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Bee Colony Optimization with Applications in Transportation Engineering

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Advances in Swarm Intelligence

Part of the book series: Studies in Computational Intelligence ((SCI,volume 1054))

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Abstract

Metaheuristics turned out to be a dominant tool for solving difficult combinatorial optimization problems. Between them, a group of biologically motivated algorithms can be identified. The Bee Colony Optimization (BCO) method, which uses collective intelligence applied by honey bees, during the nectar gathering process, is one of them. The basic idea at the back of the BCO is to create a multi-agent system (a colony of artificial bees) competent to effectively discover solutions to difficult combinatorial optimization problems. The artificial bee colony performs to a certain degree alike, and in other parts differently, from bees in nature. The analysts specify the possible agents’ behavior and simulate the relations between them (how artificial bees interact with each other). The model and related software imitate how artificial bees execute their actions. The BCO is a stochastic, random-search population-based method. The BCO uses likeness among how bees in nature search for nectar, and how optimization algorithms look for an optimum in combinatorial optimization problems under study. Two variations of the BCO algorithm can be differentiated easily: constructive BCO and the alternative based on the solutions improvement idea. This chapter offers a taxonomy and analysis of the research results accomplished using both variants of BCO to model transportation engineering processes. Thus far, BCO has effectively been used to various real-life traffic control and transportation planning problems: the vehicle routing problem, the ride-matching problem, the traffic sensor location problem on highways, inspection station location problem, the p-center problem, disruption mitigation on public transit, pre-timed control for isolated intersections, area-wide urban traffic control, etc. The main goal of this text is to notify colleagues with the key principles of Bee Colony Optimization, as well as to denote new possible BCO uses in traffic control and transportation planning.

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Acknowledgements

This work was supported by the Serbian Ministry of Education, Science and Technological Development, through University of Belgrade, Faculty of Transport and Traffic Engineering.

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Authors and Affiliations

  1. University of Belgrade, Faculty of Transport and Traffic Engineering, Vojvode Stepe 305, 11000, Belgrade, Serbia

    Dušan Teodorović, Miloš Nikolić, Milica Šelmić & Ivana Jovanović

Authors
  1. Dušan Teodorović
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  2. Miloš Nikolić
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  3. Milica Šelmić
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  4. Ivana Jovanović
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Corresponding author

Correspondence to Miloš Nikolić .

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Editors and Affiliations

  1. Department of CS and Engineering, National Institute Of Technology Silchar, Cachar, Assam, India

    Anupam Biswas

  2. Department of Industrial Engineering, Pamukkale Üniversitesi Hastaneleri, Pamukkale, Turkey

    Can B. Kalayci

  3. Centre for AI Research and Optimisation, Torrens University Australia, Brisbane, QLD, Australia

    Seyedali Mirjalili

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Teodorović, D., Nikolić, M., Šelmić, M., Jovanović, I. (2023). Bee Colony Optimization with Applications in Transportation Engineering. In: Biswas, A., Kalayci, C.B., Mirjalili, S. (eds) Advances in Swarm Intelligence. Studies in Computational Intelligence, vol 1054. Springer, Cham. https://doi.org/10.1007/978-3-031-09835-2_7

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