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A memetic ant colony optimization algorithm for the dynamic travelling salesman problem

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Abstract

Ant colony optimization (ACO) has been successfully applied for combinatorial optimization problems, e.g., the travelling salesman problem (TSP), under stationary environments. In this paper, we consider the dynamic TSP (DTSP), where cities are replaced by new ones during the execution of the algorithm. Under such environments, traditional ACO algorithms face a serious challenge: once they converge, they cannot adapt efficiently to environmental changes. To improve the performance of ACO on the DTSP, we investigate a hybridized ACO with local search (LS), called Memetic ACO (M-ACO) algorithm, which is based on the population-based ACO (P-ACO) framework and an adaptive inver-over operator, to solve the DTSP. Moreover, to address premature convergence, we introduce random immigrants to the population of M-ACO when identical ants are stored. The simulation experiments on a series of dynamic environments generated from a set of benchmark TSP instances show that LS is beneficial for ACO algorithms when applied on the DTSP, since it achieves better performance than other traditional ACO and P-ACO algorithms.

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Notes

  1. See http://comopt.ifi.uni-heidelberg.de/software/TSPLIB95/.

  2. See http://www.aco-metaheuristic.org/aco-code.

  3. The term M-ACOs denotes all ACO-based MAs used in the experiments

  4. The corresponding results of Best Robustness and Average Robustness for the environment with varying magnitudes and frequencies are similar with our basic experimental results.

References

  • Acampora G, Loia V, Gaeta M (2010) Exploring e-learning knowledge through othological memetic agents. IEEE Comput Intell Mag 5(2):66–77

    Article  Google Scholar 

  • Angus A (2006) Niching for population-based ant colony optimization. In: Proceedings of the 2nd IEEE international conference on e-science and grid computing, pp 15–22

  • Bi X, Luo G (2007) The improvement of ant colony algorithm based on the inver-over operator. In: Proceedings of the international conference on mechanics and automation, pp 2383–2387

  • Bonabeau E, Dorigo M, Theraulaz G (1997) Swarm intelligence: from natural to artificial systems. Oxford University Press, New York

    Google Scholar 

  • Branke J (1999) Memory enhanced evolutionary algorithms for changing optimization problems. In: Proceedings of the 1999 IEEE congress on evolutionary computation, pp 1875–1882

  • Branke J, Kau\(\beta\)ler T, Schmidth C, Schmeck H (2000) A multipopulation approach to dynamic optimization problems. In: Proceedings of the 4th international conference on adaptive computing in design and manufacturing, pp 299–308

  • Branke J, Salihoğlu E, Uyar Ş (2005) Towards an analysis of dynamic environments. In: Proceedings of the 2005 genetic and evolutionary computation conference, pp 1433–1400

  • Bullnheimer B, Hartl RF, Strauss C (1999) A new rank based version of the ant system—a computational study, technical report. Institute of Management Science, University of Vienna, Austria

  • Caponio A, Cascella GL, Neri F, Salvatore N, Summerr M (2007) A fast adaptive memetic algorithm for online and offline control design of PMSM drives. IEEE Trans Syst Man Cybern B Cybern 37:28–41

    Article  Google Scholar 

  • Colorni A, Dorigo M, Maniezzo V (1992) Distributed optimization by ant colonies. In: Proceedings of the 1st European conference on artificial life, pp 134–142

  • Di Caro G, Dorigo M (1998) Ant net: distributed stigmergetic control for communications networks. J Artif Intell Res 9:317–365

    MATH  Google Scholar 

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

    Article  Google Scholar 

  • Dorigo M, St\(\ddot{\hbox{u}}\)tzle T (2004) Ant colony optimization. The MIT Press, London

  • Dorigo M, Maniezzo V, Colorni A (1996) Ant system: optimization by a colony of cooperating agents. IEEE Trans Syst Man Cybern B Cybern 26(1):29–41

    Article  Google Scholar 

  • Eriksson R, Olsson B (2002) On the behaviour of evolutionary global-local hybrids with dynamic fitness functions. In: Proceedings of the 7th international conference on parallel problem solving from nature, pp 13–12

  • Eriksson R, Olsson B (2004) On the performance of evolutionary algorithms with life-time adaptation in dynamic fitness landscapes. In: Proceedings of the 2004 IEEE congress on evolutionary computation, pp 1293–1300

  • Eyckelhof CJ, Snoek M (2002) Ant systems for a dynamic TSP. In: Proceedings of the 3rd international workshop on ant algorithms, pp 88–99

  • Grefenestette JJ (1992) Genetic algorithms for changing environments. In: Proceedings of the 2nd international conference on parallel problem solving from nature, pp 137–144

  • Guntsch M, Middendorf M (2001) Pheromone modification strategies for ant algorithms applied to dynamic TSP. In: EvoWorkshops 2001: Application of evolutionary computing, pp 213–222

  • Guntsch M, Middendorf M (2002a) Applying population based ACO to dynamic optimization problems. In: Proceedings of the 3rd international workshop on ant algorithms, LNCS 2463, pp 111–122

