Inverse Ant Algorithm

  • Jaymer M. JayomaEmail author
  • Bobby D. Gerardo
  • Ruji M. MedinaEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11248)


This paper presents a swarm optimization algorithm (SOA) which is specifically an enhanced version of the ant algorithm that solves shortest path problem. Ant Algorithm finds the shortest path through its pheromone deposits. However, its solutions are less effective if implemented in actual scenario like road traffic management and others because it stagnates when using large data. Variants of the ant algorithm where being developed to address the stagnation issue like Ant Colonization Optimization, Rank Based Ant Algorithm, Max-Min Ant Algorithm, Inverted Ant Colonization Algorithm and etc. However, each development failed to integrate real-world scenarios that can contribute to stagnation when applied to traffic management. Thus, the proposed algorithm addresses the stagnation issue when applied to traffic management and can adapt and be used in an actual event that requires shortest path solution by incorporating rules and constraints and other scenarios that may contribute to the delays.


Inverse ant algorithm Rules Constraints 


  1. 1.
    Adubi, S.A., Sanjay, M.: A comparative study on the Ant Colony Optimization Algorithms. In: 2014 11th International Conference on Electronics, Computer and Computation (ICECCO), 29 September–1 October 2014 (2014). 978-1-4799-4106-3/14/$31.00 © 2014 IEEEGoogle Scholar
  2. 2.
    Collin, A.: Ant Colony Algorithms: solving optimization problems. Dr. Dobb’s J. 31(9) 46–51 (2006)Google Scholar
  3. 3.
    Dias, J.C., Machado, P., Silva, D.C., Abreu, P.H.: An Inverted Ant Colony Optimization approach to traffic. Eng. Appl. Artif. Intell. 36, 122–133 (2014)CrossRefGoogle Scholar
  4. 4.
    Gu, S., Zhang, X.: An Improved Ant Colony Algorithm with Soldier Ant. In: 11th International Conference on Natural Computation (ICNC), Hubei, China, pp. 206–209 (2015)Google Scholar
  5. 5.
    Huang, M., Ding, P.: An Improved Ant Colony Algorithm and its application in vehicle routing problem. In: Mathematical Problems in Engineering, pp. 1–9 (2013)zbMATHGoogle Scholar
  6. 6.
    Min, H., Dazhi, P., Song, Y.: An improved hybrid ant colony algorithm and its application in solving TSP*, pp. 423–427. IEEE (2014)Google Scholar
  7. 7.
    Ping, G., Chunbo, X., Yi, C., Jing, L., Yanqing, L.: Adaptive Ant Colony Optimization Algorithm. International Conference on Mechatronics and Control (ICMC), pp. 95–98. IEEE, Jinzhou (2014)Google Scholar
  8. 8.
    Su Hlaing, Z.C., Khine, M.A.: An Ant Colony Optimization Algorithm for Solving Traveling Salesman Problem. In: 2011 International Conference on Information Communication and Management, pp. 54–59 (2011)Google Scholar
  9. 9.
    Yong, L., Guangzhou, Z., Fanjun, S.: Adaptive Ant-based dynamic routing algorithm. In: 5th World Congress on Intelligent Control, pp. 2694–2697. IEEE, Hangzhou (2004)Google Scholar
  10. 10.
    Yonghua, Z., Jin, X., Wentong, Y., Yong, C.: The Advanced Ant Colony Algorithm and Its Application. 2011 Third International Conference on Measuring Technology and Mechatronics Automation, pp. 664–667 (2001)Google Scholar
  11. 11.
    Yuan, Y., Liu, Y., Wu, B.: A modified Ant Colony algorithm to solve the shortest path problem. In: International Conference on Cloud Computing and Internet of Things (CCIOT 2014), pp. 148–151. IEEE,Changchun (2014)Google Scholar
  12. 12.
    Zhaoa, D., Luob, L., Zhanga, K.: An improved ant colony optimization for the communication network routing problem. Math. Comput. Model. 52 (2010)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Graduate ProgramsTechnological Institute of the PhilippinesQuezon CityPhilippines

Personalised recommendations