A fuzzy clustering iterative model using chaotic differential evolution algorithm for evaluating flood disaster

Authors:
Yaoyao He;Jianzhong Zhou;Pangao Kou;Ning Lu;Qiang Zou
Affiliations:
School of Management, Hefei University of Technology, Hefei 230009, China and Key Laboratory of Process Optimization and Intelligent Decision-Making, Ministry of Education, Hefei, China and School ...;School of Hydropower and Information Engineering, Huazhong University of Science and Technology, Wuhan 430074, China;School of Hydropower and Information Engineering, Huazhong University of Science and Technology, Wuhan 430074, China;School of Hydropower and Information Engineering, Huazhong University of Science and Technology, Wuhan 430074, China and School of Automation, Wuhan University of Technology, Wuhan, Hubei, China;School of Hydropower and Information Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
Venue:
Expert Systems with Applications: An International Journal
Year:
2011

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Abstract

Flood disaster is a kind of frequent natural hazards. The objective of flood disaster evaluation is to establish hazard assessment model for managing flood and preventing disaster. Base on the chaotic optimization theory, this paper proposes a chaotic differential evolution algorithm to solve a fuzzy clustering iterative model for evaluating flood disaster. By using improved logistic chaotic map and penalty function, the objective function can be solved more perfectly. Two practical flood disaster cases have been taken into account so as to test the effect of novel hybrid method. Simulation results and comparisons show that the chaotic differential evolution algorithm is competitive and stable in performance with simple differential evolution and other optimization approaches presented in literatures.