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Fuzzy goal programming-based ant colony optimization algorithm for multi-objective topology design of distributed local area networks

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Abstract

Topology design of a distributed local area network (DLAN) is a complex optimization problem and has been generally modelled as a single-objective optimization problem. Traditionally, iterative techniques such as genetic algorithms and simulated annealing have been used to solve the problem. In this paper, we formulated the DLAN topology design problem as a multi-objective optimization problem considering five design objectives. These objectives are network reliability, network availability, average link utilization, monetary cost, and average network delay. The multi-objective nature of the problem has been addressed by incorporating a fuzzy goal programming approach to combine the individual design objectives into a single-objective function. The objective function is then optimized using the ant colony algorithm adapted for the problem. The performance of the proposed fuzzy goal programming-based ant colony optimization algorithm (GPACO) is evaluated with respect to the algorithm control parameters, namely pheromone deposit and evaporation rate, colony size and heuristic values. A comparative study was also done using four other multi-objective optimization algorithms which are non-dominated sorting genetic algorithm II, archived multi-objective simulated annealing algorithm, lexicographic ant colony optimization, and Pareto-dominance ant colony optimization. Results revealed that, in general, GPACO was able to find solutions of higher quality as compared to the other four algorithms.

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References

  1. 1.

    Kumar R, Banerjee N (2003) Multicriteria network design using evolutionary algorithm. In: Genetic and evolutionary computation GECCO 2003. Springer, pp 2179–2190

  2. 2.

    Ersoy C, Panwar S (1993) Topological design of interconnected LAN/MAN networks. IEEE J Sel Area Commun 11:1172–1182

  3. 3.

    Nezamoddini N, Lam S (2015) Reliability and topology based network design using pattern mining guided genetic algorithm. Expert Syst Appl 42:7483–7492

  4. 4.

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

  5. 5.

    Gen M, Ida K, Kim J (1998) A spanning tree-based genetic algorithm for bicriteria topological network design. In: IEEE international conference on evolutionary computation, pp 164–173

  6. 6.

    Xianhai T, Weidong J, Duo Z (2003) The application of multicriterion satisfactory optimization in computer networks design. In: Parallel and distributed computing, applications and technologies, pp 660–664

  7. 7.

    White A, Mann J, Smith G (1999) Genetic algorithms and network ring design. Ann Oper Res 6(1):347–371

  8. 8.

    Shukla N, Dashora Y, Tiwari M, Shankar R (2013) Design of computer network topologies: a vroom inspired psychoclonal algorithm. Appl Math Model 37:888–902

  9. 9.

    Khan SA, Engelbrecht AP (2008) A fuzzy ant colony optimization algorithm for topology design of distributed local area networks. In: IEEE swarm intelligence symposium, pp 1–7

  10. 10.

    Khan SA, Engelbrecht AP (2012) A fuzzy particle swarm optimization algorithm for computer communication network topology design. Appl Intell 36(1):161–177

  11. 11.

    Khan SA, Engelbrecht AP (2009) Fuzzy hybrid simulated annealing algorithms for topology design of switched local area networks. Soft Comput 3(1):45–61

  12. 12.

    Kamiyama N (2016) Generating desirable network topologies using multiagent system. Comput Commun 76:87–100

  13. 13.

    Holland JH (1975) Adaptation in natural and artificial systems. University of Michigan Press, Ann Arbor

  14. 14.

    Kirkpatrick S, Gelatt C Jr, Vecchi M (1983) Optimization by simulated annealing. Science 220(498–516):1983

  15. 15.

    Miettinen K (2001) Some methods for nonlinear multi-objective optimization. In: IEEE/ACM 1st international conference on evolutionary multi-criterion optimization, Lecture notes in computer science, vol 1993. Springer, pp 1–20

  16. 16.

    Elshqeirat B, Soh S, Rai S, Lazarescu M (2014) A dynamic programming algorithm for reliable network design. IEEE Trans Reliab 63(2):443–454

  17. 17.

    Elshqeirat B, Soh S, Rai S, Lazarescu M (2015) Topology design with minimal cost subject to network reliability constraint. IEEE Trans Reliab 64(1):118–131

  18. 18.

    Rodriguez-Martin I, Salazar-Gonzalez J, Yaman H (2016) A branch-and-cut algorithm for two-level survivable network design problems. Comput Oper Res 67:102–112

  19. 19.

    Hannan EL (1981) On fuzzy goal programming. Decis Sci 12(3):522–531

  20. 20.

    Gravel M, Price W, Gagne C (2002) Scheduling continuous casting of aluminum using a multiple objective ant colony optimization metaheuristic. Eur J Oper Res 143(1):218–229

  21. 21.

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

  22. 22.

