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2019 | OriginalPaper | Buchkapitel

10. Ant Colony Optimization: Overview and Recent Advances

verfasst von : Marco Dorigo, Thomas Stützle

Erschienen in: Handbook of Metaheuristics

Verlag: Springer International Publishing

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Abstract

Ant Colony Optimization (ACO) is a metaheuristic that is inspired by the pheromone trail laying and following behavior of some ant species. Artificial ants in ACO are stochastic solution construction procedures that build candidate solutions for the problem instance under concern by exploiting (artificial) pheromone information that is adapted based on the ants’ search experience and possibly available heuristic information. Since the proposal of Ant System, the first ACO algorithm, many significant research results have been obtained. These contributions focused on the development of high performing algorithmic variants, the development of a generic algorithmic framework for ACO algorithm, successful applications of ACO algorithms to a wide range of computationally hard problems, and the theoretical understanding of important properties of ACO algorithms. This chapter reviews these developments and gives an overview of recent research trends in ACO.

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Vorheriges Kapitel An Accelerated Introduction to Memetic Algorithms

Nächstes Kapitel Swarm Intelligence

Other approximate methods are also conceivable. For example, when stopping exact methods, like Branch and Bound, before completion [11, 104] (e.g., using some given time bound, or when some guarantee on solution quality is obtained through the use of lower and upper bounds), we can convert exact algorithms into approximate ones.

The adaptation to maximization problems is straightforward.

Static problems are those whose topology and costs do not change while they are being solved. This is the case, for example, for the classic TSP, in which city locations and intercity distances do not change during the algorithm’s run-time. In contrast, in dynamic problems the topology and costs can change while solutions are built. An example of such a problem is routing in telecommunications networks [52], in which traffic patterns change all the time.

The experiment described was originally executed using a laboratory colony of Argentine ants (Iridomyrmex humilis). It is known that these ants deposit pheromone both when leaving and when returning to the nest [89].

In the ACO literature, this is often called differential path length effect.

A process like this, in which a decision taken at time t increases the probability of making the same decision at time T > t is said to be an autocatalytic process. Autocatalytic processes exploit positive feedback.

Note that, when applied to symmetric TSPs, the edges are considered to be bidirectional and edges (i, j) and (j, i) are both updated. This is different for the ATSP, where edges are directed; in this case, an ant crossing edge (i, j) will update only this edge and not edge (j, i).

ACS was an offspring of Ant-Q [82], an algorithm intended to create a link between reinforcement learning [179] and Ant Colony Optimization. Computational experiments have shown that some aspects of Ant-Q, in particular the pheromone update rule, could be strongly simplified without affecting performance. It is for this reason that Ant-Q was abandoned in favor of the simpler and equally good ACS.

The maximum time for the largest instances was 20 min on a 450 MHz Pentium III PC with 256 MB RAM. Programs were written in C++ and the PC was run under Red Hat Linux 6.1.

There have been several proposals of ant-inspired algorithms for continuous optimization [17, 73, 142]. However, these differ strongly from the underlying ideas of ACO (for example, they use direct communication among ants) and therefore cannot be considered as algorithms falling into the framework of the ACO metaheuristic.

A. Acan, An external memory implementation in ant colony optimization, in Ant Colony Optimization and Swarm Intelligence: 4th International Workshop, ANTS 2004, ed. by M. Dorigo, M. Birattari, C. Blum, L. M. Gambardella, F. Mondada, T. Stützle. Lecture Notes in Computer Science, vol. 3172 (Springer, Heidelberg, 2004), pp. 73–84

A. Acan, An external partial permutations memory for ant colony optimization, in Evolutionary Computation in Combinatorial Optimization, ed. by G. Raidl, J. Gottlieb. Lecture Notes in Computer Science, vol. 3448 (Springer, Heidelberg, 2005), pp. 1–11

