Skip to main content

Advertisement

Springer Nature Link
Log in

Quantum ant colony optimization algorithm for AGVs path planning based on Bloch coordinates of pheromones

  • Published:
Natural Computing Aims and scope Submit manuscript

Abstract

In this work, a novel quantum ant colony optimization algorithm for automated guided vehicles (AGVs) path planning based on Bloch coordinates of pheromones is proposed. In consideration of the difficulty in solving the AGVs path planning problem because of NP-hard computational complexity, this approach combines the advantages of quantum theory and ant colony algorithm to obtain feasible, conflict-free, and optimal paths. To expand the search space, the pheromones on paths are coded according to Bloch coordinates. To make full use of the pheromones of three-dimensional Bloch coordinates, they are chosen with certain probabilities in accordance with the paths they obtained. Repulsions among AGVs are supposed to exist to avoid conflicts. A repulsion factor is employed in the state transition rule to increase the space–time distance among AGVs as much as possible. We compare the performance of the proposed algorithm with those of the other three methods in simulation of AGVs path planning at an automated container terminal. Simulation results illustrate the superiority of the proposed algorithm.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

We’re sorry, something doesn't seem to be working properly.

Please try refreshing the page. If that doesn't work, please contact support so we can address the problem.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  • Balaprakash P, Birattari M, Stützle T, Yuan Z, Dorigo M (2009) Estimation-based ant colony optimization and local search for the probabilistic traveling salesman problem. Swarm Intell 3(3):223–242

    Article  MATH  Google Scholar 

  • Chen X, Xia X, Yu R (2013) Quantum ant colony algorithm based on Bloch coordinates. J Comput 8(6):405–412

    Google Scholar 

  • Dorigo M (1992) Ottimizzazione, apprendimento automatico, ed algoritmi basati su metafora naturale (optimization, learning and natural algorithms). Dissertation, Politecnico di Milano, Italy

  • Evers JJM, Koppers SAJ (1996) Automated guided vehicle traffic control at a container terminal. Transp Res A-Pol 30(1):21–34

    Google Scholar 

  • Fanti MP, Mangini AM, Pedroncelli G, Ukovich W (2018) A decentralized control strategy for the coordination of AGV systems. Control Eng Pract 70:86–97

    Article  Google Scholar 

  • Fazlollahtabar H, Saidi-Mehrabad M (2015) Methodologies to optimize automated guided vehicle scheduling and routing problems: a review study. J Intell Robot Syst 77(3–4):525–545

    Article  Google Scholar 

  • Forcael E, González V, Orozco F, Vargas S, Pantoja A, Moscoso P (2014) Ant colony optimization model for tsunamis evacuation routes. Comput-Aided Civil Infrastruct Eng 29(10):723–737

    Article  Google Scholar 

  • Gigliotta O, Mirolli M, Nolfi S (2014) Communication based dynamic role allocation in a group of homogeneous robots. Nat Comput 13(3):391–402

    Article  MathSciNet  Google Scholar 

  • Grower LK (1996) A fast quantum mechanics algorithm for database search. In: Proc of the 28th annual ACM symp on theory of computing, New York, USA, pp 212–219

  • Han KH, Kim JH (2002) Quantum-inspired evolutionary algorithm for a class of combinatorial optimization. IEEE Trans Evol Comput 6(6):580–593

    Article  MathSciNet  Google Scholar 

  • Han KH, Kim JH (2004) Quantum-inspired evolutionary algorithms with a new termination criterion. IEEE Trans Evol Comput 8(2):156–169

    Article  MathSciNet  Google Scholar 

  • Héctor JC, Iris FAV, Kees JR (2014) Transport operations in container terminals: literature overview, trends, research directions and classification scheme. Eur J Oper Res 236:1–13

    Article  MATH  Google Scholar 

  • Kim KH, Jeon SM, Ryu KR (2006) Deadlock prevention for automated guided vehicles in automated container terminals. OR Spectr 28:659–679

    Article  MATH  Google Scholar 

  • Leng YQ, Yu C, Zhang W, Zhang Y, He X, Zhou WJ (2017) Task-oriented hierarchical control architecture for swarm robotic system. Nat Comput 16(4):579–596

    Article  MathSciNet  Google Scholar 

  • Li P, Li S (2008) Quantum-inspired evolutionary algorithm for continuous space optimization based on Bloch coordinates of qubits. Neurocomputing 72(1):581–591

    Article  Google Scholar 

  • Li J, Zhao S, Lu A (2015) Quantum ant colony optimization algorithm based on Bloch spherical search. Int J Eng Sci 4(4):41–51

    Google Scholar 

  • Li Q, Pogromsky A, Adriaansen T, Udding JT (2016) A control of collision and deadlock avoidance for automated guided vehicles with a fault-tolerance capability. Int J Adv Robot Syst 13(2):1–24

    Article  Google Scholar 

  • Liu M, Zhang F, Ma Y, Pota HR, Shen W (2016) Evacuation path optimization based on quantum ant colony algorithm. Adv Eng Inform 30(3):259–267

    Article  Google Scholar 

  • Miyamoto T, Inoue K (2016) Local and random searches for dispatch and conflict-free routing problem of capacitated AGV systems. Comput Ind Eng 91:1–9

    Article  Google Scholar 

  • Nacera B, Bouabdellah K, Hafid H (2016) Cooperation between intelligent autonomous vehicles to enhance container terminal operations. J Innov Digital Ecosyst 3:22–29