    Deb K, Pratap A, Agarwal S, Meyarivan T (2002) A fast and elitist multi-objective genetic algorithm: NSGA-II. IEEE Trans Evol Comput 6(2):182197

  23. 23.

    Bandyopadhyay S, Saha S, Maulik U, Deb K (2009) A simulated annealing-based multiobjective optimization algorithm: AMOSA. IEEE Trans Evol Comput 12(3):269–283

  24. 24.

    Thompson D, Bilbro G (2000) Comparison of a genetic algorithm with a simulated annealing algorithm for the design of an ATM network. IEEE Commun Lett 4(8):267–269

  25. 25.

    Pierre S, Legault G (1998) A genetic algorithm for designing distributed computer network topologies. IEEE Trans Syst Man Cybern 28(2):249–258

  26. 26.

    Ombuki B, Nakamura M, Nakao Z, Onaga I (1999) Evolutionary computation for topological optimization of 3-connected computer networks. In: IEEE conference on systems, man, and cybernetics, pp 659–664

  27. 27.

    Din D (2015) Genetic algorithm for virtual topology design on MLR WDM networks. Opt Switch Netw 18:20–34

  28. 28.

    Dengiz B, Altiparmak F, Smith AE (1997) Efficient optimization of all-terminal reliable networks, using an evolutionary approach. IEEE Trans Reliab 46(1):18–26

  29. 29.

    Mostafa M, Eid S (2000) A genetic algorithm for joint optimization of capacity and flow assignment in packet switched networks. In: 17th national radio science conference, pp C51–C56

  30. 30.

    Fetterolf P (1990) Design of data networks with spanning tree bridges. In: IEEE international conference on systems, man, and cybernetics, pp 298–300

  31. 31.

    Soni S, Narasimhan S, LeBlanc L (2004) Telecommunication access network design with reliability constraints. IEEE Trans Reliab 53(4):532–541

  32. 32.

    Harmatos J, Szentes A, Godor I (2000) Planning of tree-topology UMTS terrestrial access networks. In: Proceedings of the 11th IEEE international symposium on personal, indoor and mobile radio communications, vol 1, pp 353–357

  33. 33.

    Dengiz B, Altiparmak F, Belgin O (2010) Design of reliable communication networks: a hybrid ant colony optimization algorithm. IIE Trans 42(4):273–287

  34. 34.

    Ashraf M, Mishra R (2013) Extended ant colony optimization algorithm EACO for efficient design of networks and improved reliability. In: International conference on heterogeneous networking for quality, reliability, security and robustness. Springer, pp 939–950

  35. 35.

    Premprayoon P, Wardkein P (2005) Topological communication network design using ant colony optimization. In: ICACT 2005. The 7th international conference on advanced communication technology, 2005, vol 2. IEEE, pp 1147–1151

  36. 36.

    Watcharasitthiwat K, Wardkein P (2009) Reliability optimization of topology communication network design using an improved ant colony optimization. Comput Electr Eng 35(5):730–747

  37. 37.

    Miyoshi T, Shimizu S, Tanaka Y (2003) Fast topological design with simulated annealing for multicast networks. In: 7th international symposium on computers and communications, pp 959–966

  38. 38.

    Elbaum R, Sidi M (1996) Topological design of local-area networks using genetic algorithms. IEEE/ACM Trans Netw (TON) 4(5):766–778

  39. 39.

    Atiqullah M, Rao S (1993) Reliability optimization of communication networks using simulated annealing. Microelectron Reliab 33(9):1303–1319

  40. 40.

    Dengiz B, Alabas C (2001) A simulated annealing algorithm for design of computer communication networks. World Multiconf Syst Cybern Inform 5:188–193

  41. 41.

    Demirkol I, Ersoy C, Caglayan MU, Delić H (2001) Location area planning in cellular networks using simulated annealing. In: Proceedings of the IEEE conference on computer communications, pp 13–20

  42. 42.

    Ali M (2000) Assignment of multicast switches in optical networks. In: Proceedings of the 25th annual IEEE conference on local computer networks, pp 381–382

  43. 43.

    Khan SA, Engelbrecht AP (2007) A new fuzzy operator and its application to topology design of distributed local area networks. Inf Sci 177(12):2692–2711

  44. 44.

    Rehman S, Khan S (2016) A fuzzy logic based multi-criteria wind turbine selection strategy a case study of Qassim, Saudi Arabia. Energies 9:872

  45. 45.

    Jereb L (1998) Network reliability: models, measures and analysis. In: Proceedings of the 6th IFIP workshop on performance modelling and evaluation of atm networks, tutorial papers. Ilkley, p T02

  46. 46.

