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A Biologically Inspired Optimization Algorithm for Solving Fuzzy Shortest Path Problems with Mixed Fuzzy Arc Lengths

Zhang, Xiaoge; Wang, Qing; Adamatzky, Andrew; Chan, Felix T.S.; Mahadevan, Sankaran; Deng, Yong

Authors

Xiaoge Zhang

Qing Wang

Felix T.S. Chan

Sankaran Mahadevan

Yong Deng



Abstract

© 2014, Springer Science+Business Media New York. The shortest path problem is among fundamental problems of network optimization. Majority of the optimization algorithms assume that weights of data graph’s edges are pre-determined real numbers. However, in real-world situations, the parameters (costs, capacities, demands, time) are not well defined. The fuzzy set has been widely used as it is very flexible and cost less time when compared with the stochastic approaches. We design a bio-inspired algorithm for computing a shortest path in a network with various types of fuzzy arc lengths by defining a distance function for fuzzy edge weights using α cuts. We illustrate effectiveness and adaptability of the proposed method with numerical examples, and compare our algorithm with existing approaches.

Journal Article Type Article
Publication Date Jan 1, 2014
Journal Journal of Optimization Theory and Applications
Print ISSN 0022-3239
Electronic ISSN 1573-2878
Publisher Springer Verlag
Peer Reviewed Peer Reviewed
Volume 163
Issue 3
Pages 1049-1056
APA6 Citation Zhang, X., Wang, Q., Adamatzky, A., Chan, F. T., Mahadevan, S., & Deng, Y. (2014). A Biologically Inspired Optimization Algorithm for Solving Fuzzy Shortest Path Problems with Mixed Fuzzy Arc Lengths. Journal of Optimization Theory and Applications, 163(3), 1049-1056. https://doi.org/10.1007/s10957-014-0542-6
DOI https://doi.org/10.1007/s10957-014-0542-6
Keywords shortest path, fuzzy numbers, bio-inspired, optimization
Publisher URL http://dx.doi.org/10.1007/s10957-014-0542-6