    Article  Google Scholar 

  • Nishi T, HiranakaY Grossmann IE (2011) A bilevel decomposition algorithm for simultaneous production scheduling and conflict-free routing for automated guided vehicles. Comput Oper Res 38(5):876–888

    Article  MathSciNet  MATH  Google Scholar 

  • Ohta A, Goto H, Matsuzawa T, Takimoto M, Kambayashi Y, Takeda M (2016) An improved evacuation guidance system based on ant colony optimization. In: 2016 Intelligent and evolutionary systems, proceedings in adaptation, learning and optimization, pp 15–27

  • Park JH, Kim HJ, Lee C (2009) Ubiquitous software controller to prevent deadlocks for automated guided vehicle systems in a container port terminal environment. J Intell Manuf 20(3):321–325

    Article  Google Scholar 

  • Roy D, Gupta A, Koster RBM (2016) A non-linear traffic flow-based queuing model to estimate container terminal throughput with AGVs. Int J Prod Res 54(2):472–493

    Article  Google Scholar 

  • Saidi-Mehrabad M, Dehnavi-Arani S, Evazabadian F, Mahmoodian V (2015) An Ant Colony Algorithm (ACA) for solving the new integrated model of job shop scheduling and conflict-free routing of AGVs. Comput Ind Eng 86:2–13

    Article  Google Scholar 

  • Shao S, Xia Z, Chen G, Zhang J (2014) A new scheme of multiple automated guided vehicle system for collision and deadlock free. In: 2014 IEEE international conference on information science & technology, pp 606–610

  • Shor PW (1994) Algorithms for quantum computation. Diserete logarithms and factoring. In: 1994 Proceedings of the 35th annual symposium on foundations of computer science, pp 124–134

  • Smolic-Rocak N, Bogdan S, Kovacic Z, Petrovic T (2010) Time window based dynamic routing in multi-AGV systems. IEEE Trans Autom Sci Eng 7(1):151–155

    Article  Google Scholar 

  • Vivaldinni K, Rocha LF, Martarelli NJ, Becker M, Moreira AP (2016) Integrated tasks assignment and routing for the estimation of the optimal number of AGVS. Int J Adv Manuf Technol 82:719–736

    Article  Google Scholar 

  • Wang L, Niu Q, Fei M (2007) A novel quantum ant colony optimization algorithm. Bio-Insp Comput Intell Appl 4688:277–286

    Google Scholar 

  • Wang L, Niu Q, Fei M (2008) A novel quantum ant colony optimization algorithm and its application to fault diagnosis. Trans Inst Measur Control 30(4):313–329

    Article  Google Scholar 

  • Wei X (2017) A multi-objective ACO for operating room scheduling optimization. Nat Comput 16(4):607–617

    Article  MathSciNet  Google Scholar 

  • Wu J, Yang L, Peng L, Liu F (2013) Privacy-preserving data mining algorithm quantum ant colony optimization. Appl Math Inform Sci 7(3):1129–1135

    Article  Google Scholar 

  • Wu J, Yang L, Li T, Zhang C, Li Z (2015) Rule-based fuzzy classifier based on quantum ant optimization algorithm. J Intell Fuzzy Syst 29(6):2365–2371

    Article  MATH  Google Scholar 

  • Xin J, Negenborn R, Corman F, Corman F, Lodewijks G (2015) Control of interacting machines in automated container terminals using a sequential planning approach for collision avoidance. Transp Res C-Emer 60:377–396

    Article  Google Scholar 

  • Yang L, Wu J (2011) Quantum ant colony optimization algorithm on collision detection. In: 2011 International conference on computational and information sciences, pp 341–344

  • Yang Z, Osta JPV, Veen BV, Krevelen RV, Klavere RV, Stam A, Kok J, Back T, Emmerich M (2017) Dynamic vehicle routing with time windows in theory and practice. Nat Comput 16(1):1–16

    Article  MathSciNet  MATH  Google Scholar 

  • Zhang F, Liu M, Zhou Z, Shen WM (2013) Quantum ant colony algorithm-based emergency evacuation path choice algorithm. In: 2013 IEEE international conference on computer supported cooperative work in design, pp 576–580

  • Zhang S, Yang Y, Liang C, Xu B, Li J (2017) Optimal control of multiple AGV path conflict in automated terminals. J Transp Syst Eng Inf Technol 17(2):83–89 (in Chinese)

    Google Scholar 

  • Zuo S, Ou Y, Zhu X (2017) A path planning framework for indoor low-cost mobile robots. In: 2017 IEEE international conference on information and automation, pp 18–23

Download references

Acknowledgements

This work is supported by the Ministry of Education of Humanities and Social Science Project (Nos. 15YJC630145, 15YJC630059), the Soft Science Research Key Project of Shanghai Science and Technology Innovation Action Plan (No. 18692105500). Here we would like to express our gratitude to them.

Author information

Authors and Affiliations

  1. Merchant Marine College, Shanghai Maritime University, Shanghai, China

    Junjun Li & Huafeng Wu

  2. Institute of Logistics Science & Engineering, Shanghai Maritime University, Shanghai, China

    Bowei Xu & Yongsheng Yang

Authors
  1. Junjun Li
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Bowei Xu
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Yongsheng Yang
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Huafeng Wu
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Bowei Xu.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Li, J., Xu, B., Yang, Y. et al. Quantum ant colony optimization algorithm for AGVs path planning based on Bloch coordinates of pheromones. Nat Comput 19, 673–682 (2020). https://doi.org/10.1007/s11047-018-9711-0

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11047-018-9711-0

Keywords