork structure is an important component of analysis in many parts of the natural and social sciences. Optimization of network structure in order to achieve specific goals has been a major research focus. The small-world network is known to have a high average clustering coefficient and a low average path length. Previous studies have introduced a series of models to generate small-world networks, but few focus on how to improve the efficiency of the generating process. In this paper, we propose a genetic simulated annealing (GSA) algorithm to improve the efficiency of transforming other kinds of networks into small-world networks by adding edges, and we apply this algorithm to some experimental systems. In the process of using the GSA algorithm, the existence of hubs and disassortative structure is revealed.http://dx.doi.org/10.1155/2018/1453898
collection DOAJ
language English
format Article
sources DOAJ
author Haifeng Du
Jiarui Fan
Xiaochen He
Marcus W. Feldman
spellingShingle Haifeng Du
Jiarui Fan
Xiaochen He
Marcus W. Feldman
A Genetic Simulated Annealing Algorithm to Optimize the Small-World Network Generating Process
Complexity
author_facet Haifeng Du
Jiarui Fan
Xiaochen He
Marcus W. Feldman
author_sort Haifeng Du
title A Genetic Simulated Annealing Algorithm to Optimize the Small-World Network Generating Process
title_short A Genetic Simulated Annealing Algorithm to Optimize the Small-World Network Generating Process
title_full A Genetic Simulated Annealing Algorithm to Optimize the Small-World Network Generating Process
title_fullStr A Genetic Simulated Annealing Algorithm to Optimize the Small-World Network Generating Process
title_full_unstemmed A Genetic Simulated Annealing Algorithm to Optimize the Small-World Network Generating Process
title_sort genetic simulated annealing algorithm to optimize the small-world network generating process
publisher Hindawi-Wiley
series Complexity
issn 1076-2787
1099-0526
publishDate 2018-01-01
description Network structure is an important component of analysis in many parts of the natural and social sciences. Optimization of network structure in order to achieve specific goals has been a major research focus. The small-world network is known to have a high average clustering coefficient and a low average path length. Previous studies have introduced a series of models to generate small-world networks, but few focus on how to improve the efficiency of the generating process. In this paper, we propose a genetic simulated annealing (GSA) algorithm to improve the efficiency of transforming other kinds of networks into small-world networks by adding edges, and we apply this algorithm to some experimental systems. In the process of using the GSA algorithm, the existence of hubs and disassortative structure is revealed.
url http://dx.doi.org/10.1155/2018/1453898
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AT jiaruifan ageneticsimulatedannealingalgorithmtooptimizethesmallworldnetworkgeneratingprocess
AT xiaochenhe ageneticsimulatedannealingalgorithmtooptimizethesmallworldnetworkgeneratingprocess
AT marcuswfeldman ageneticsimulatedannealingalgorithmtooptimizethesmallworldnetworkgeneratingprocess
AT haifengdu geneticsimulatedannealingalgorithmtooptimizethesmallworldnetworkgeneratingprocess
AT jiaruifan geneticsimulatedannealingalgorithmtooptimizethesmallworldnetworkgeneratingprocess
AT xiaochenhe geneticsimulatedannealingalgorithmtooptimizethesmallworldnetworkgeneratingprocess
AT marcuswfeldman geneticsimulatedannealingalgorithmtooptimizethesmallworldnetworkgeneratingprocess
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