An Improved Immune Genetic Algorithm for the Optimization of Enterprise Information System based on Time Property

In order to optimize enterprise information system’s structure and improve its performance, this paper deals with the structure optimization problem based on an improved immune genetic algorithm (IIGA). First, a new immune genetic algorithm (IGA) is proposed, i.e., IIGA, which can overcome traditional genetic algorithm (GA)’s deficiency of slow convergence. In the new IGA, Niche algorithm is used to accelerate convergence speed, and measures such as convergence function, and “noise” chromosome are proposed to avoid Niche algorithm’s deficiency of premature convergence. Then the structure and time property of enterprise information system (EIS) are discussed. And then optimization model of EIS structure is given. Finally, the IIGA and its application in EIS structure optimization are exemplified, and by comparing with self-adaptive Genetic Algorithm (SAGA) and traditional GA, the results verified IIGA’s better convergence speed and optimization ability .

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