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Dynamic routing with ant system and memory-based decision-making process

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Abstract

Dynamic routing is an essential tool for today’s cities. Dynamic routing problems can be solved by modelling them as dynamic optimization problems (DOPs). DOPs can be solved using Swarm Intelligence and specially ant colony optimization (ACO) algorithms. Although different versions of ACO have already been presented for DOPs, there are still limitations in preventing stagnation and premature convergence and increasing convergence rate. To address these issues, we present an in-memory pheromone trail and an algorithm based on it (named AS-gamma) in the framework of ACO. In-memory pheromone trail is effectively increasing diversity after a change in an environment. Results of experimenting AS-gamma in three scenarios on a real-world transportation network with different simulated traffic conditions demonstrated the effectiveness of the presented in-memory pheromone trail method. The advantages of AS-gamma over three existing DOP algorithms have been illustrated in terms of solutions quality. Offline performance and accuracy measures indicate that AS-gamma faces less stagnation, premature convergence and it is suitable for crowded environments.

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References

  • Abolhoseini S, Sadeghi Niaraki A (2016) Survey on certain and heuristic route finding algorithms in GIS (in Persian). GEJ 7(4):49–65

    Google Scholar 

  • Abolhoseini S, Sadeghi Niaraki A (2017) Dynamic multi-objective navigation in urban transportation network using ant colony optimization. Int J Transp Eng (IJTE) 6(1):49–64

    Google Scholar 

  • Aghdam AH, Alesheikh AA (2018) Predicting the future location of cars on urban street network by chaining spatial web services. IET Intel Transp Syst 12(8):793–800

    Article  Google Scholar 

  • Blokpoel R et al (2016) SUMO 2016–traffic, mobility, and logistics. DLR, Berlin

    Google Scholar 

  • Branke J (2012) Evolutionary optimization in dynamic environments illustrated. Springer, New York

    Google Scholar 

  • Chowdhury S et al (2019) A modified ant colony optimization algorithm to solve a dynamic traveling salesman problem: a case study with drones for wildlife surveillance. J Comput Des Eng 6(3):368–386

    Google Scholar 

  • Colorni A, Dorigo M, Maniezzo V (1992) Distributed optimization by ant colonies. Mit Press, Toward a practice of autonomous systems. In proceedings of the First European Conference on Artificial Life

  • de Almeida JPLS, Nakashima RT, Neves-Jr F, Arrudad LVR (2019) Bio-inspired on-line path planner for cooperative exploration of unknown environment by a Multi-Robot System. Robot Auton Syst 112:32–48

    Article  Google Scholar 

  • Dorigo M, Maniezzo V, Colorni A (1996) Ant system: optimization by a colony of cooperating agents. IEEE Trans Syst Man Cybern B 26(1):29–41

    Article  CAS  Google Scholar 

  • Dorigo M, Stützle T (2004) Ant colony optimization, illustrated edn. MIT Press, Cambridge

    Book  Google Scholar 

  • Dorigo M, Stützle T (2019) Ant colony optimization: overview and recent advances. In: Glover FW, Kochenberger GA (eds) Handbook of metaheuristics. Springer, Cham

    Google Scholar 

  • Eiben ÁE, Hinterding R, Michalewicz Z (1999) Parameter control in evolutionary algorithms. IEEE Trans Evol Comput 3(2):124–141

    Article  Google Scholar 

  • Euchi J, Yassine A, Chabchoub AH (2015) The dynamic vehicle routing problem: solution with hybrid metaheuristic approach. Swarm Evol Comput 21:41–53

    Article  Google Scholar 

  • Felicia A, Lakshmanan L (2016) Accident avoidance and privacy preserving navigation system in vehicular network. Int J Eng Sci 6(3):2266–2270

    Google Scholar 

  • Gao S et al (2016) Ant colony optimization with clustering for solving the dynamic location routing problem. Appl Math Comput 285:149–173

    Article  Google Scholar 

  • Garcia MP et al (2009) Path planning for autonomous mobile robot navigation with ant colony optimization and fuzzy cost function evaluation. Appl Soft Comput 9(3):1102–1110

    Article  Google Scholar 

  • Hwang I, Jang YJ (2019) Q (λ) learning-based dynamic route guidance algorithm for overhead hoist transport systems in semiconductor fabs. Int J Prod Res 58:1–23

    Google Scholar 

  • Jin Y, Branke J (2005) Evolutionary optimization in uncertain environments-a survey. IEEE Trans Evol Comput 9(3):303–317

    Article  Google Scholar 

  • Jin Y et al (2013) A framework for finding robust optimal solutions over time. Memet Comput 5(1):3–18

    Article  Google Scholar 

  • Li C et al (2008) Benchmark generator for CEC 2009 competition on dynamic optimization

  • Lima DA, Tinoco CR, Oliveira GM (2016) A cellular automata model with repulsive pheromone for swarm robotics in surveillance. In International Conference on Cellular Automata. Springer, Cham

  • Liu J-L (2005) Rank-based ant colony optimization applied to dynamic traveling salesman problems. Eng Optim 37(8):831–847

    Article  Google Scholar 

  • Manusov V, Matrenin P, Khasanzoda N (2018) Swarm algorithms in dynamic optimization problem of reactive power compensation units control. Int J Electr Comput Eng 9:2088–8708

    Google Scholar 

  • Mavrovouniotis M, Li C, Yang S (2017a) A survey of swarm intelligence for dynamic optimization: algorithms and applications. Swarm Evol Comput 33:1–17

