THE USE OF A GIS –BASED GENETIC ALGORITHM IN MULTI-OBJECTIVE QUASI-OPTIMIZATION IN URBAN ROUTE SELECTION

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PAHLAVANI P.; DELAVAR M.R.; SAMADZADEGAN F.

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Journal: Year:2007 | Volume:40 | Issue:8 (102) Page(s): 1077-1091

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Title

THE USE OF A GIS –BASED GENETIC ALGORITHM IN MULTI-OBJECTIVE QUASI-OPTIMIZATION IN URBAN ROUTE SELECTION

Author(s)

PAHLAVANI P. | DELAVAR M.R. | SAMADZADEGAN F. | Issue Writer Certificate

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Abstract

Shortest path selection is known as one of the most important functions in geo-spatial information system (GIS) network analysis. It is classified into two types; one is single criterion shortest path problems (SSP) and the other is multi-criteria shortest path problems (MSPP). The current GIS usually confronts the University College of Engineering, University of Tehran 5 lack of powerful tools in spatial analysis for MSPP, so the suitable solution for MSPP is one of the current major needs for GIS. Urban multi-objective optimized routing problems are called both NP-Hard problems and one of the branches of MSPP.Algorithmic and approximation schemes are available to solve MSPP; unless they meet some problems such as the complexity of these approaches often prohibits their implementation in large urban route networks, they are limited to a few numbers of criteria, each criterion is independent with no weight of importance and it is needed to select one solution from Pareto-optimal solutions. These categories of optimization problems can not be solved practically and therefore an approximation of the general optimum has to be considered.During the last decades, genetic algorithm (GA) has been one of the most important references in artificial intelligent (AI) for multi-objective complex optimization problems. By proposing a novel approach on the bases of route guidance navigation system principles, virus theory (viral infection and local infection) and by GIS and GA utilization, this paper is come up to rate search improvement in urban multi-objective routing problems on real networks with multiple dependent criteria. For the purpose of the innovative approach capability proof, it has been discussed the achieving results in each step of approach improvement from generic GA to proposed an innovative approach in a case of four experiments. As in the first experiment, we have done generic GA design and implementation for quasi multi-objective optimal urban route selection. In the second experiment, generic GA is improved due to Minimal Generation Gap (MGG) model utilization. In the third experiment, virus theory and viral infection improved the second experiment and finally in the fourth experiment by adding local infection we have come up to third experiment improvement.Moreover, all these experiments have been implemented in a part of North-West of Tehran traffic network by considering and modeling four criteria of route including length, time, congestion and degree of difficulty.Accepting unlimited criteria, being "range (scale)-independent ranking" method, taking the "importance" of each objective chosen by driver, utilizing priority knowledge with viral infection and local infection, converging GA to "best compromise" solution and proposing a quality metric for assessing the best compromise solution are the major characteristics of this innovative approach. Furthermore, the achieved results ineach step of the algorithm improvement, from generic GA to novel approach are the evidences of high capability of the proposed method.

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APA: Copy

PAHLAVANI, P., DELAVAR, M.R., & SAMADZADEGAN, F.. (2007). THE USE OF A GIS –BASED GENETIC ALGORITHM IN MULTI-OBJECTIVE QUASI-OPTIMIZATION IN URBAN ROUTE SELECTION. JOURNAL OF FACULTY OF ENGINEERING (UNIVERSITY OF TEHRAN), 40(8 (102)), 1077-1091. SID. https://sid.ir/paper/14148/en

Vancouver: Copy

PAHLAVANI P., DELAVAR M.R., SAMADZADEGAN F.. THE USE OF A GIS –BASED GENETIC ALGORITHM IN MULTI-OBJECTIVE QUASI-OPTIMIZATION IN URBAN ROUTE SELECTION. JOURNAL OF FACULTY OF ENGINEERING (UNIVERSITY OF TEHRAN)[Internet]. 2007;40(8 (102)):1077-1091. Available from: https://sid.ir/paper/14148/en

IEEE: Copy

P. PAHLAVANI, M.R. DELAVAR, and F. SAMADZADEGAN, “THE USE OF A GIS –BASED GENETIC ALGORITHM IN MULTI-OBJECTIVE QUASI-OPTIMIZATION IN URBAN ROUTE SELECTION,” JOURNAL OF FACULTY OF ENGINEERING (UNIVERSITY OF TEHRAN), vol. 40, no. 8 (102), pp. 1077–1091, 2007, [Online]. Available: https://sid.ir/paper/14148/en

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