A hybrid algorithm based on PSO and simulated annealing and its applications for partner selection in virtual enterprise

  • Authors:

  • Fuqing Zhao;Qiuyu Zhang;Dongmei Yu;Xuhui Chen;Yahong Yang

  • Affiliations:

  • School of Computer and Communication, Lanzhou University of Technology, Lanzhou, P.R. China;School of Computer and Communication, Lanzhou University of Technology, Lanzhou, P.R. China;School of Computer and Communication, Lanzhou University of Technology, Lanzhou, P.R. China;School of Computer and Communication, Lanzhou University of Technology, Lanzhou, P.R. China;College of Civil Engineering, Lanzhou University of Techchnology, Lanzhou, P.R. China

  • Venue:
  • ICIC'05 Proceedings of the 2005 international conference on Advances in Intelligent Computing - Volume Part I
  • Year:
  • 2005

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Abstract

Partner selection is a very popular problem in the research of virtual organization and supply chain management, the key step in the formation of virtual enterprise is the decision making on partner selection. In this paper, a activity network based multi-objective partner selection model is put forward. Then a new heuristic algorithm based on particle swarm optimization(PSO) and simulated annealing(SA) is proposed to solve the multi-objective problem. PSO employs a collaborative population-based search, which is inspired by the social behavior of bird flocking. It combines local search(by self experience) and global search(by neighboring experience), possessing high search efficiency. SA employs certain probability to avoid becoming trapped in a local optimum and the search process can be controlled by the cooling schedule. The hybrid algorithm combines the high speed of PSO with the powerful ability to avoid being trapped in local minimum of SA. We compare the hybrid algorithm to both the standard PSO and SA models, the simulation results show that the proposed model and algorithm are effective.