I. Alaya, C. Solnon, K. Ghédira, Ant colony optimization for multi-objective optimization problems, in 19th IEEE International Conference on Tools with Artificial Intelligence (ICTAI 2007), vol. 1 (IEEE Computer Society, Los Alamitos, 2007), pp. 450–457

D.A. Alexandrov, Y.A. Kochetov, The behavior of the ant colony algorithm for the set covering problem, in Operations Research Proceedings 1999, ed. by K. Inderfurth, G. Schwödiauer, W. Domschke, F. Juhnke, P. Kleinschmidt, G. Wäscher (Springer, Berlin, 2000), pp. 255–260

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

D. Applegate, R.E. Bixby, V. Chvátal, W.J. Cook, The Traveling Salesman Problem: A Computational Study (Princeton University Press, Princeton, 2006)

P. Balaprakash, M. Birattari, T. Stützle, Z. Yuan, M. Dorigo, Estimation-based ant colony optimization algorithms for the probabilistic travelling salesman problem. Swarm Intell. 3(3), 223–242 (2009)

P. Balaprakash, M. Birattari, T. Stützle, Z. Yuan, M. Dorigo, Estimation-based metaheuristics for the single vehicle routing problem with stochastic demands and customers. Comput. Optim. Appl. 61(2), 463–487 (2015)

A. Bauer, B. Bullnheimer, R.F. Hartl, C. Strauss, An ant colony optimization approach for the single machine total tardiness problem, in Proceedings of the 1999 Congress on Evolutionary Computation (CEC’99) (IEEE Press, Piscataway, 1999), pp. 1445–1450

10.

R. Beckers, J.-L. Deneubourg, S. Goss, Modulation of trail laying in the ant Lasius niger (hymenoptera: Formicidae) and its role in the collective selection of a food source. J. Insect Behav. 6(6), 751–759 (1993)

11.

R. Bellman, A.O. Esogbue, I. Nabeshima, Mathematical Aspects of Scheduling and Applications (Pergamon Press, New York, 1982)

12.

S. Benedettini, A. Roli, L. Di Gaspero, Two-level ACO for haplotype inference under pure parsimony, in Ant Colony Optimization and Swarm Intelligence, 6th International Workshop, ANTS 2008, ed. by M. Dorigo, M. Birattari, C. Blum, M. Clerc, T. Stützle, A.F.T. Winfield. Lecture Notes in Computer Science, vol. 5217 (Springer, Heidelberg, 2008), pp. 179–190

13.

D. Bertsekas, Network Optimization: Continuous and Discrete Models (Athena Scientific, Belmont, 1998)

14.

L. Bianchi, L.M. Gambardella, M. Dorigo, An ant colony optimization approach to the probabilistic traveling salesman problem, in Parallel Problem Solving from Nature – PPSN VII: 7th International Conference, J.J. Merelo Guervós, P. Adamidis, H.-G. Beyer, J.-L. Fernández-Villacanas, H.-P. Schwefel. Lecture Notes in Computer Science, vol. 2439 (Springer, Heidelberg, 2002), pp. 883–892

15.

L. Bianchi, M. Birattari, M. Manfrin, M. Mastrolilli L. Paquete, O. Rossi-Doria, T. Schiavinotto, Hybrid metaheuristics for the vehicle routing problem with stochastic demands. J. Math. Model. Algorithms 5(1), 91–110 (2006)

16.

L. Bianchi, L.M. Gambardella, M. Dorigo, W. Gutjahr, A survey on metaheuristics for stochastic combinatorial optimization. Nat. Comput. 8(2), 239–287 (2009)

17.

G. Bilchev, I.C. Parmee, The ant colony metaphor for searching continuous design spaces, in Evolutionary Computing, AISB Workshop, ed. by T.C. Fogarty. Lecture Notes in Computer Science, vol. 993 (Springer, Heidelberg, 1995), pp. 25–39

18.

