The risk programming of virtual enterprises based on algorithms of Min / Max / Mean / Random-PSO

Min Huang; Xuejing Wu; Xingwei Wang; W. H. Ip; Kai Leung Yung

doi:10.1109/ICIEA.2006.257188

The risk programming of virtual enterprises based on algorithms of Min / Max / Mean / Random-PSO

Min Huang, Xuejing Wu, Xingwei Wang, W. H. Ip, Kai Leung Yung

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

Abstract

The classical Particle Swarm Optimization (PSO) is an effective method to find the extreme values of continuous functions, however its application in discrete space is still premature. In this text, four algorithms of PSO are proposed to solve the problem of combinatorial optimization. They are designed according to the different reactions between particle extremum and overall extremum and help to solve the problem of risk programming of virtual enterprises. Among these four algorithms of PSO, namely Min-PSO, Max-PSO, Mean-PSO, and Random-PSO, Random-PSO is concluded as the best after comparisons. Meanwhile, multi-level fuzzy synthetic evaluation is integrated to assess the overall risk level. Simulation analysis suggested that PSO is a simple but effective algorithm to solve the problem of combinatorial optimization.

Original language	English
Title of host publication	2006 1st IEEE Conference on Industrial Electronics and Applications
DOIs	https://doi.org/10.1109/ICIEA.2006.257188
Publication status	Published - 1 Dec 2006
Event	2006 1st IEEE Conference on Industrial Electronics and Applications, ICIEA 2006 - Singapore, Singapore Duration: 24 May 2006 → 26 May 2006

Conference

Conference	2006 1st IEEE Conference on Industrial Electronics and Applications, ICIEA 2006
Country/Territory	Singapore
City	Singapore
Period	24/05/06 → 26/05/06

ASJC Scopus subject areas

Electrical and Electronic Engineering
Industrial and Manufacturing Engineering

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10.1109/ICIEA.2006.257188

Cite this

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title = "The risk programming of virtual enterprises based on algorithms of Min / Max / Mean / Random-PSO",

abstract = "The classical Particle Swarm Optimization (PSO) is an effective method to find the extreme values of continuous functions, however its application in discrete space is still premature. In this text, four algorithms of PSO are proposed to solve the problem of combinatorial optimization. They are designed according to the different reactions between particle extremum and overall extremum and help to solve the problem of risk programming of virtual enterprises. Among these four algorithms of PSO, namely Min-PSO, Max-PSO, Mean-PSO, and Random-PSO, Random-PSO is concluded as the best after comparisons. Meanwhile, multi-level fuzzy synthetic evaluation is integrated to assess the overall risk level. Simulation analysis suggested that PSO is a simple but effective algorithm to solve the problem of combinatorial optimization.",

author = "Min Huang and Xuejing Wu and Xingwei Wang and Ip, {W. H.} and Yung, {Kai Leung}",

year = "2006",

month = dec,

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doi = "10.1109/ICIEA.2006.257188",

language = "English",

isbn = "078039514X",

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Huang, M, Wu, X, Wang, X, Ip, WH & Yung, KL 2006, The risk programming of virtual enterprises based on algorithms of Min / Max / Mean / Random-PSO. in 2006 1st IEEE Conference on Industrial Electronics and Applications., 4025806, 2006 1st IEEE Conference on Industrial Electronics and Applications, ICIEA 2006, Singapore, Singapore, 24/05/06. https://doi.org/10.1109/ICIEA.2006.257188

The risk programming of virtual enterprises based on algorithms of Min / Max / Mean / Random-PSO. / Huang, Min; Wu, Xuejing; Wang, Xingwei et al.
2006 1st IEEE Conference on Industrial Electronics and Applications. 2006. 4025806.

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

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T1 - The risk programming of virtual enterprises based on algorithms of Min / Max / Mean / Random-PSO

AU - Huang, Min

AU - Wu, Xuejing

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AU - Ip, W. H.

AU - Yung, Kai Leung

PY - 2006/12/1

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N2 - The classical Particle Swarm Optimization (PSO) is an effective method to find the extreme values of continuous functions, however its application in discrete space is still premature. In this text, four algorithms of PSO are proposed to solve the problem of combinatorial optimization. They are designed according to the different reactions between particle extremum and overall extremum and help to solve the problem of risk programming of virtual enterprises. Among these four algorithms of PSO, namely Min-PSO, Max-PSO, Mean-PSO, and Random-PSO, Random-PSO is concluded as the best after comparisons. Meanwhile, multi-level fuzzy synthetic evaluation is integrated to assess the overall risk level. Simulation analysis suggested that PSO is a simple but effective algorithm to solve the problem of combinatorial optimization.

AB - The classical Particle Swarm Optimization (PSO) is an effective method to find the extreme values of continuous functions, however its application in discrete space is still premature. In this text, four algorithms of PSO are proposed to solve the problem of combinatorial optimization. They are designed according to the different reactions between particle extremum and overall extremum and help to solve the problem of risk programming of virtual enterprises. Among these four algorithms of PSO, namely Min-PSO, Max-PSO, Mean-PSO, and Random-PSO, Random-PSO is concluded as the best after comparisons. Meanwhile, multi-level fuzzy synthetic evaluation is integrated to assess the overall risk level. Simulation analysis suggested that PSO is a simple but effective algorithm to solve the problem of combinatorial optimization.

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The risk programming of virtual enterprises based on algorithms of Min / Max / Mean / Random-PSO

Abstract

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