Generalized Ant Colony Optimizer: swarm-based meta-heuristic algorithm for cloud services execution

Abstract

This work presents a swarm-based meta-heuristic technique known as Generalized Ant Colony Optimizer (GACO). It is a hybrid approach which consists of Simple Ant Colony Optimization and Global Colony Optimization concepts. The main concept behind GACO is the foraging behavior of ants. GACO operates in the following four phases: Creation of a new colony, search of nearest food location, balance the solution, and updating of pheromone. GACO has been tested on seventeen well recognized standard benchmark functions and its results have been compared with three different meta-heuristic algorithms namely as Genetic Algorithm, Particle Swarm Optimization and Artificial Bee Colony. The performance metrics such as average and standard deviation are computed and evaluated with respect to these metrics. The proposed GACO performs better in comparison to the aforementioned algorithms. The proposed algorithm optimizes the cloud resource allocation problem and gives better results with unknown search spaces.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

References

  1. 1.

    Kumar A, Bawa S (2012) Distributed and big data storage management in grid computing, arXiv preprint arXiv:1207.2867

    Google Scholar 

  2. 2.

    Choi Y, Lim Y (2016) Optimization approach for resource allocation on cloud computing for IoT. Int J Distrib Sens Netw 12(3):3479247

    Google Scholar 

  3. 3.

    Leitner P, Ferner J, Hummer W, Dustdar S (2013) Data-driven and automated prediction of service level agreement violations in service compositions. Distrib Parallel Databases 31(3):447

    Google Scholar 

  4. 4.

    Leitner P, Hummer W, Dustdar S (2013) Cost-based optimization of service compositions. IEEE Trans Serv Comput 6(2):239

    Google Scholar 

  5. 5.

    Farahnakian F, Ashraf A, Pahikkala T, Liljeberg P, Plosila J, Porres I, Tenhunen H (2015) Using ant colony system to consolidate VMs for green cloud computing. IEEE Trans Serv Comput 8(2):187

    Google Scholar 

  6. 6.

    Riveni M, Nguyen TD, Dustdar S (2017) In: International conference on business process management. Springer, pp 361–373

  7. 7.

    Ranjan R, Wang L, Chen J, Benatallah B (2011) Cloud computing: methodology, systems, and applications. CRC Press, Boca Raton

    Google Scholar 

  8. 8.

    Özer AH, Özturan C (2009) In: Fifth international conference on soft computing, computing with words and perceptions in system analysis, decision and control, 2009. ICSCCW 2009. IEEE, pp 1–4

  9. 9.

    Li W, Liu X, Zhang X, Zhang X (2015) Dynamic fair allocation of multiple resources with bounded number of tasks in cloud computing systems. Multiagent Grid Syst 11(4):245

    Google Scholar 

  10. 10.

    Dorigo M, Blum C (2005) Ant colony optimization theory: a survey. Theor Comput Sci 344(2–3):243

    MathSciNet  MATH  Google Scholar 

  11. 11.

    Socha K, Dorigo M (2008) Ant colony optimization for continuous domains. Eur J Oper Res 185(3):1155

    MathSciNet  MATH  Google Scholar 

  12. 12.

    Merkle D, Middendorf M (2003) Ant colony optimization with global pheromone evaluation for scheduling a single machine. Appl Intell 18(1):105

    MATH  Google Scholar 

  13. 13.

    Liu X, Zhang X, Li W, Zhang X (2017) Swarm optimization algorithms applied to multi-resource fair allocation in heterogeneous cloud computing systems. Computing 99(12):1231

    MathSciNet  MATH  Google Scholar 

  14. 14.

    Neshat M, Sepidnam G, Sargolzaei M, Toosi AN (2014) Artificial fish swarm algorithm: a survey of the state-of-the-art, hybridization, combinatorial and indicative applications. Artif Intell Rev 42(4):965

    Google Scholar 

  15. 15.

