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

Xiaoge Zhang, Qing Wang, Andrew Adamatzky, Tung Sun Chan, Sankaran Mahadevan, Yong Deng

Research output: Journal article publicationJournal articleAcademic researchpeer-review

27 Citations (Scopus)

Abstract

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.
Original languageEnglish
Pages (from-to)1049-1056
Number of pages8
JournalJournal of Optimization Theory and Applications
Volume163
Issue number3
DOIs
Publication statusPublished - 1 Jan 2014

Keywords

  • Bio-inspired
  • Fuzzy numbers
  • Optimization
  • Shortest path

ASJC Scopus subject areas

  • Control and Optimization
  • Management Science and Operations Research
  • Applied Mathematics

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