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ACOp: An Algorithm Based on Ant Colony Optimization for Parking Slot Detection

  • Walter BalzanoEmail author
  • Silvia Stranieri
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 927)

Abstract

Ant Colony Optimization (ACO) is a known, largely employed paradigm for optimization algorithm. It is a bio-inspired approach following the real world ants beahvior in food search, that is traveling along the path having the highest pheromone. ACO algorithms provide a heuristic techinque to find global optima for an optimization problem, by using the global parameters of trail and pheromone that make a path more attractive than another. In this work, we focus on a problem that has serius impact on traffic congestion in VANETs: available parking slot detection. Despite ACO paradigm has been largely used in VANET field to address clusterization, routing, and communication failure, parking problem has never been handled with ant colony optimization. The main contribution of this paper is an innovative approach to the parking detection, that is formulated as an optimization problem and managed through ACO, and that provides, by means of opportune representation of the environment, a path that maximizes the number of available parking slot met.

Keywords

ACO VANET Optimization 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Naples University, Federico IINaplesItaly

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