  • Guntsch M, Middendorf M (2002b) A population based approach for ACO. In: EvoWorkshops 2002: Application of evolutionary computing, pp 72–81

  • Guntsch M, Middendorf M, Schmeck H (2001) An ant colony optimization approach to dynamic TSP. In: Proc of the genetic and evolutionary computation conference, pp 860–867

  • Guo T, Michalewicz Z (1998) Inver-over operator for the TSP. In: Proceedings of the 5th international conference on parallel problem solving from nature, pp 803–812

  • He J, Yao X (2002) From an Individual to a population: an analysis of the first hitting time of population-bases evolutionary algorithms. IEEE Trans Evol Comput 6(5):495–511

    Article  Google Scholar 

  • Holland J (1975) Adaption in Natural and artificial systems. University of Michigan Press, Ann Arbor

  • Isaacs A, Puttige V, Ray T, Smith W, Anavatti S (2008) Development of a memetic algorithm for dynamic multi-objective optimization and its application for online neural network modelling of UAVs. IEEE international joint conference on neural networks (IJCNN 2008), pp 548–554

  • Ishibuchi H, Yoshida T, Murata T (2003) Balance between genetic search and local search in memetic algorithms for multiobjective permutation flowshop scheduling. IEEE Trans Evol Comput 7(2):204–233

    Article  Google Scholar 

  • Jen E (2003) Stable or robust. What’s the difference. Complexity 8(3):12–18

    MathSciNet  Google Scholar 

  • Jin Y, Branke J (2005) Evolutionary optimization in uncertain environments—a survey. IEEE Trans Evol Comput 9(3):303–317

    Article  Google Scholar 

  • Krasnogor N, Smith J (2005) A tutorial for competent memetic algorithms: model, taxonomy, and design issues. IEEE Trans Evol Comput 9(5):474–487

    Article  Google Scholar 

  • Lee Z-J, Su S-F, Chuang C-C, Liu K-H (2006) Genetic algorithm with ant colony optimization for multiple sequence alignment. Appl Soft Comput 8(1):55–78

    Article  Google Scholar 

  • Lim D, Ong Y-S, Jin Y, Sendhoff B, Lee BS (2007a) Efficient hierarchical parallel genetic algorithms using grid computing. Future Gen Comput Syst 23(4):658–670

    Article  Google Scholar 

  • Lim D, Ong Y-S, Lim MH, Jin Y (2007b) Single/multi-objective inverse robust evolutionary design methodology in the presence of uncertainty, evolutionary computation in dynamic and uncertain environments. Ser Stud Comput Intell 51:437–456

    Article  Google Scholar 

  • Lim KK, Ong Y-S, Lim MH, Chen X, Agarwal A (2008) Hybrid ant colony algorithms for path planning in sparse graphs. Soft Comput 12(10):981–994

    Article  Google Scholar 

  • Lin S, Kerninghan BW (1972) An effective heuristic algorithm for the traveling salesman problem. Oper Res 21:498–516

    Article  Google Scholar 

  • Liu B, Wang L, Jun YH (2007) An effective PSO-based memetic algorithm for flow shop scheduling. IEEE Trans Syst Man Cybern B Cybern 37(1):18–27

    Article  Google Scholar 

  • Mavrovouniotis M, Yang S (2010) Ant colony optimization with immigrants schemes for dynamic environments. In: Proceedings of parallel problem solving from nature PPSN XI, pp 371–380

  • Mei Y, Tang K, Yao X (2009) Improved memetic algorithm for capacitated arc routing problem. In: IEEE congress on evolutionary computation, pp 1699–1706

  • Michalewicz Z (1999) Genetic algorithms + data structures = evolution programs, 3rd edn. Springer, Berlin

  • Morrison RW (2003) Performance measurement in dynamic environments. GECCO workshop on evolutionary algorithms for dynamic optimisation problems, pp 5–8

  • Neri F, Mininno E (2010) Memetic compact differential evolution for cartesian robot control. IEEE Comput Intell Mag 5(2):54–65

    Article  Google Scholar 

  • Neri F, Toivanen J, Cascella GL, Ong Y-S (2007) An adaptive multimeme algorithm for designing HIV multidrug therapies. IEEE ACM Trans Comput Biol Bioinform 4(2):264–278

    Article  Google Scholar 

  • Neumann F, Witt C (2009) Runtime analysis of a simple ant colony optimization algorithm. Algorithmica 54(2):243–255

    Article  MathSciNet  MATH  Google Scholar 

  • Ong Y-S, Keane AJ (2004) Meta-Lamarckian learning in memetic algorithms. IEEE Trans Evol Comput 8(2):99–110

    Article  Google Scholar 

  • Ong Y-S, Lim MH, Zhu N, Wong KW (2006) Classification of adaptive memetic algorithms: a comparative study. IEEE Trans Syst Man Cybern B Cybern 36(1):141–152