    Kennedy J (2010) Particle swarm optimization. In: Sammut C, Webb GI (eds) Encyclopedia of machine learning. Springer US, Boston, MA, pp 760–766. https://doi.org/10.1007/978-0-387-30164-8_630

    Google Scholar 

  16. 16.

    Karaboga D, Ozturk C (2011) A novel clustering approach: artificial bee colony (ABC) algorithm. Appl Soft Comput 11(1):652

    Google Scholar 

  17. 17.

    Dorigo M, Caro G Di (1999) In: Proceedings of the 1999 congress on evolutionary computation, 1999. CEC 99, vol 2, IEEE, pp 1470–1477

  18. 18.

    Gandomi AH, Yang XS, Alavi AH (2013) Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems. Eng Comput 29(1):17

    Google Scholar 

  19. 19.

    Yang XS (2011) Bat algorithm for multi-objective optimisation. Int J Bio-Inspir Comput 3(5):267

    Google Scholar 

  20. 20.

    Yang XS, Karamanoglu M, He X (2014) Flower pollination algorithm: a novel approach for multiobjective optimization. Eng Optim 46(9):1222

    MathSciNet  Google Scholar 

  21. 21.

    Karimkashi S, Kishk AA (2010) Invasive weed optimization and its features in electromagnetics. IEEE Trans Antennas Propag 58(4):1269

    Google Scholar 

  22. 22.

    Yang XS (2010) Firefly algorithm, stochastic test functions and design optimisation. Int J Bio-Inspir Comput 2(2):78

    Google Scholar 

  23. 23.

    Dhiman G, Kumar V (2017) Spotted hyena optimizer: a novel bio-inspired based metaheuristic technique for engineering applications. Adv Eng Softw 114:48

    Google Scholar 

  24. 24.

    Mirjalili S, Mirjalili SM, Lewis A (2014) Grey wolf optimizer. Adv Eng Softw 69:46

    Google Scholar 

  25. 25.

    Dhiman G, Kumar V (2018) Emperor penguin optimizer: a bio-inspired algorithm for engineering problems. Knowl-Based Syst 159:20–50

    Google Scholar 

  26. 26.

    Gandomi AH, Alavi AH (2012) Krill herd: a new bio-inspired optimization algorithm. Commun Nonlinear Sci Numer Simul 17(12):4831

    MathSciNet  MATH  Google Scholar 

  27. 27.

    Yan JY, Ling Q, Sun DM (2006) In: International conference on machine learning and cybernetics, 2006, IEEE, pp 2103–2106

  28. 28.

    Weile DS, Michielssen E (1997) Genetic algorithm optimization applied to electromagnetics: a review. IEEE Trans Antennas Propag 45(3):343

    Google Scholar 

  29. 29.

    Du D, Simon D, Ergezer M (2009) In: IEEE international conference on systems, man and cybernetics, 2009. SMC 2009, IEEE, pp 997–1002

  30. 30.

    Yao X, Liu Y, Lin G (1999) Evolutionary programming made faster. IEEE Trans Evolut Comput 3(2):82

    Google Scholar 

  31. 31.

    Koza JR (1994) Genetic programming as a means for programming computers by natural selection. Stat Comput 4(2):87

    Google Scholar 

  32. 32.

    Hatamlou A (2013) Black hole: a new heuristic optimization approach for data clustering. Inf Sci 222:175

    MathSciNet  Google Scholar 

  33. 33.

    Geem ZW, Kim JH, Loganathan GV (2001) A new heuristic optimization algorithm: harmony search. Simulation 76(2):60

    Google Scholar 

  34. 34.

    Van Laarhoven PJ, Aarts EH (1987) Simulated annealing: theory and applications. Springer, New York, pp 7–15

    MATH  Google Scholar 

  35. 35.

    Yang Ll, Qian Wy, Zhang Q (2011) Central force optimization. J Bohai Univ (Nat Sci Ed) 3:001

    Google Scholar 

  36. 36.

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

    Google Scholar 

  37. 37.

    Blum C (2005) Ant colony optimization: introduction and recent trends. Phys Life Rev 2(4):353

    Google Scholar 

  38. 38.

