Adaptation methodology of CBR for environmental emergency preparedness system based on an Improved Genetic Algorithm

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• 作者：Zhenliang Liao^a ; ^{zl_liao@tongji.edu.cn} ; Xuewei Mao^a ; Phillip M. Hannam^b ; Tingting Zhao^a ; ^c
• 关键词：Environmental emergency ; Improved Genetic Algorithm ; Case-Based Reasoning ; Adaptation
• 刊名：Expert Systems with Applications
• 出版年：2012
• 期刊代码：62_09574174
• 类别：et
• 出版时间：15 June, 2012
• 卷：39
• 期：8
• 页码：7029-7040
• 文件大小：878 K

摘要

Emergency preparedness enables us to effectively handle sudden environmental events. In previous research, we have proposed to develop environmental emergency preparedness systems employing Case-Based Reasoning (CBR) technology, though developing such a CBR system has been stifled by a deficiency of cases and difficulties of case adaptation. In this paper, an Improved Genetic Algorithm (IGA) is put forward to resolve the issue of adaptability, and thus simultaneously overcoming the deficiency of cases.

First we introduce the Frame method, which creates a case representation in accordance with the characteristics of, for instance, a sudden chemical leakage event and the system鈥檚 preparedness for treating this case. Then we present the principle of genetic algorithm (GA) for case adaptation. Next, we introduce an Improved Genetic Algorithm (IGA) that achieves case adaptation in the CBR system. The IGA overcomes simplex GA (SGA)鈥檚 defects including premature and slow convergence rate, and also enhances search efficiency for globally optimal solutions. The IGA employs technologies including the Multi-Factor Integrated Fitness Function, the Multi-Parameter Cascade Code method, the Small Section method for generation of an initial population, and Niche technology for genetic operations including selection, crossover, and mutation. The results of SGA and IGA performance testing are also presented. A prototype CBR-IGA environmental emergency preparedness system is developed and introduced, and its applicability is tested using a hypothetical ammonia leakage emergency in one district of Shanghai. The results indicate that the proposed IGA methodology can resolve the adaptation issue and expand the case base effectively in CBR systems for environmental emergency preparedness. Future research opportunities are discussed, including the potential to combine CBR, GA, and Back Propagation-Artificial Neural Network (BP-ANN) to alleviate additional challenges, such as the 鈥渟peed and accuracy鈥?of environmental emergency response.