M. Birattari, G. Di Caro, M. Dorigo, Toward the formal foundation of ant programming, in Ant Algorithms: Third International Workshop, ANTS 2002, ed. by M. Dorigo, G. Di Caro, M. Sampels. Lecture Notes in Computer Science, vol. 2463 (Springer, Heidelberg, 2002), pp. 188–201

19.

C. Blum, Theoretical and practical aspects of ant colony optimization, PhD thesis, IRIDIA, Université Libre de Bruxelles, Brussels, 2004

20.

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

21.

C. Blum, Beam-ACO for simple assembly line balancing. INFORMS J. Comput. 20(4), 618–627 (2008)

22.

C. Blum, M.J. Blesa, New metaheuristic approaches for the edge-weighted k-cardinality tree problem.Comput. Oper. Res. 32(6), 1355–1377 (2005)

23.

C. Blum, M. Dorigo, The hyper-cube framework for ant colony optimization. IEEE Trans. Syst. Man Cybern. B 34(2), 1161–1172 (2004)

24.

C. Blum, M. Dorigo, Search bias in ant colony optimization: on the role of competition-balanced systems. IEEE Trans. Evol. Comput. 9(2), 159–174 (2005)

25.

C. Blum, M. Sampels, Ant colony optimization for FOP shop scheduling: a case study on different pheromone representations, in Proceedings of the 2002 Congress on Evolutionary Computation (CEC’02) (IEEE Press, Piscataway, 2002), pp. 1558–1563

26.

C. Blum, M. Sampels, M. Zlochin, On a particularity in model-based search, in Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2002), ed. by W.B. Langdon et al. (Morgan Kaufmann Publishers, San Francisco, 2002), pp. 35–42

27.

C. Blum, M. Yabar, M.J. Blesa, An ant colony optimization algorithm for DNA sequencing by hybridization.Comput. Oper. Res. 35(11), 3620–3635 (2008)

28.

K.D. Boese, A.B. Kahng, S. Muddu, A new adaptive multi-start technique for combinatorial global optimization. Oper. Res. Lett. 16(2), 101–113 (1994)

29.

M. Bolondi, M. Bondanza, Parallelizzazione di un algoritmo per la risoluzione del problema del commesso viaggiatore, Master’s thesis, Dipartimento di Elettronica, Politecnico di Milano, Italy, 1993

30.

S.C. Brailsford, W.J. Gutjahr, M.S. Rauner, W. Zeppelzauer, Combined discrete-event simulation and ant colony optimisation approach for selecting optimal screening policies for diabetic retinopathy. Comput. Manag. Sci. 4(1), 59–83 (2006)

31.

B. Bullnheimer, R.F. Hartl, C. Strauss, A new rank based version of the Ant System — a computational study, Technical report, Institute of Management Science, University of Vienna, 1997

32.

B. Bullnheimer, R.F. Hartl, C. Strauss, A new rank-based version of the Ant System: a computational study. Cent. Eur. J. Oper. Res. Econ. 7(1), 25–38 (1999)

33.

B. Bullnheimer, G. Kotsis, C. Strauss, Parallelization strategies for the Ant System, in High Performance Algorithms and Software in Nonlinear Optimization, ed. by R. De Leone, A. Murli, P. Pardalos, G. Toraldo. Kluwer Series of Applied Optmization, vol. 24 (Kluwer Academic Publishers, Dordrecht, 1998), pp. 87–100

34.

E. Cantú-Paz, Efficient and Accurate Parallel Genetic Algorithms (Kluwer Academic Publishers, Boston, 2000)

35.

J.M. Cecilia, J.M. García, A. Nisbet, M. Amos, M. Ujaldón, Enhancing data parallelism for ant colony optimization on GPUs. J. Parallel Distrib. Comput. 73(1), 52–61 (2013)

36.

