Skip to main content
SpringerLink
Log in
Book cover

Nature-Inspired Computing and Optimization pp 51–78Cite as

Multi-objective Ant Colony Optimisation in Wireless Sensor Networks

Part of the Modeling and Optimization in Science and Technologies book series (MOST,volume 10)

Abstract

Biologically inspired ant colony optimisation (ACO) has been used in several applications to solve NP-hard combinatorial optimisation problems. An interesting area of application for ACO-based algorithms is their use in wireless sensor networks (WSNs). Due to their robustness and self-organisation, ACO-based algorithms are well-suited for the distributed, autonomous and self-organising structure of WSNs. While the original ACO-based algorithm and its direct descendants can take only one objective into account, multi-objective ant colony optimisation (MOACO) is capable of considering multiple (conflicting) objectives simultaneously. In this chapter, a detailed review and summary of MOACO-based algorithms and their applications in WSNs is given. In particular, a taxonomy of MOACO-based algorithms is presented and their suitability for multi-objective combinatorial optimisation problems in WSNs is highlighted.

Keywords

  • Pareto Optimal Solution
  • Packet Delivery Ratio
  • Heuristic Information
  • Pheromone Information
  • MOACO Algorithm

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   139.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   179.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   179.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Alaya I, Solnon C, Ghédira K (2007) Ant colony optimization for multi-objective optimization problems. In: 19th IEEE international conference on tools with artificial intelligence (ICTAI 2007), October 29–31, 2007, Patras, Greece, vol 1, pp 450–457

    Google Scholar 

  2. Angus D (2007) Crowding population-based ant colony optimisation for the multi-objective travelling salesman problem. In: IEEE symposium on computational intelligence in multicriteria decision making, MCDM 2007, Honolulu, Hawaii, USA, April 1–5, 2007, pp 333–340

    Google Scholar 

  3. Angus D, Woodward C (2009) Multiple objective ant colony optimisation. Swarm Intell 3(1):69–85

    CrossRef  Google Scholar 

  4. Barán B, Schaerer M (2003) A multiobjective ant colony system for vehicle routing problem with time windows. In: The 21st IASTED international multi-conference on applied informatics (AI 2003), February 10–13, 2003. Innsbruck, Austria, pp 97–102

    Google Scholar 

  5. Berre ML, Hnaien F, Snoussi H (2011) Multi-objective optimization in wireless sensors networks. In: 2011 international conference on microelectronics (ICM). IEEE, pp 1–4

    Google Scholar 

  6. Blum C (2005) Beam-aco—hybridizing ant colony optimization with beam search: an application to open shop scheduling. Comput OR 32:1565–1591

    CrossRef  MATH  Google Scholar 

  7. Blum C, Blesa MJ (2005) New metaheuristic approaches for the edge-weighted k-cardinality tree problem. Comput OR 32:1355–1377

    CrossRef  MATH  Google Scholar 

  8. Bridgman PW (1922) Dimensional analysis. Yale University Press

    Google Scholar 

  9. Bullnheimer B, Hartl R, Strauß C (1997) A new rank based version of the ant system—a computational study. Central Eur J Oper Res Econ. Citeseer

    Google Scholar 

  10. De Campos LM, Fernández-Luna JM, Gámez JA, Puerta JM (2002) Ant colony optimization for learning bayesian networks. Int J Approx Reason 31(3):291–311

    CrossRef  MathSciNet  MATH  Google Scholar 

  11. De Campos LM, Puerta J et al (2008) Learning bayesian networks by ant colony optimisation: searching in two different spaces. Mathware Soft Comput 9(3):251–268

    MathSciNet  MATH  Google Scholar 

  12. Cardoso P, Jesus M, Márquez A (2003) Monaco-multi-objective network optimisation based on an aco. Proc X Encuentros de Geometrıa Computacional, Seville, Spain

    Google Scholar 

  13. Caro GD, Dorigo M (1998) Antnet: distributed stigmergetic control for communications networks. J Artif Intell Res (JAIR) 9:317–365

    MATH  Google Scholar 

  14. Caro GD, Ducatelle F, Gambardella LM (2005) Anthocnet: an adaptive nature-inspired algorithm for routing in mobile ad hoc networks. Eur Trans Telecommun 16(5):443–455

    CrossRef  Google Scholar 

  15. Coello CAC, Dhaenens C, Jourdan L (eds) (2010) Advances in multi-objective nature inspired computing. In: Studies in computational intelligence, vol 272. Springer

