Assessment of Ic Clustering Evolution by Using a Novel Diffusion Model and a Genetic Algorithm

The evolution of industrial clusters is a critical factor in the strategic development of locations for high-tech industries. Most previous studies have neglected the quantitative aspects of industrial clustering and their access to crucial data may have been limited. This work developed a novel diffusion model to illustrate the extension of IC clusters from Taiwan to China and included foreign direct investment (FDI) as a quantitative indicator of industrial clustering. We also modified the conventional Bass model by incorporating profitability as an incremental factor in conjunction with regulatory FDI limits as constraints for parameter estimation. A genetic algorithm was used with the fourth-order Runge-Kutta algorithm for parameter optimization. Finally, t-statistics were used to compare clustering features among three IC sub-industries: design, packaging/testing and memory modules. Simulation results demonstrate negligible standard deviation in the optimized parameters, which confirms the reliability of our findings. Furthermore, comparison results demonstrate that the proposed model is more stable and accurate than the conventional Bass model. The proposed approach is applicable to other high-tech industries and other locations around the world.