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 proceedingConference article published in proceeding or bookAcademic researchpeer-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 languageEnglish
Title of host publication2006 1st IEEE Conference on Industrial Electronics and Applications
DOIs
Publication statusPublished - 1 Dec 2006
Event2006 1st IEEE Conference on Industrial Electronics and Applications, ICIEA 2006 - Singapore, Singapore
Duration: 24 May 200626 May 2006

Conference

Conference2006 1st IEEE Conference on Industrial Electronics and Applications, ICIEA 2006
CountrySingapore
CitySingapore
Period24/05/0626/05/06

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Industrial and Manufacturing Engineering

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