    Google Scholar 

  16. Constantinou D (2011) Ant colony optimisation algorithms for solving multi-objective power-aware metrics for mobile ad hoc networks. University of Pretoria, Thesis

    Google Scholar 

  17. Costa D, Hertz A (1997) Ants can colour graphs. J Oper Res Soc 48(3):295–305

    CrossRef  MATH  Google Scholar 

  18. Deepalakshmi P, Radhakrishnan S (2011) An ant colony-based multi objective quality of service routing for mobile ad hoc networks. EURASIP J Wirel Commun Netw 2011:153

    CrossRef  Google Scholar 

  19. Den Besten M, Stützle T, Dorigo M (2000) Ant colony optimization for the total weighted tardiness problem. In: Parallel problem solving from nature PPSN VI. Springer, pp 611–620

    Google Scholar 

  20. Doerner K, Hartl R, Reimann M (2001) Are COMPETants more competent for problem solving? The case of a multiple objective transportation problem. Report series SFB adaptive information systems and modelling in economics and management science

    Google Scholar 

  21. Doerner KF, Gutjahr WJ, Hartl RF, Strauss C, Stummer C (2004) Pareto ant colony optimization: a metaheuristic approach to multiobjective portfolio selection. Ann OR 131(1–4):79–99

    CrossRef  MathSciNet  MATH  Google Scholar 

  22. Dorigo M (1992) Optimization, learning and natural algorithms (in Italian). PhD thesis, Politecnico di Milano, Italy

    Google Scholar 

  23. Dorigo M, Gambardella LM (1997a) Ant colonies for the travelling salesman problem. BioSystems 43(2):73–81

    CrossRef  Google Scholar 

  24. Dorigo M, Gambardella LM (1997b) Ant colony system: a cooperative learning approach to the traveling salesman problem. IEEE Trans Evol Comput 1(1):53–66

    CrossRef  Google Scholar 

  25. Dorigo M, Maniezzo V, Colorni A (1991) The ant system: an autocatalytic optimizing process. In: TR91-016, Politecnico di Milano

    Google Scholar 

  26. Dorigo M, Maniezzo V, Colorni A, Maniezzo V (1991b) Positive feedback as a search strategy. Technical report, Dipartimento di Elettronica, Politecnico di Milano

    Google Scholar 

  27. 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):29–41

    CrossRef  Google Scholar 

  28. Dorigo M, Caro GD, Gambardella LM (1999) Ant algorithms for discrete optimization. Artif Life 5(2):137–172

    CrossRef  Google Scholar 

  29. Ducatelle F, Caro GD, Gambardella LM (2005) Using ant agents to combine reactive and proactive strategies for routing in mobile ad-hoc networks. Int J Comput Intell Appl 5(2):169–184

    CrossRef  MATH  Google Scholar 

  30. Fenet S, Solnon C (2003) Searching for maximum cliques with ant colony optimization. In: Applications of evolutionary computing, EvoWorkshop 2003: EvoBIO, EvoCOP, EvoIASP, EvoMUSART, EvoROB, and EvoSTIM, Essex, UK, April 14–16, 2003, Proceedings, pp 236–245

    Google Scholar 

  31. Fidanova S, Marinov P, Paprzycki M (2013) Influence of the number of ants on multi-objective ant colony optimization algorithm for wireless sensor network layout. In: Large-scale scientific computing—9th international conference, LSSC 2013, Sozopol, Bulgaria, June 3–7, 2013. Revised Selected Papers, pp 232–239

    Google Scholar 

  32. Fishburn P (1967) Additive utilities with incomplete product set: applications to priorities and sharings

    Google Scholar 

  33. Fonseca CM, Fleming PJ, Zitzler E, Deb K, Thiele L (eds) (2003) Evolutionary multi-criterion optimization. In: Second international conference, EMO 2003, Faro, Portugal, April 8–11, 2003, Proceedings, Lecture notes in computer science, vol 2632. Springer

    Google Scholar 

  34. Gambardella LM, Dorigo M (1995) Ant-q: a reinforcement learning approach to the traveling salesman problem. In: Machine learning, Proceedings of the twelfth international conference on machine learning, Tahoe City, California, USA, July 9–12, 1995, pp 252–260

    Google Scholar 

  35. Gambardella LM, Dorigo M (1996) Solving symmetric and asymmetric tsps by ant colonies. In: International conference on evolutionary computation, pp 622–627

