Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B2, 299-304, 2016
https://doi.org/10.5194/isprs-archives-XLI-B2-299-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
 
07 Jun 2016
A MODIFIED GENETIC ALGORITHM FOR FINDING FUZZY SHORTEST PATHS IN UNCERTAIN NETWORKS
A. A. Heidari1 and M. R. Delavar2 1School of Surveying and Geospatial Eng., College of Eng., University of Tehran, North Kargar Ave., Tehran, Iran
2Center of Excellence in Geomatic Eng. in Disaster Management, School of Surveying and Geospatial Eng., College of Eng., University of Tehran, North Kargar Ave., Tehran, Iran
Keywords: Genetic Algorithm, Shortest Path Problem, Uncertainty, Quality Assessment, Optimization Abstract. In realistic network analysis, there are several uncertainties in the measurements and computation of the arcs and vertices. These uncertainties should also be considered in realizing the shortest path problem (SPP) due to the inherent fuzziness in the body of expert's knowledge. In this paper, we investigated the SPP under uncertainty to evaluate our modified genetic strategy. We improved the performance of genetic algorithm (GA) to investigate a class of shortest path problems on networks with vague arc weights. The solutions of the uncertain SPP with considering fuzzy path lengths are examined and compared in detail. As a robust metaheuristic, GA algorithm is modified and evaluated to tackle the fuzzy SPP (FSPP) with uncertain arcs. For this purpose, first, a dynamic operation is implemented to enrich the exploration/exploitation patterns of the conventional procedure and mitigate the premature convergence of GA technique. Then, the modified GA (MGA) strategy is used to resolve the FSPP. The attained results of the proposed strategy are compared to those of GA with regard to the cost, quality of paths and CPU times. Numerical instances are provided to demonstrate the success of the proposed MGA-FSPP strategy in comparison with GA. The simulations affirm that not only the proposed technique can outperform GA, but also the qualities of the paths are effectively improved. The results clarify that the competence of the proposed GA is preferred in view of quality quantities. The results also demonstrate that the proposed method can efficiently be utilized to handle FSPP in uncertain networks.
Conference paper (PDF, 1302 KB)


Citation: Heidari, A. A. and Delavar, M. R.: A MODIFIED GENETIC ALGORITHM FOR FINDING FUZZY SHORTEST PATHS IN UNCERTAIN NETWORKS, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B2, 299-304, https://doi.org/10.5194/isprs-archives-XLI-B2-299-2016, 2016.

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