Advertisement

New Bio-inspired Meta-Heuristics - Green Herons Optimization Algorithm - for Optimization of Travelling Salesman Problem and Road Network

  • Chiranjib Sur
  • Anupam Shukla
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8298)

Abstract

Following the nature and its processes has been proved to be very fruitful when it comes to tackling the difficult hardships and making life easy. Yet again the nature and its processes has been proven to be worthy of following, but this time the discrete family is being facilitated and another member is added to the bio-inspired computing family. A new biological phenomenon following meta-heuristics called Green Heron Optimization Algorithm (GHOA) is being introduced for the first time which acquired its potential and habit from an intelligent bird called Green Heron whose diligence, skills, perception analysis capability and procedure for food acquisition has overwhelmed many zoologists. This natural skillset of the bird has been transferred into operations which readily favor the graph based and discrete combinatorial optimization problems, both unconstrained and constraint though the latter requires safe guard and validation check so that the generated solutions are acceptable. With proper modifications and modeling it can also be utilized for other wide variety of real world problems and can even optimize benchmark equations. In this work we have mainly concentrated on the algorithm introduction with establishment, illustration with minute details of the steps and performance validation of the algorithm for a wide range of dimensions of the Travelling Salesman Problem combinatorial optimization problem datasets to clearly validate its scalability performance and also on a road network for optimized graph based path planning. The result of the simulation clearly stated its capability for combination generation through randomization and converging global optimization and thus has contributed another important member of the bio-inspired computation family.