    Article  Google Scholar 

  • Ong Y-S, Lim MH, Chen X (2010) Research frontier: memetic computing—past, present and future. IEEE Comput Intell Mag 5(2):24–36

    Article  Google Scholar 

  • Raman N, Talbot FB (1993) The job shop tardiness problem: a decomposition approach. Eur J Oper Res 69(2):187–199

    Article  MATH  Google Scholar 

  • Rand W, Riolo R (2005) Measurements for understand the behaviour of the genetic algorithm in dynamic environments: a case study using the shaky ladder hyperplane-defined functions. In: Proceedings of the 2005 genetic and evolutionary computation, pp 32–38

  • Smith JE (2007) Coevolving memetic algorithms: a review and progress report. IEEE Trans Syst Man Cybern B Cybern 37(1):6–17

    Article  Google Scholar 

  • St\(\ddot{\hbox{u}}\)tzle T, Hoos H (1997) The MAX-MIN ant system and local search for the traveling salesman problem. In: Proceedings of the 1997 IEEE international conference on evolutionary computation, pp 309–314

  • St\(\ddot{\hbox{u}}\)tzle T, Hoos H (2000) MAX-MIN ant system. Fut Gen Comput Syst 8(16):889–914

    Google Scholar 

  • Talbi EG, Bachelet V (2006) Cosearch: a parallel cooperative metaheuristic. J Math Model Algorithms 5(1):5–22

    Article  MathSciNet  MATH  Google Scholar 

  • Tang K, Yao X (2007) Memetic algorithm for VLSI floorplanning. IEEE Trans Syst Man Cybern B Cybern 37(1):62–69

    Article  Google Scholar 

  • Tang K, Mei Y, Yao X (2009) Memetic algorithm with extended neighborhood search for capacitated arc routing problems. IEEE Trans Evol Comput 13(5):1151–1166

    Article  Google Scholar 

  • Wang H, Wang D, Yang S (2009a) A memetic algorithm with adaptive hill climbing strategy for dynamic optimization problems. Soft Comput 13(8–9):763–780

    Article  Google Scholar 

  • Wang J, Osigie E, Thulasiraman P, Thulasiram RK (2009b) HOPNET: a hybrid ant colony optimization routing algorithm for mobile ad hoc network. Ad Hoc Networks 7(4):690–705

    Article  Google Scholar 

  • Weicker K (2002) Performance measures for dynamic environments. In: Proceedings of parallel problem solving from nature PPSN VII, pp 64–73

  • William EH, Krasnogor N, Smith JE (eds) (2005) Recent advances in memetic algorithms. Springer, Berlin

  • Xing L-N, Chen Y-W, Yang K-W (2008a) A hybrid approach combining an improved genetic algorithm and optimization strategies for the asymetric traveling salesman problem. Eng Appl Artif Intell 21(8):1370–1370

    Article  Google Scholar 

  • Xing L-N, Chen Y-W, Yang K-W (2008b) Double layer ant colony optimization for multi-objective flexible job shop scheduling problems. New Gen Comput 26(4):313–327

    Article  MATH  Google Scholar 

  • Xing L-N, Chen Y-W, Wang P, Zhao Q, Xiong J (2010) A knowledge-based ant colony optimization for flexible job shop scheduling problems. Appl Soft Comput 10(3):888–896

    Article  Google Scholar 

  • Yang S (2008) Genetic algorithms with memory and elitism based immigrants in dynamic environments. Evol Comput 16(3):385–416

    Article  Google Scholar 

  • Yu X, Tang K, Chen T, Yao X (2009) Empirical analysis of evolutionary algorithms with immigrants schemes for dynamic optimization. Memetic Comput 1(1):3–24

    Article  Google Scholar 

  • Zhang X, Tang L (2008) A new hybrid ant colony optimization algorithm for the vehicle routing problem. Pattern Recogn Lett 30(9):848–855

    Article  MathSciNet  Google Scholar 

  • Zhou Z, Ong Y-S, Lim MH (2007) Memetic algorithm using multi-surrogates for computationally expensive optimization problems. Soft Comput 11(9):873–888

    Article  Google Scholar 

Download references

Acknowledgments

The authors would like to thank the guest editors and anonymous reviewers for their thoughtful suggestions and constructive comments. This work was supported by the Engineering and Physical Sciences Research Council (EPSRC) of UK under Grant EP/E060722/01 and Grant EP/E060722/02.

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Authors and Affiliations

  1. Department of Computer Science, University of Leicester, University Road, Leicester, LE1 7RH, UK

    Michalis Mavrovouniotis

  2. Department of Information Systems and Computing, Brunel University, Uxbridge, Middlesex, UB8 3PH, UK

    Shengxiang Yang

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  1. Michalis Mavrovouniotis
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  2. Shengxiang Yang
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Correspondence to Michalis Mavrovouniotis.

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Mavrovouniotis, M., Yang, S. A memetic ant colony optimization algorithm for the dynamic travelling salesman problem. Soft Comput 15, 1405–1425 (2011). https://doi.org/10.1007/s00500-010-0680-1

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