    Taillard E (1998) FANT: fast ant system, Technical report

  39. 39.

    Dorigo M, Caro GD, Gambardella LM (1999) Ant algorithms for discrete optimization. Artif Life 5(2):137

    Google Scholar 

  40. 40.

    Kaji T (2001) In: IEEE international conference on systems, man, and cybernetics, 2001, vol. 5, IEEE, pp 3429–3434

  41. 41.

    Boryczka U (2009) Finding groups in data: cluster analysis with ants. Appl Soft Comput 9(1):61

    Google Scholar 

  42. 42.

    Giraldo LF, Lozano F, Quijano N (2011) Foraging theory for dimensionality reduction of clustered data. Mach Learn 82(1):71

    MathSciNet  Google Scholar 

  43. 43.

    Deneubourg J, Goss S, Franks N, Sendova-Franks A, Detrain C, Chretien L (1992) In: From animals to animats: proceedings of the first international conference on simulation of adaptive behavior, pp 353–363

  44. 44.

    Dorigo M, Birattari M (2011) Encyclopedia of machine learning. Springer, New York, pp 36–39

    Google Scholar 

  45. 45.

    Dorigo M, Stützle T (2003) Handbook of metaheuristics. Springer, New York, pp 250–285

    Google Scholar 

  46. 46.

    Shtovba SD (2005) Ant algorithms: theory and applications. Program Comput Softw 31(4):167

    MATH  Google Scholar 

  47. 47.

    Silva CA, Sousa J, Runkler TA, Da Costa JS (2009) Distributed supply chain management using ant colony optimization. Eur J Oper Res 199(2):349

    MathSciNet  MATH  Google Scholar 

  48. 48.

    Lorpunmanee S, Sap MN, Abdullah AH, Chompoo-inwai C (2007) An ant colony optimization for dynamic job scheduling in grid environment. Int J Comput Inf Sci Eng 1(4):207

    Google Scholar 

  49. 49.

    Singh B, Bawa S (2007) In: Proceedings of the third conference on IASTED international conference, pp 283–286

  50. 50.

    Di Caro G, Dorigo M (1998) In: International conference on parallel problem solving from nature, Springer, pp 673–682

  51. 51.

    Ahuja A, Das S, Pahwa A (2007) An AIS-ACO hybrid approach for multi-objective distribution system reconfiguration. IEEE Trans Power Syst 22(3):1101

    Google Scholar 

  52. 52.

    Gao Y, Guan H, Qi Z, Hou Y, Liu L (2013) A multi-objective ant colony system algorithm for virtual machine placement in cloud computing. J Comput Syst Sci 79(8):1230

    MathSciNet  MATH  Google Scholar 

  53. 53.

    Runkler TA (2005) Ant colony optimization of clustering models. Int J Intell Syst 20(12):1233

    MATH  Google Scholar 

  54. 54.

    Luh GC, Lin CY (2008) Optimal design of truss structures using ant algorithm. Struct Multidiscip Optim 36(4):365

    Google Scholar 

  55. 55.

    Kasprzok A, Ayalew B, Lau C (2018) An ant-inspired model for multi-agent interaction networks without stigmergy. Swarm Intell 12(1):53

    Google Scholar 

  56. 56.

    Pacini E, Mateos C, Garino CG (2016) Multi-objective swarm intelligence schedulers for online scientific clouds. Computing 98(5):495

    MathSciNet  Google Scholar 

  57. 57.

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

    Google Scholar 

  58. 58.

    Merkle D, Middendorf M (2002) Modeling the dynamics of ant colony optimization. Evolut Comput 10(3):235

    MATH  Google Scholar 

  59. 59.

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

    Google Scholar 

  60. 60.

    Kolavali SR, Bhatnagar S (2008) In: International conference on network control and optimization, Springer, pp 37–44

  61. 61.

    Liu J, Xu S, Zhang F, Wang L (2017) A hybrid genetic-ant colony optimization algorithm for the optimal path selection. Intell Autom Soft Comput 23(2):235

    Google Scholar 

  62. 62.

