Authors: Zhentao Shi; Jianchao Zeng
Addresses: College of Electrical and Information Engineering, Lanzhou University of Technology, No. 85 Langongping, 730050 Lanzhou, China; Complex System and Computational Intelligence Laboratory, Taiyuan University of Science and Technology, No. 66 Waliu Road, 030024 Taiyuan, China ' Complex System and Computational Intelligence Laboratory, Taiyuan University of Science and Technology, No .66 Waliu Road, 030024 Taiyuan, China
Abstract: Based on the organisational behaviour theory and complex adaptive system method, the enterprise organisation behaviours are analysed for the firm dynamic growth evolution modelling. Further, the particle swarm optimisation algorithm is introduced to individual behaviour parameters optimising, so each individual can adjust their own behaviour parameters according to their own status and the surroundings, which makes agents more intelligent and adaptive than that in the previous research. Concretely, organisation behaviours in the firm are described at first. Then the firm growth evolution model is given and the group behaviour structure is also built up. Besides, the principles and evolution processes of the model are specifically presented and the firm growth evolution model is simulated. Finally, the simulation results are analysed in greater detail. The results illustrate that applying the particle swarm optimisation to the group behaviour has a strong guiding sense to the development of the enterprise.
Keywords: particle swarm optimisation; PSO; complex adaptive systems; organisational behaviour; dynamic growth evolution; modelling; firm growth; group behaviour; simulation; enterprise development.
International Journal of Wireless and Mobile Computing, 2013 Vol.6 No.3, pp.261 - 270
Available online: 06 Aug 2013 *Full-text access for editors Access for subscribers Purchase this article Comment on this article