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Novel local restart strategies with hyper-populated ant colonies for dynamic optimization problems

  • S.I. : Machine Learning Applications for Self-Organized Wireless Networks
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Abstract

The emergence of novel metaheuristic algorithms and the impracticality of exact algorithms led to the increased application of stochastic optimization techniques to solve combinatorial optimization problems. Determination of population size, stopping criteria, selection of optimal parameter values, getting out of the local optima and most importantly the interplay between various parameters are yet to be addressed. In this work, the significance of population size and how it interplays with other parameters in determining the effective convergence of the system in both static and dynamic scenarios for travelling salesman problem (TSP) are explained. This work utilizes a more complex variant of introducing dynamism in TSP, by swapping existing nodes with new nodes. This work proposes novel local restart strategies for efficient search space reset during node replacements in dynamic TSP. The proposed local restart strategy in combination with hyper-populated ant systems is found to outperform existing state-of-the-art solutions on benchmark datasets including Oliver30, Eilon51 and KROA100 from TSPLIB.

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References

  1. Bottou L (2010) Large-scale machine learning with stochastic gradient descent. In: Proceedings of COMPSTAT’2010 Physica-Verlag HD, pp 177–186

  2. Bousquet O, Bottou L (2008) The tradeoffs of large scale learning. In: Advances in neural information processing systems, pp 161–168

  3. Sieminski A (2015) Potentials of hyper populated ant colonies. In: Asian conference on intelligent information and database systems, pp 408–417

  4. Salimans T, Ho J, Chen X, Sutskever I (2017) Evolution strategies as a scalable alternative to reinforcement learning. arXiv preprint arXiv:1703.03864

  5. Gass SI, Harris CM (2012) Encyclopedia of operations research and management science. Springer, New York

    MATH  Google Scholar 

  6. Sorensen K (2015) Metaheuristicsthe metaphor exposed. Int Trans Oper Res 22(1):3–18

    Article  MathSciNet  MATH  Google Scholar 

  7. Kirkpatrick S, Gelatt CD, Vecchi MP (1983) Optimization by simulated annealing. Science 220(4598):671–680

    Article  MathSciNet  MATH  Google Scholar 

  8. Glover F (1989) Tabu searchpart I. ORSA J Comput 1(3):190–206

    Article  MathSciNet  MATH  Google Scholar 

  9. Beni G, Wang J (1993) Swarm intelligence in cellular robotic systems. In: Robots and biological systems: towards a new bionics? Springer, Berlin, pp 703–712

  10. Kennedy J (2011) Particle swarm optimization. In: Encyclopedia of machine learning, pp 760–766

  11. Dorigo M, Maniezzo V, Colorni A (1991) The ant system: an autocatalytic optimizing process. Technical report 1–21

  12. Rosengren R (1971) Route fidelity, visual memory and recruitment behaviour in foraging wood ants of the genus Formica:(Hymenoptera, Formicidae). Societas pro fauna et flora Fennica

  13. Deneubourg JL, Pasteels JM, Verhaeghe JC (1983) Probabilistic behaviour in ants: a strategy of errors? J Theor Biol 105(2):259–271

    Article  Google Scholar 

  14. Gutjahr WJ (2000) A graph-based ant system and its convergence. Future Gen Comput Syst 16(8):873–888

    Article  Google Scholar 

  15. Prakasam A, Savarimuthu N (2016) Metaheuristic algorithms and probabilistic behaviour: a comprehensive analysis of ant colony optimization and its variants. Artif Intell Rev 45(1):97–130

    Article  Google Scholar 

  16. Yang Q, Chen WN, Yu Z, Gu T, Li Y, Zhang H, Zhang J (2017) Adaptive multimodal continuous ant colony optimization. IEEE Trans Evol Comput 21(2):191–205

    Article  Google Scholar 

  17. Liu J, Yang J, Liu H, Tian X, Gao M (2017) An improved ant colony algorithm for robot path planning. Soft Comput 21(19):5829–5839

    Article  Google Scholar 

  18. Chen Z, Wang RL (2017) Ant colony optimization with different crossover schemes for global optimization. Clust Comput 20(2):1247–1257

    Article  Google Scholar 

  19. Wang J, Cao J, Sherratt RS, Park JH (2017) An improved ant colony optimization-based approach with mobile sink for wireless sensor networks. J Supercomput. https://doi.org/10.1007/s11227-017-2115-6

    Google Scholar 

  20. Sathiamoorthy J, Ramakrishnan B (2017) Energy and delay efficient dynamic cluster formation using hybrid AGA with FACO in EAACK MANETs. Wirel Netw 23(2):371–385

    Article  Google Scholar 

  21. Zhang Q, Zhang C (2017) An improved ant colony optimization algorithm with strengthened pheromone updating mechanism for constraint satisfaction problem. Neural Comput Appl. https://doi.org/10.1007/s00521-017-2912-0

    Google Scholar 

  22. Rosset V, Paulo MA, Cespedes JG, Nascimento MC (2017) Enhancing the reliability on data delivery and energy efficiency by combining swarm intelligence and community detection in large-scale WSNs. Expert Syst Appl 78:89–102