    Article  Google Scholar 

  • Mavrovouniotis M, Müller FM, Yang S (2015) An ant colony optimization based memetic algorithm for the dynamic travelling salesman problem. In Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation. ACM

  • Mavrovouniotis M, Müller FM, Yang S (2017b) Ant colony optimization with local search for the dynamic travelling salesman problems. IEEE Trans Cybern 47(7):1743–1756

    Article  Google Scholar 

  • Mavrovouniotis M, Yang S (2011) Memory-based immigrants for ant colony optimization in changing environments. In European Conference on the Applications of Evolutionary Computation. Berlin, Heidelberg

  • Mavrovouniotis M, Yang S (2012) Ant colony optimization with memory-based immigrants for the dynamic vehicle routing problem. In 2012 IEEE Congress on Evolutionary Computation (CEC). IEEE

  • Mavrovouniotis M, Yang S (2013) Adapting the pheromone evaporation rate in dynamic routing problems. In European Conference on the applications of evolutionary computation. Springer, Berlin, Heidelberg, pp 606–615

  • Mavrovouniotis M, Yang S (2013) Ant colony optimization with immigrants schemes for the dynamic travelling salesman problem with traffic factors. Appl Soft Comput 13(10):4023–4037

    Article  Google Scholar 

  • Mavrovouniotis M, Yang S (2014) Ant colony optimization with self-adaptive evaporation rate in dynamic environments. IEEE

  • Mavrovouniotis M, Yang S (2014) Interactive and non-interactive hybrid immigrants schemes for ant algorithms in dynamic environments. In 2014 IEEE Congress on evolutionary computation (CEC). IEEE

  • Mavrovouniotis M, Yang S (2015) Ant algorithms with immigrants schemes for the dynamic vehicle routing problem. Inf Sci 294:456–477

    Article  Google Scholar 

  • Mavrovouniotis M, Yang S, Yao X (2014) Multi-colony ant algorithms for the dynamic travelling salesman problem. In Computational Intelligence in Dynamic and Uncertain Environments (CIDUE). IEEE

  • Montemanni R, Gambardella LM, Rizzoli AE, Donati AV (2005) Ant colony system for a dynamic vehicle routing problem. Journal Comb Optim 10(4):327–343

    Article  Google Scholar 

  • Morrison R (2003) Performance measurement in dynamic environments. In Proceedings of the 2003 Genetic and Evolutionary Computation Conference. Chicago

  • Prakasam A, Savarimuthu N (2019) Novel local restart strategies with hyper-populated ant colonies for dynamic optimization problems. Neural Comput Appl 31(1):63–76

    Article  Google Scholar 

  • Rizzoli A, Montemanni R, Lucibello E (2007) Ant colony optimization for real-world vehicle routing problems. Swarm Intell 1(2):135–151

    Article  Google Scholar 

  • Schaefer R, Cotta C, Kolodziej J, Rudolph G (2002) Parallel problem solving from nature-PPSN X. Lect Notes Comput Sci 6238:64–73

    Google Scholar 

  • Sharif M, Sadeghi-Niaraki A (2017) Ubiquitous sensor network simulation and emulation environments: a survey. J Netw Comput Appl 93:150

    Article  Google Scholar 

  • Singh P et al (2015) Dynamic shortest route finder using pgRouting for emergency management. Appl Geomat 7(4):255–262

    Article  Google Scholar 

  • Skinderowicz R (2016) Ant colony system with a restart procedure for TSP. In Proceedings of the International Conference on computational collective intelligence. Springer, Cham

  • Souza NLB, Lima DA (2019) Tabu search for the surveillance task optimization of a robot controlled by two-dimensional stochastic cellular automata ants model. In 2019 Latin American Robotics Symposium (LARS), 2019 Brazilian Symposium on Robotics (SBR) and 2019 Workshop on Robotics in Education (WRE). IEEE

  • Trojanowski K, Michalewicz Z (1999) Searching for optima in non-stationary environments. In Proceedings of the 1999 Congress on evolutionary computation, CEC 99. IEEE

  • Xiang X, Qiu J, Xiao J, Zhang X (2020) Demand coverage diversity based ant colony optimization for dynamic vehicle routing problems. Eng Appl Artif Intell 91:103582

    Article  Google Scholar 

  • Xu H, Pu P, Duan F (2018) Dynamic vehicle routing problems with enhanced ant colony optimization. Discret Dyn Nat Soc 2018:13

    Google Scholar 

  • Yang Z, Emmerich M, Bäck T (2015) Ant based solver for dynamic vehicle routing problem with time windows and multiple priorities. IEEE

  • Zhang S, Zhang Y (2018) A hybrid genetic and ant colony algorithm for finding the shortest path in dynamic traffic networks. Autom Control Comput Sci 52(1):67–76

    Article  Google Scholar 

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Authors and Affiliations

  1. Faculty of Geodesy and Geomatics Engineering, K. N. Toosi University of Technology, Tehran, Iran

    Sina Abolhoseini & Ali Asghar Alesheikh

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  1. Sina Abolhoseini
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  2. Ali Asghar Alesheikh
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Correspondence to Sina Abolhoseini.

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Abolhoseini, S., Alesheikh, A.A. Dynamic routing with ant system and memory-based decision-making process. Environ Syst Decis 41, 198–211 (2021). https://doi.org/10.1007/s10669-020-09788-7

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