    Google Scholar 

  36. 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):237–255

    CrossRef  MathSciNet  MATH  Google Scholar 

  37. Gambardella LM, Taillard É, Agazzi G (1999) Macs-vrptw: a multiple ant colony system for vehicle routing problems with time windows. New ideas in optimization. McGraw-Hill Ltd., UK, pp 63–76

    Google Scholar 

  38. García OC, Triguero FH, Stützle T (2002) A review on the ant colony optimization metaheuristic: basis, models and new trends. Mathware Soft Comput 9(3):141–175

    MathSciNet  Google Scholar 

  39. Gardel P, Baran B, Estigarribia H, Fernandez U, Duarte S (2006) Multiobjective reactive power compensation with an ant colony optimization algorithm. In: The 8th IEE international conference on AC and DC power transmission, 2006. ACDC 2006, IET, pp 276–280

    Google Scholar 

  40. Glover F (1989) Tabu search—part I. INFORMS J Comput 1(3):190–206

    CrossRef  MATH  Google Scholar 

  41. Glover F (1990) Tabu search—part II. INFORMS J Comput 2(1):4–32

    CrossRef  MATH  Google Scholar 

  42. Haimes YY, Ladson L, Wismer DA (1971) Bicriterion formulation of problems of integrated system identification and system optimization. IEEE Trans Syst Man Cybern 1(3):296–297

    CrossRef  MathSciNet  MATH  Google Scholar 

  43. Hansen M, Jaszkiewicz A (1998) Evaluating the quality of approximations to the non-dominated set. Department of Mathematical Modelling, Technical Universityof Denmark, IMM

    Google Scholar 

  44. Hart PE, Nilsson NJ, Raphael B (1968) A formal basis for the heuristic determination of minimum cost paths. IEEE Trans Syst Sci Cybern 4(2):100–107

    CrossRef  Google Scholar 

  45. Iredi S, Merkle D, Middendorf M (2001) Bi-criterion optimization with multi colony ant algorithms. In: Evolutionary multi-criterion optimization, first international conference, EMO 2001, Zurich, Switzerland, March 7–9, 2001, Proceedings, pp 359–372

    Google Scholar 

  46. Kellner A, Hogrefe D (2014) Multi-objective ant colony optimisation-based routing in WSNs. IJBIC 6(5):322–332

    Google Scholar 

  47. Kirkpatrick S (1984) Optimization by simulated annealing: quantitative studies. J Stat Phys 34(5–6):975–986

    CrossRef  MathSciNet  Google Scholar 

  48. Knowles J, Thiele L, Zitzler E (2006) A tutorial on the performance assessment of stochastic multiobjective optimizers. TIK report 214

    Google Scholar 

  49. Korb O, Stützle T, Exner TE (2006) PLANTS: application of ant colony optimization to structure-based drug design. In: Ant colony optimization and swarm intelligence, 5th international workshop, ANTS 2006, Brussels, Belgium, September 4–7, 2006, Proceedings, pp 247–258

    Google Scholar 

  50. Leguizamon G, Michalewicz Z (1999) A new version of ant system for subset problems. In: Proceedings of the 1999 congress on evolutionary computation, 1999. CEC 99, vol 2. IEEE

    Google Scholar 

  51. Lessing L, Dumitrescu I, Stützle T (2004) A comparison between ACO algorithms for the set covering problem. In: 4th international workshop Ant colony optimization and swarm intelligence, ANTS 2004, Brussels, Belgium, September 5–8, 2004, Proceedings, pp 1–12

    Google Scholar 

  52. López-Ibáñez M, Stützle T (2012a) The automatic design of multiobjective ant colony optimization algorithms. IEEE Trans Evol Comput 16(6):861–875

    CrossRef  Google Scholar 

  53. López-Ibáñez M, Stützle T (2012b) An experimental analysis of design choices of multi-objective ant colony optimization algorithms. Swarm Intell 6(3):207–232

    CrossRef  Google Scholar 

  54. López-Ibáñez M, Paquete L, Stützle T (2004) On the design of ACO for the biobjective quadratic assignment problem. In: Ant colony optimization and swarm intelligence, 4th international workshop, ANTS 2004, Brussels, Belgium, September 5–8, 2004, Proceedings, pp 214–225

    Google Scholar 

  55. Lourenco H, Martin O, Stützle T (2003) Iterated local search. Handbook of metaheuristics, pp 320–353

    Google Scholar 

  56. Maniezzo V (1999) Exact and approximate nondeterministic tree-search procedures for the quadratic assignment problem. INFORMS J Comput 11(4):358–369

