Enterprise information system structure optimization based on time property with improved immune genetic algorithm and binary tree

This paper deals with how to optimize enterprise information system (EIS) structure based on time property. Once the EIS structure is formally represented, the time property corresponding to EIS structure expression can be obtained. Thus, aiming at the minimum time property, the EIS structure can be optimized. To this end, first, the formal representation of EIS structure based on object-based knowledge mesh (OKM) and binary tree is proposed. Second, different time properties corresponding to various structures are defined and clarified. Then, the optimal model of EIS structure is constructed. And then, the EIS structure model is optimized by the improved immune genetic algorithm (IGA) based on binary tree, niche algorithm and self-adaptive operators, and the steps of the improved IGA are presented in detail as well. Finally, the EIS structure optimization based on time property is illustrated by an example, which verifies the proposed approach.

[1]  Richard Y. K. Fung,et al.  An immune-genetic algorithm for introduction planning of new products , 2009, Comput. Ind. Eng..

[2]  Rudolf Bayer,et al.  Symmetric binary B-Trees: Data structure and maintenance algorithms , 1972, Acta Informatica.

[3]  Li Chuan,et al.  A Novel Self-adaptive Immune Genetic Algorithm , 2008 .

[4]  Yan Hong-sen.  NEW APPROACH TO KNOWLEDGE REPRESENTATION FOR ADVANCED MANUFACTURING MODES , 2006 .

[5]  Raffaella Piccarreta,et al.  Binary trees for dissimilarity data , 2010, Comput. Stat. Data Anal..

[6]  Li Ru-yun.  Immune Genetic Algorithm Based on Group Division and Hybridization , 2008 .

[7]  Jinsung Kim,et al.  PID control of Reverse Osmosis desalination plant using Immune-Genetic Algorithm , 2009, 2009 ICCAS-SICE.

[8]  Abdelhamid Bouchachia,et al.  An Immune Genetic Algorithm for Software Test Data Generation , 2007, 7th International Conference on Hybrid Intelligent Systems (HIS 2007).

[9]  Zhi-gang Su,et al.  Immune genetic algorithm-based adaptive evidential model for estimating unmeasured parameter: Estimating levels of coal powder filling in ball mill , 2010, Expert Syst. Appl..

[10]  Hongji Yang,et al.  A formal method to software integration in reuse , 1996, Proceedings of 20th International Computer Software and Applications Conference: COMPSAC '96.

[11]  Kouji HAYAMIZU,et al.  Multiagent Architecture CONSORTS for Ubiquitous Computing Environment , 2004 .

[12]  GU Xing-sheng,et al.  Niche Particle Swarm Optimization Algorithm , 2007 .

[13]  David E. Goldberg,et al.  Genetic Algorithms with Sharing for Multimodalfunction Optimization , 1987, ICGA.

[14]  Marc Frappier,et al.  How to verify dynamic properties of information systems , 2004 .

[15]  Jen-Chih Lin,et al.  Reconfiguring binary tree structures in a faulty supercube with unbounded expansion , 2002, Parallel Comput..

[16]  Jim Alves-Foss,et al.  Formal Verification and Visualization of Security Policies , 2008, J. Comput..

[17]  Hong-Sen Yan,et al.  Automatic construction and optimization of knowledge mesh for self-reconfiguration of knowledgeable manufacturing system , 2012, Expert Syst. Appl..

[18]  Dong Hong-li,et al.  A Kind of New Immune Genetic Algorithm and Its Application , 2005 .

[19]  Yan Hong-sen.  Research of Automatic Construction of the Knowledge Mesh Based on the User's Functional Requirement , 2005 .

[20]  Wang Xing-yu.  DNA-Immune-Genetic Algorithm Based on Information Entropy , 2006 .

[21]  Jin Ye.  Optimization of network of block erection on dock using modified immune genetic algorithm , 2006 .

[22]  Cao Hai-wang.  Study on formal representation of enterprise information system based on object-based knowledge mesh , 2009 .