A. Colorni, M. Dorigo, V. Maniezzo, Distributed optimization by ant colonies, in Proceedings of the First European Conference on Artificial Life, ed. by F.J. Varela, P. Bourgine (MIT, Cambridge, 1992), pp. 134–142

37.

A. Colorni, M. Dorigo, V. Maniezzo, An investigation of some properties of an ant algorithm, in Parallel Problem Solving from Nature – PPSN II, ed. by R. Männer, B. Manderick (North-Holland, Amsterdam, 1992), pp. 509–520

38.

O. Cordón, I. Fernández de Viana, F. Herrera, L. Moreno, A new ACO model integrating evolutionary computation concepts: the best-worst Ant System, in Abstract proceedings of ANTS 2000 – From Ant Colonies to Artificial Ants: Second International Workshop on Ant Algorithms, ed. by M. Dorigo, M. Middendorf, T. Stützle (IRIDIA, Université Libre de Bruxelles, Brussels, 2000), pp. 22–29

39.

O. Cordón, I. Fernández de Viana, F. Herrera, Analysis of the best-worst Ant System and its variants on the TSP. Mathw. Soft Comput. 9(2–3), 177–192 (2002)

40.

O. Cordón, F. Herrera, T. Stützle, Special issue on ant colony optimization: models and applications. Mathw. Soft Comput. 9(2–3), 137–268 (2003)

41.

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

42.

B. Crawford, R. Soto, E. Monfroy, F. Paredes, W. Palma, A hybrid ant algorithm for the set covering problem. Int. J. Phys. Sci. 6(19), 4667–4673 (2011)

43.

L. Dawson, I.A. Stewart, Improving ant colony optimization performance on the GPU using CUDA, in Proceedings of the IEEE Congress on Evolutionary Computation, CEC 2013 (IEEE Press, Piscataway, 2013), pp. 1901–1908

44.

L.M. de Campos, J.M. Fernández-Luna, J.A. Gámez, J.M. Puerta, Ant colony optimization for learning Bayesian networks. Int. J. Approx. Reason. 31(3), 291–311 (2002)

45.

L.M. de Campos, J.A. Gamez, J.M. Puerta, Learning Bayesian networks by ant colony optimisation: searching in the space of orderings. Mathw. Soft Comput. 9(2–3), 251–268 (2002)

46.

A. Delvacq, P. Delisle, M. Gravel, M. Krajecki, Parallel ant colony optimization on graphics processing units. J. Parallel Distrib. Comput. 73(1), 52–61 (2013)

47.

M.L. den Besten, T. Stützle, M. Dorigo, Ant colony optimization for the total weighted tardiness problem, in Proceedings of PPSN-VI, Sixth International Conference on Parallel Problem Solving from Nature, ed. by M. Schoenauer, K. Deb, G. Rudolph, X. Yao, E. Lutton, J.J. Merelo, H.-P. Schwefel. Lecture Notes in Computer Science, vol. 1917 (Springer, Heidelberg, 2000), pp. 611–620

48.

J.-L. Deneubourg, S. Aron, S. Goss, J.-M. Pasteels, The self-organizing exploratory pattern of the Argentine ant. J. Insect Behav. 3(2), 159–168 (1990)

49.

G. Di Caro, Ant Colony Optimization and its application to adaptive routing in telecommunication networks, PhD thesis, IRIDIA, Université Libre de Bruxelles, Brussels, 2004

50.

G. Di Caro, M. Dorigo, AntNet: a mobile agents approach to adaptive routing, Technical Report IRIDIA/97-12, IRIDIA, Université Libre de Bruxelles, Brussels, 1997

51.

G. Di Caro, M. Dorigo, Ant colonies for adaptive routing in packet-switched communications networks, in Proceedings of PPSN-V, Fifth International Conference on Parallel Problem Solving from Nature, ed. by A. E. Eiben, T. Bäck, M. Schoenauer, H.-P. Schwefel. Lecture Notes in Computer Science, vol. 1498 (Springer, Heidelberg, 1998), pp. 673–682

52.