    Article  Google Scholar 

  23. Tsai CW, Tsai PW, Pan JS, Chao HC (2015) Metaheuristics for the deployment problem of WSN: a review. Microprocess Microsyst 39(8):1305–1317

    Article  Google Scholar 

  24. Kaur S, Mahajan R (2018) Hybrid meta-heuristic optimization based energy efficient protocol for wireless sensor networks. Egypt Inform J. https://doi.org/10.1016/j.eij.2018.01.002

    Google Scholar 

  25. Zhang M, Yang M, Wu Q, Zheng R, Zhu J (2018) Smart perception and autonomic optimization: a novel bio-inspired hybrid routing protocol for MANETs. Future Gen Comput Syst 81:505–513

    Article  Google Scholar 

  26. Zhang T, Ke L, Li J, Li J, Huang J, Li Z (2018) Metaheuristics for the tabu clustered traveling salesman problem. Comput Oper Res 89:1–12

    Article  MathSciNet  MATH  Google Scholar 

  27. Yan Y, Sohn HS, Reyes G (2017) A modified ant system to achieve better balance between intensification and diversification for the traveling salesman problem. Appl Soft Comput 60:256–267

    Article  Google Scholar 

  28. Elhoseny M, Tharwat A, Yuan X, Hassanien AE (2018) Optimizing K-coverage of mobile WSNs. Expert Syst Appl 92:142–153. https://doi.org/10.1016/j.eswa.2017.09.008

    Article  Google Scholar 

  29. Elsayed W, Elhoseny M, Sabbeh S, Riad A (2017) Self-maintenance model for wireless sensor networks. Comput Electr Eng. https://doi.org/10.1016/j.compeleceng.2017.12.022

    Google Scholar 

  30. Mavrovouniotis M, Muller FM, Yang S (2017) Ant colony optimization with local search for dynamic traveling salesman problems. IEEE Trans Cybern 47(7):1743–1756

    Article  Google Scholar 

  31. Sieminski A (2015) Using ACS for dynamic traveling salesman problem. In: New research in multimedia and internet systems, pp 145–155

  32. Sieminski A (2016) Using hyper populated ant colonies for solving the TSP. Vietnam J Comput Sci 3(2):103–117

    Article  Google Scholar 

  33. Guntsch M, Middendorf M (2001) Pheromone modification strategies for ant algorithms applied to dynamic TSP. In: Workshops on applications of evolutionary computation, pp 213–222

  34. Guntsch M, Middendorf M (2002) A population based approach for ACO. In: Cagnoni S, Gottlieb J, Hart E, Middendorf M, Raidl GR (eds) Applications of evolutionary computing. EvoWorkshops 2002. Lecture notes in computer science, vol 2279. Springer, Berlin

  35. Mavrovouniotis M, Yang S (2010) Ant colony optimization with immigrants schemes in dynamic environments. Parallel Probl Solving Nat PPSN XI:371–380

  36. Chen H, Jason T, Nor M (2007) Solving dynamic traveling salesman problem using ant colony system with local search. Int Multi Conf Eng Comput Sci. pp 117–121

  37. Stutzle T, Hoos H (1998) Improvements on the ant-system: introducing the MAX-MIN ant system. In Artificial neural nets and genetic algorithms, pp 245–249

  38. Zhang H, Zhou J (2016) Dynamic multiscale region search algorithm using vitality selection for traveling salesman problem. Expert Syst Appl 60:81–95

    Article  Google Scholar 

  39. Chen SM, Chien CY (2011) Solving the traveling salesman problem based on the genetic simulated annealing ant colony system with particle swarm optimization techniques. Expert Syst Appl 38(12):14439–14450

    Article  Google Scholar 

  40. Masutti TAS, Castro LND (2009) A self-organizing neural network using ideas from the immune system to solve the traveling salesman problem. Inf Sci 179(10):1454–1468

    Article  MathSciNet  Google Scholar 

  41. Cochrane EM, Beasley JE (2003) The co-adaptive neural network approach to the Euclidean traveling salesman problem. Neural Netw 16(10):1499–1525

    Article  Google Scholar 

  42. Wilson EO (1971) The insect societies. Harvard University Press, Cambridge, MA (Distributed by Oxford University Press)

    Google Scholar 

  43. Nicolis G, Prigogine I (1977) Self-organization in nonequilibrium systems. Wiley, New York, p 191977

    MATH  Google Scholar 

  44. Moglich M, Holldobler B (1975) Communication and orientation during foraging and emigration in the ant Formica fusca. J Comp Physiolo A Neuroethol Sens Neural Behav Physiol 101(4):275–288

    Article  Google Scholar 

  45. Verhaeghe JC (1982) Food recruitment in Tetramorium impurum. (Hymenoptera: Formicidae). Insectes Soc 29(1):67–85

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Computer Applications, National Institute of Technology, Trichy, Tamilnadu, India

    Anandkumar Prakasam & Nickolas Savarimuthu

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  1. Anandkumar Prakasam
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  2. Nickolas Savarimuthu
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Correspondence to Anandkumar Prakasam.

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Prakasam, A., Savarimuthu, N. Novel local restart strategies with hyper-populated ant colonies for dynamic optimization problems. Neural Comput & Applic 31 (Suppl 1), 63–76 (2019). https://doi.org/10.1007/s00521-018-3638-3

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