Application of Improved Discrete Particle Swarm Algorithm in Partner Selection of Virtual Enterprise

Partner selection is a critical issue in the research of virtual enterprise. It is usually a combinatorial optimization problem. In this paper, an improved discrete particle swarm algorithm is proposed for the partner selection optimization. At first, an optimization model is presented, which includes the main crucial factors for partner selection, such as running cost, reaction time and failure risk. To decrease the probability of the particle swarm algorithm to trap in a local optimum, a new particle position updating formula is modified as follows. Particle velocity is divided into three regions and according the region a particle fall into, its position is correspondingly determined to keep unchanged or take value 0 and 1. As the iteration continues, these three regions can adaptively change their range to increase the global convergence. Numerical results have demonstrated the improved particle swarm algorithm show better performance in partner selection.An algorithm based on discrete binary particle swarm optimization (PSO) is then presented for the solution. A typical example is used to illustrate the effectiveness of the algorithm. The proposed optimization model and scheme provides a reference to partner selection in the actual VE operation.