    Khan SA (2009) Design and analysis of evolutionary and swarm intelligence techniques for topology design of distributed local area networks. PhD thesis, University of Pretoria

  47. 47.

    Dearborn R, Napolitan R, Whitcomb L, Wilson J (2006) The costs of downtime: North American medium businesses. In: Infonetics research press release

  48. 48.

    Tornatore M, Maier GA, Pattavina A, Villa M, Righetti A, Clemente R, Martinelli M (2003) Availability optimization of static path-protected WDM networks. In: Optical fiber communication conference. Optical Society of America, p FA5

  49. 49.

    Igai K, Oki E (2011) A simple estimation scheme for upper bound of link utilization based on RTT measurement. Cyber J Multidiscip J Sci Technol J Sel Areas Telecommun (JSAT) 10–16

  50. 50.

    Jasem HN, Zukarnain ZA, Othman M, Subramaniam S (2010) On the delay and link utilization with the new-additive increase multiplicative decrease congestion avoidance and control algorithm. Sci Res Essays 5(23):3719–3729

  51. 51.

    Jasem HN, Zukarnain ZA, Mohamed O, Shamala S (2010) Evaluation study for delay and link utilization with the new-additive increase multiplicative decrease congestion avoidance and control algorithm. Preprint arXiv:1001.2848

  52. 52.

    Pucha H, Zhang Y, Mao ZM, Hu YC (2007) Understanding network delay changes caused by routing events. In: ACM SIGMETRICS performance evaluation review, vol 35. ACM, pp 73–84

  53. 53.

    Sportack MA, Fairweather J (1999) IP routing fundamentals. Cisco Press, Indianapolis

  54. 54.

    Charnes A, Cooper WW, Ferguson RO (1955) Optimal estimation of executive compensation by linear programming. Manage Sci 1(2):138–151

  55. 55.

    Aouni B, Kettani O (2001) Goal programming model: a glorious history and a promising future. Eur J Oper Res 133(2):225–231

  56. 56.

    Zadeh LA (1965) Fuzzy sets. Inf Control 8(3):338–353

  57. 57.

    Jones D, Tamiz M (2010) Goal programming variants. In: Hillier FS (ed) Practical goal programming. Springer, New York, pp 11–22

  58. 58.

    Güneş M, Umarosman N (2005) Fuzzy goal programming approach on computation of the fuzzy arithmetic mean. Math Comput Appl 10(2):211–220

  59. 59.

    Mekidiche M, Belmokaddem M (2012) Application of weighted additive fuzzy goal programming approach to quality control system design. Int J Intell Syst Appl (IJISA) 4(11):14

  60. 60.

    Mekidiche M, Belmokaddem M, Djemmaa Z (2013) Weighted additive fuzzy goal programming approach to aggregate production planning. Int J Intell Syst Appl (IJISA) 5(4):20

  61. 61.

    Romero C (2004) A general structure of achievement function for a goal programming model. Eur J Oper Res 153(3):675–686

  62. 62.

    Dorigo M (2007) Ant colony optimization. Scholarpedia 2(3):1461

  63. 63.

    Mohan BC, Baskaran R (2012) A survey: ant colony optimization based recent research and implementation on several engineering domain. Expert Syst Appl 39(4):4618–4627

  64. 64.

    Behravan H (2012) Swarm intelligence/ant colonies through applications. In: Computational intelligence II. University of Estern Finland

  65. 65.

    Dorigo M, Stützle T (2003) The ant colony optimization metaheuristic: algorithms, applications, and advances. In: Glover F, Kochenberger GA (eds) Handbook of metaheuristics. Springer, New York, pp 250–285

  66. 66.

    Dorigo M, Birattari M, Stützle T (2006) Ant colony optimization. IEEE Comput Intell Mag 1(4):28–39

  67. 67.

    Selvi V, Umarani R (2010) Comparative analysis of ant colony and particle swarm optimization techniques. Int J Comput Appl (0975–8887) 5(4):1–6

  68. 68.

    Dorigo M, Stützle T (2004) Ant colony optimization. Massachusetts Institute of Technology, Cambridge

  69. 69.

    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

  70. 70.

    Coello-Coello CA (1999) A comprehensive survey of evolutionary-based multiobjective optimization techniques. Knowl Inf Syst 1(3):269–308

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Acknowledgements

The authors would like to thank Amani Saad for her assistance.

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Correspondence to Salman A. Khan.

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Khan, S.A., Mahmood, A. Fuzzy goal programming-based ant colony optimization algorithm for multi-objective topology design of distributed local area networks. Neural Comput & Applic 31, 2329–2347 (2019). https://doi.org/10.1007/s00521-017-3191-5

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Keywords

  • Network design
  • Ant colony optimization
  • Multi-objective optimization
  • Heuristics