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Ant Colony Optimization Algorithm for Fuzzy Transport Modelling: InterCriteria Analysis

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Recent Advances in Computational Optimization (WCO 2020)

Part of the book series: Studies in Computational Intelligence ((SCI,volume 986))

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Abstract

Public transport plays an important role in our live. It is very important to have a reliable service. Up to 1000 km, trains and buses play the main role in the public transport. The number of the people and which kind of transport they prefer is important information for transport operators. In this paper is proposed algorithm for transport modeling and passenger flow, based on Ant Colony Optimization method. The problem is described as multi-objective optimization problem. There are two optimization purposes: minimal transportation time and minimal price. Some fuzzy element is included. When the price is in a predefined interval it is considered the same. Similar for the starting traveling time. The aim is to show how many passengers will prefer train and how many will prefer buses according their preferences, the price or the time. The InterCriteria Analysis (ICrA) is applied over numerical results obtained from ACO algorithm in order to estimate the algorithm performance. The ICrA results show that the proposed ACO algorithm performs very well.

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Acknowledgements

Work presented here is partially supported by the National Scientific Fund of Bulgaria under grant DFNI DN12/5 “Efficient Stochastic Methods and Algorithms for Large-Scale Problems”, Grant No BG05M2OP001-1.001-0003, financed by the Science and Education for Smart Growth Operational Program.

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Authors and Affiliations

  1. Institute of Information and Communication Technology, Bulgarian Academy of Sciences, Sofia, Bulgaria

    Stefka Fidanova

  2. Institute of Biophysics and Biomedical Engineering, Bulgarian Academy of Sciences, Sofia, Bulgaria

    Olympia Roeva

  3. System Research Institute, Polish Academy of Sciences Warsaw and Management Academy, Warsaw, Poland

    Maria Ganzha

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  1. Stefka Fidanova
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  2. Olympia Roeva
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  3. Maria Ganzha
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Correspondence to Stefka Fidanova .

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  1. Institute of Information and Communication Technology, Bulgarian Academy of Sciences, Sofia, Bulgaria

    Stefka Fidanova

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Fidanova, S., Roeva, O., Ganzha, M. (2022). Ant Colony Optimization Algorithm for Fuzzy Transport Modelling: InterCriteria Analysis. In: Fidanova, S. (eds) Recent Advances in Computational Optimization. WCO 2020. Studies in Computational Intelligence, vol 986. Springer, Cham. https://doi.org/10.1007/978-3-030-82397-9_6

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