    CrossRef  MathSciNet  MATH  Google Scholar 

  57. Mariano CE, Morales E (1999) A multiple objective ant-q algorithm for the design of water distribution irrigation networks. Instituto Mexicano de Tecnología del Agua, Technical report HC-9904

    Google Scholar 

  58. Martens D, Backer MD, Haesen R, Baesens B, Mues C, Vanthienen J (2006) Ant-based approach to the knowledge fusion problem. In: Ant colony optimization and swarm intelligence, 5th international workshop, ANTS 2006, Brussels, Belgium, September 4–7, 2006, Proceedings, pp 84–95

    Google Scholar 

  59. McMullen PR (2001) An ant colony optimization approach to addressing a JIT sequencing problem with multiple objectives. AI Eng 15(3):309–317

    Google Scholar 

  60. Merkle D, Middendorf M (2003) Ant colony optimization with global pheromone evaluation for scheduling a single machine. Appl Intell 18(1):105–111

    CrossRef  MATH  Google Scholar 

  61. Merkle D, Middendorf M, Schmeck H (2002) Ant colony optimization for resource-constrained project scheduling. IEEE Trans Evol Comput 6(4):333–346

    CrossRef  MATH  Google Scholar 

  62. Miettinen K (1999) Nonlinear multiobjective optimization, vol 12. Springer

    Google Scholar 

  63. Miller DW et al (1960) Executive decisions and operations research. Prentice-Hall

    Google Scholar 

  64. Mladenović N, Hansen P (1997) Variable neighborhood search. Comput Oper Res 24(11):1097–1100

    CrossRef  MathSciNet  MATH  Google Scholar 

  65. Paquete L, Stützle T (2007) Stochastic local search algorithms for multiobjective combinatorial optimization: a review. In: Handbook of approximation algorithms and metaheuristics, vol 13

    Google Scholar 

  66. Parpinelli RS, Lopes HS, Freitas AA (2002) Data mining with an ant colony optimization algorithm. IEEE Trans Evol Comput 6(4):321–332

    CrossRef  MATH  Google Scholar 

  67. Persis DJ, Robert TP (2015) Ant based multi-objective routing optimization in mobile ad-hoc network. Indian J Sci Technol 8(9):875–888

    CrossRef  Google Scholar 

  68. Pinto D, Barán B (2005) Solving multiobjective multicast routing problem with a new ant colony optimization approach. In: 3rd international Latin American networking conference, LANC 2005, Sponsored by IFIP TC6 communication networks and ACM SIGCOMM, Organized by CLEI (Centro Latino-Americano de Estudios en Informática), Cali, Colombia, October 10–13, 2005, pp 11–19

    Google Scholar 

  69. Reimann M, Doerner K, Hartl RF (2004) D-ants: savings based ants divide and conquer the vehicle routing problem. Comput OR 31(4):563–591

    CrossRef  MATH  Google Scholar 

  70. Sett S, Thakurta PKG (2015) Multi objective optimization on clustered mobile networks: an aco based approach. In: Information systems design and intelligent applications. Springer, pp 123–133

    Google Scholar 

  71. Shmygelska A, Hoos HH (2005) An ant colony optimisation algorithm for the 2d and 3d hydrophobic polar protein folding problem. BMC Bioinform 6:30

    CrossRef  Google Scholar 

  72. Socha K, Knowles JD, Sampels M (2002) A MAX-MIN ant system for the university course timetabling problem. In: Ant algorithms, Third international workshop, ANTS 2002, Brussels, Belgium, September 12–14, 2002, Proceedings, pp 1–13

    Google Scholar 

  73. Socha K, Sampels M, Manfrin M (2003) Ant algorithms for the university course timetabling problem with regard to the state-of-the-art. In: Applications of evolutionary computing, EvoWorkshop 2003: EvoBIO, EvoCOP, EvoIASP, EvoMUSART, EvoROB, and EvoSTIM, Essex, UK, April 14–16, 2003, Proceedings, pp 334–345

    Google Scholar 

  74. Solnon C (2000) Solving permutation constraint satisfaction problems with artificial ants. In: ECAI 2000, Proceedings of the 14th European conference on artificial intelligence, Berlin, Germany, August 20–25, 2000, pp 118–122

