A Hybrid Algorithm With Particle Swarm Optimization for Partner Selection Problem in Virtual Enterprise

Partner selection is an inherent problem in VE. Minimizing risk in partner selection and ensuring the due date of the project are keys to ensure the success of the VE. In this paper, the description of a partner selection problem is introduced. The HPSO with simulated annealing (SA) is proposed to solve the complex combinatorial optimization problems. 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. Compared with the PSO and SA, the HPSO has better synthetic performance in both the computation speed and optimality. The computation results suggest its potential to solve the practical partner selection and sub-project management problems