    Gambardella LM, Dorigo M (2000) An ant colony system hybridized with a new local search for the sequential ordering problem. INFORMS J Comput 12(3):237

    MathSciNet  MATH  Google Scholar 

  63. 63.

    Costa D, Hertz A (1997) Ants can colour graphs. J Oper Res Soc 48(3):295

    MATH  Google Scholar 

  64. 64.

    Žerovnik J, Vesel A (2000) How well can ants color graphs? J Comput Inf Technol 8(2):131

    Google Scholar 

  65. 65.

    Bianchi L, Gambardella LM, Dorigo M (2002) In: International conference on parallel problem solving from nature, Springer, pp 883–892

  66. 66.

    Reimann M, Doerner K, Hartl RF (2004) D-ants: savings based ants divide and conquer the vehicle routing problem. Comput Oper Res 31(4):563

    MATH  Google Scholar 

  67. 67.

    Moss J, Johnson CG (2003) In: Artificial neural nets and genetic algorithms, Springer, pp 182–186

  68. 68.

    Solnon C (2002) Ants can solve constraint satisfaction problems. IEEE Trans Evolut Comput 6(4):347

    Google Scholar 

  69. 69.

    Parpinelli RS, Lopes HS, Freitas AA (2002) Data mining with an ant colony optimization algorithm. IEEE Trans Evolut Comput 6(4):321

    MATH  Google Scholar 

  70. 70.

    Merz P, Freisleben B (1999) In: Proceedings of the 1999 congress on evolutionary computation, 1999. CEC 99. vol. 3, IEEE, pp 2063–2070

  71. 71.

    Stutzle T, Dorigo M (1999) Aco algorithms for the quadratic assignment problem. New Ideas Optim C(C50):33

    MATH  Google Scholar 

  72. 72.

    Banerjee S, Mukherjee I, Mahanti P (2009) Cloud computing initiative using modified ant colony framework. World Acad Sci Eng Technol 56(32):221

    Google Scholar 

  73. 73.

    Socha K (2004) In: International workshop on ant colony optimization and swarm intelligence, Springer, pp 25–36

  74. 74.

    Lu DN, Nguyen TH, Nguyen DN, Nguyen HN et al. (2017) In: International conference on information networking (ICOIN), 2017, IEEE, pp 584–589

  75. 75.

    Zeng W, Zhao Y, Ou K, Song W (2009) In: Proceedings of the 2nd international conference on interaction sciences: information technology, culture and human, ACM, pp 1044–1048

  76. 76.

    Mishra R, Jaiswal A (2012) Ant colony optimization: a solution of load balancing in cloud. Int J Web Semant Technol 3(2):33

    Google Scholar 

  77. 77.

    Gutjahr WJ (2002) ACO algorithms with guaranteed convergence to the optimal solution. Inf Process Lett 82(3):145

    MathSciNet  MATH  Google Scholar 

  78. 78.

    Nakib A, Ismail B, Ouchraa S, Schmitt L et al (2017) Metaheuristics for intelligent electrical networks, vol 10. Wiley, Hoboken

    Google Scholar 

  79. 79.

    Molga M, Smutnicki C (2005) Test functions for optimization needs, Test Funct Optim Needs 101

  80. 80.

    Jamil M, Yang XS (2013) A literature survey of benchmark functions for global optimisation problems. Int J Math Modell Numer Optim 4(2):150

    MATH  Google Scholar 

Download references

Author information

Affiliations

Authors

Corresponding author

Correspondence to Ajay Kumar.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Kumar, A., Bawa, S. Generalized Ant Colony Optimizer: swarm-based meta-heuristic algorithm for cloud services execution. Computing 101, 1609–1632 (2019). https://doi.org/10.1007/s00607-018-0674-x

Download citation

Keywords

  • Ant algorithms
  • Meta-heuristics
  • Cloud computing
  • Optimization

Mathematics Subject Classification

  • 91B32
  • 68T20
  • 90C26