    Google Scholar 

  75. Solnon C (2002) Ants can solve constraint satisfaction problems. IEEE Trans Evol Comput 6(4):347–357

    CrossRef  Google Scholar 

  76. Sotelo-Figueroa MA, Baltazar R, Carpio JM (2010) Application of the bee swarm optimization BSO to the knapsack problem. In: Soft computing for recognition based on biometrics, pp 191–206

    Google Scholar 

  77. Stützle T (1999) Local search algorithms for combinatorial problems—analysis, improvements, and new applications, DISKI, vol 220. Infix

    Google Scholar 

  78. Stützle T, Hoos H (1997) Max-min ant system and local search for the traveling salesman problem. In: IEEE International conference on evolutionary computation, 1997. IEEE, pp 309–314

    Google Scholar 

  79. Stützle T, Hoos HH (2000) MAX-MIN ant system. Fut Gener Comput Syst 16(8):889–914

    CrossRef  MATH  Google Scholar 

  80. T’Kindt V, Monmarché N, Tercinet F, Laügt D (2002) An ant colony optimization algorithm to solve a 2-machine bicriteria flowshop scheduling problem. Eur J Oper Res 142(2):250–257

    CrossRef  MathSciNet  MATH  Google Scholar 

  81. Triantaphyllou E (2000) Multi-criteria decision making methods. In: Multi-criteria decision making methods: a comparative study. Springer, pp 5–21

    Google Scholar 

  82. Vira C, Haimes YY (1983) Multiobjective decision making: theory and methodology, vol 8. North-Holland, New York

    Google Scholar 

  83. Wierzbicki AP (1982) A mathematical basis for satisficing decision making. Math Model 3(5):391–405

    CrossRef  MathSciNet  MATH  Google Scholar 

  84. Wierzbicki AP (1986) On the completeness and constructiveness of parametric characterizations to vector optimization problems. Oper Res Spektr 8(2):73–87

    Google Scholar 

  85. Yagmahan B, Yenisey MM (2010) A multi-objective ant colony system algorithm for flow shop scheduling problem. Expert Syst Appl 37(2):1361–1368

    CrossRef  Google Scholar 

  86. Yazdi FR (2013) Ant colony with colored pheromones routing for multi objectives quality of services in wsns. Int J Res Comput Sci 3(1):1

    CrossRef  MathSciNet  Google Scholar 

  87. Zeleny M, Cochrane JL (1973) Multiple criteria decision making. University of South Carolina Press

    Google Scholar 

  88. Zitzler E, Thiele L (1999) Multiobjective evolutionary algorithms: a comparative case study and the strength pareto approach. IEEE Trans Evol Comput 3(4):257–271

    CrossRef  Google Scholar 

  89. Zitzler E, Deb K, Thiele L (2000) Comparison of multiobjective evolutionary algorithms: empirical results. Evol Comput 8(2):173–195

    CrossRef  Google Scholar 

  90. Zitzler E, Thiele L, Laumanns M, Fonseca CM, da Fonseca VG (2003) Performance assessment of multiobjective optimizers: an analysis and review. IEEE Trans Evol Comput 7(2):117–132

    CrossRef  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Institute of System Security, Technische Universität Braunschweig, Rebenring 56, 38106, Braunschweig, Germany

    Ansgar Kellner

Authors
  1. Ansgar Kellner
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ansgar Kellner .

Editor information

Editors and Affiliations

  1. Dept of Computer Science and Engineering, SOA University Dept of Computer Science and Engineering, Bhubaneswar, Odisha, India

    Srikanta Patnaik

  2. School of Science and Technology, Middlesex University School of Science and Technology, London, United Kingdom

    Xin-She Yang

  3. School of H.S.E., University of Hyogo School of H.S.E., Himeji, Japan

    Kazumi Nakamatsu

Rights and permissions

Reprints and Permissions

Copyright information

© 2017 Springer International Publishing AG

About this chapter

Cite this chapter

Kellner, A. (2017). Multi-objective Ant Colony Optimisation in Wireless Sensor Networks. In: Patnaik, S., Yang, XS., Nakamatsu, K. (eds) Nature-Inspired Computing and Optimization. Modeling and Optimization in Science and Technologies, vol 10. Springer, Cham. https://doi.org/10.1007/978-3-319-50920-4_3

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-50920-4_3

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-50919-8

  • Online ISBN: 978-3-319-50920-4

  • eBook Packages: EngineeringEngineering (R0)

search

Navigation