Keywords

Green Herons Optimization Algorithm combinatorial optimization graph based problems bio-inspired meta-heuristics 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
  2. 2.
  3. 3.
  4. 4.
  5. 5.
  6. 6.
    Blum, C., Roli, A.: Metaheuristics in combinatorial optimization: Overview and conceptual comparison. ACM Comput. Surv. 35(3), 268–308 (2003)CrossRefGoogle Scholar
  7. 7.
    Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of the IEEE International Conference on Neural Networks, vol. 4, pp. 1942–1948 (November/December 1995)Google Scholar
  8. 8.
    Karaboga, D.: An idea based on honey bee swarm for numerical optimization. Technical Report TR06, Erciyes University (October 2005)Google Scholar
  9. 9.
    Kashan, A.H.: League Championship Algorithm: A New Algorithm for Numerical Function Optimization. In: Proceedings of the 2009 International Conference of Soft Computing and Pattern Recognition (SOCPAR 2009), pp. 43–48. IEEE Computer Society, Washington, DC (2009)CrossRefGoogle Scholar
  10. 10.
    Yang, X.-S., Deb, S.: Cuckoo search via Levy flights. In: World Congress on Nature & Biologically Inspired Computing (NaBIC 2009), pp. 210–214. IEEE Publication, USA (2009)CrossRefGoogle Scholar
  11. 11.
    Yang, X.-S.: A New Metaheuristic Bat-Inspired Algorithm. In: González, J.R., Pelta, D.A., Cruz, C., Terrazas, G., Krasnogor, N. (eds.) NICSO 2010. SCI, vol. 284, pp. 65–74. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  12. 12.
    Kirkpatrick, S., Gelatt Jr., C.D., Vecchi, M.P.: Optimization by Simulated Annealing. Science 220(4598), 671–680 (1983)CrossRefzbMATHMathSciNetGoogle Scholar
  13. 13.
    Storn, R., Price, K.: Differential evolution - a simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization 11(4), 341–359 (1997)CrossRefzbMATHMathSciNetGoogle Scholar
  14. 14.
    Farmer, J.D., Packard, N., Perelson, A.: The immune system, adaptation and machine learning. Physica D 22(1-3), 187–204 (1986)CrossRefMathSciNetGoogle Scholar
  15. 15.
    Geem, Z.W., Kim, J.H., Loganathan, G.V.: A new heuristic optimization algorithm: harmony search. Simulation 76(2), 60–68 (2001)CrossRefGoogle Scholar
  16. 16.
    Krishnanand, K., Ghose, D.: Glowworm swarm optimization for simultaneous capture of multiple local optima of multimodal functions. Swarm Intelligence 3(2), 87–124 (2009)CrossRefGoogle Scholar
  17. 17.
    Haddad, O.B., Afshar, A., Mariño, M.A.: Honey-bees mating optimization (HBMO) algorithm: a new heuristic approach for water resources optimization. Water Resources Management 20(5), 661–680 (2006)CrossRefGoogle Scholar
  18. 18.
    Sur, C., Sharma, S., Shukla, A.: Multi-objective adaptive intelligent water drops algorithm for optimization & vehicle guidance in road graph network. In: 2013 International Conference on Informatics, Electronics & Vision (ICIEV), May 17-18, pp. 1–6 (2013)Google Scholar
  19. 19.
    Civicioglu, P.: Transforming geocentric cartesian coordinates to geodetic coordinates by using differential search algorithm. Computers & Geosciences 46, 229–247 (2012)CrossRefGoogle Scholar
  20. 20.
    Kaveh, A., Talatahari, S.: A Novel Heuristic Optimization Method: Charged System Search. Acta Mechanica 213(3-4), 267–289 (2010)CrossRefzbMATHGoogle Scholar
  21. 21.
    Gandomi, A.H., Alavi, A.H.: Krill Herd Algorithm: A New Bio-Inspired Optimization Algorithm. Communications in Nonlinear Science and Numerical Simulation (2012)Google Scholar
  22. 22.
    Liang, Y.-C., Cuevas, J.R.: Virus Optimization Algorithm for Curve Fitting Problems. In: IIE Asian Conference 2011Google Scholar
  23. 23.
  24. 24.
    Lin, S., Kernighan, B.W.: An effective heuristic algorithm for the traveling salesman problem. Operations Research 21, 498–516 (1973)CrossRefzbMATHMathSciNetGoogle Scholar
  25. 25.
    Helsgaun, K.: An effective implementation of the linkernighan traveling salesman heuristic. European Journal of Operational Research 126(1), 106–130 (2000)CrossRefzbMATHMathSciNetGoogle Scholar
  26. 26.
    Applegate, D., Bixby, R.E., Chvátal, V., Cook, W.: TSP Cuts Which Do Not Conform to the Template Paradigm. In: Jünger, M., Naddef, D. (eds.) Computational Combinatorial Optimization. LNCS, vol. 2241, pp. 261–304. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  27. 27.
    Hahsler, M., Hornik, K.: TSP Infrastructure for the Traveling Salesperson Problem (2007)Google Scholar
  28. 28.
    Dantzig, G.B., Fulkerson, D.R., Johnson, S.M.: Solution of a Large-scale Traveling Salesman Problem. Operations Research 2, 393–410 (1954)CrossRefMathSciNetGoogle Scholar
  29. 29.
    Miller, Pekny, J.: Exact Solution of Large Asymmetric Traveling Salesman Problems. Science 251, 754–761 (1991)CrossRefGoogle Scholar
  30. 30.
    Kennedy, J., Eberhart, R.C.: A Discrete Version of The Particle Swarm Algorithm. In: Proceedings of Conference on Systems, Man, and Cybernetics, pp. 4104–4108. IEEE Services Center, NJ (1997)Google Scholar
  31. 31.
    Guo, P., Wang, X., Han, Y.: A Hybrid Genetic Algorithm for Structural Optimization with Discrete Variables. In: 2011 International Conference on Internet Computing & Information Services (ICICIS), September 17-18, pp. 223–226 (2011)Google Scholar
  32. 32.
    Sur, C., Shukla, A.: Discrete bacteria foraging optimization algorithm for vehicle distribution optimization in graph based road network management. In: Thampi, S.M., Abraham, A., Pal, S.K., Rodriguez, J.M.C. (eds.) Recent Advances in Intelligent Informatics. AISC, vol. 235, pp. 351–358. Springer, Heidelberg (2014)CrossRefGoogle Scholar
  33. 33.
    Dorigo, M., Gambardella, L.M.: Ant Colony System: A Cooperative Learning Approach to the Traveling Salesman Problem. IEEE Transactions on Evolutionary Computation 1(1), 53–66 (1997)CrossRefGoogle Scholar
  34. 34.
    Chen, W.-N., Zhang, J.: A novel set-based particle swarm optimization method for discrete optimization problem. IEEE Transactions on Evolutionary Computation 14(2), 278–300 (2010)CrossRefGoogle Scholar
  35. 35.
    Clerc, M.: Discrete Particle Swarm Optimization, illustrated by the Traveling Salesman Problem. In: New Optimization Techniques in Engineering. STUDFUZZ, vol. 141, pp. 219–239. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  36. 36.
    Kundu, D., Suresh, K., Ghosh, S., Das, S., Panigrahi, B.K., Das, S.: Multi-objective optimization with artificial weed colonies. Information Sciences 181(12), 2441–2454 (2011)CrossRefMathSciNetGoogle Scholar
  37. 37.
    Sur, C., Sharma, S., Shukla, A.: Analysis & modeling multi-breeded Mean-Minded ant colony optimization of agent based Road Vehicle Routing Management. In: 2012 International Conference For Internet Technology and Secured Transactions, pp. 634–641 (2012)Google Scholar
  38. 38.
    Sur, C., Sharma, S., Shukla, A.: Egyptian Vulture Optimization Algorithm – A New Nature Inspired Meta-heuristics for Knapsack Problem. In: Meesad, P., Unger, H., Boonkrong, S. (eds.) IC2IT2013. AISC, vol. 209, pp. 227–237. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  39. 39.
    Sur, C., Sharma, S., Shukla, A.: Solving Travelling Salesman Problem Using Egyptian Vulture Optimization Algorithm - A New Approach. In: Kłopotek, M.A., Koronacki, J., Marciniak, M., Mykowiecka, A., Wierzchoń, S.T. (eds.) IIS 2013. LNCS, vol. 7912, pp. 254–267. Springer, Heidelberg (2013)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  • Chiranjib Sur
    • 1
  • Anupam Shukla
    • 1
  1. 1.ABV-Indian Institute of Information Technology & ManagementGwaliorIndia

Personalised recommendations