Fusing genetic algorithm and Ant Colony Algorithm to optimize virtual enterprise partner selection problem

The partner selection in virtual enterprises organization is one of the key issues corporate enterprises experience nowadays. Based on the model of Ant Colony Optimization Algorithm (ACA) in virtual enterprise partner selection, in this paper, we fuse the genetic algorithm into ACA, called fusion algorithm, in order to improve the effect of the partner selection. The fusion algorithm has two steps: 1) it uses the GA to optimize the model of partner selection and takes advantages of rapid convergence of GA in initial search periods. 2) When GA search speed has become slow, the ACA takes over the search process, in which it uses the candidates produced by the GA as the seeds of pheromone used by ACA. By experimental comparison with GA optimization and ACA optimization, it shows that the fusion algorithm has performed better than the GA and ACA optimization, respectively, in both speed and accuracy under our selected numerical case. The fusion algorithm presented in this study may be applicable to similar business problems.