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

53.

G. Di Caro, M. Dorigo, Mobile agents for adaptive routing, in Proceedings of the 31st International Conference on System Sciences (HICSS-31), ed. by H. El-Rewini. (IEEE Computer Society Press, Los Alamitos, 1998), pp. 74–83

54.

G. Di Caro, F. Ducatelle, L.M. Gambardella, AntHocNet: an adaptive nature-inspired algorithm for routing in mobile ad hoc networks. Eur. Trans. Telecommun. 16(5), 443–455 (2005)

55.

D. Díaz, P. Valledor, P. Areces, J. Rodil, M. Suárez, An ACO algorithm to solve an extended cutting stock problem for scrap minimization in a bar mill, in Swarm Intelligence, 9th International Conference, ANTS 2014, ed. by M. Dorigo, M. Birattari, S. Garnier, H. Hamann, M. Montes de Oca, C. Solnon, T. Stützle. Lecture Notes in Computer Science, vol. 8667 (Springer, Heidelberg, 2014), pp. 13–24

56.

K.F. Doerner, R.F. Hartl, M. Reimann, Are CompetAnts more competent for problem solving? the case of a multiple objective transportation problem. Cent. Eur. J. Oper. Res. Econ. 11(2), 115–141 (2003)

57.

K.F. Doerner, D. Merkle, T. Stützle, Special issue on ant colony optimization. Swarm Intell. 3(1), 1–2 (2009)

58.

B. Doerr, F. Neumann, D. Sudholt, C. Witt, On the runtime analysis of the 1-ANT ACO algorithm, in Genetic and Evolutionary Computation Conference, GECCO 2007, Proceedings (ACM press, New York, 2007), pp. 33–40

59.

B. Doerr, F. Neumann, D. Sudholt, C. Witt, Runtime analysis of the 1-ant ant colony optimizer. Theor. Comput. Sci. 412(17), 1629–1644 (2011)

60.

A.V. Donati, R. Montemanni, N. Casagrande, A.E. Rizzoli, L.M. Gambardella, Time dependent vehicle routing problem with a multi ant colony system. Eur. J. Oper. Res. 185(3), 1174–1191 (2008)

61.

M. Dorigo, Optimization, Learning and Natural Algorithms (in Italian), PhD thesis, Dipartimento di Elettronica, Politecnico di Milano, Italy, 1992

62.

M. Dorigo, C. Blum, Ant colony optimization theory: a survey. Theor. Comput. Sci. 344(2–3), 243–278 (2005)

63.

M. Dorigo, G. Di Caro, The Ant Colony Optimization meta-heuristic, in New Ideas in Optimization, ed. by D. Corne, M. Dorigo, F. Glover (McGraw Hill, London, 1999), pp. 11–32

64.

M. Dorigo, L.M. Gambardella, Ant colonies for the traveling salesman problem. BioSystems 43(2), 73–81 (1997)

65.

M. Dorigo, L.M. Gambardella, Ant Colony System: a cooperative learning approach to the traveling salesman problem. IEEE Trans. Evol. Comput. 1(1), 53–66 (1997)

66.

M. Dorigo, T. Stützle, Ant Colony Optimization (MIT Press, Cambridge, 2004)

67.

M. Dorigo, V. Maniezzo, A. Colorni, The Ant System: an autocatalytic optimizing process, Technical Report 91-016 Revised, Dipartimento di Elettronica, Politecnico di Milano, Italy, 1991

68.

M. Dorigo, V. Maniezzo, A. Colorni, Positive feedback as a search strategy, Technical Report 91–016, Dipartimento di Elettronica, Politecnico di Milano, Italy, 1991

69.

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

70.

M. Dorigo, G. Di Caro, L.M. Gambardella, Ant algorithms for discrete optimization.Artif. Life 5(2), 137–172 (1999)

71.

M. Dorigo, G. Di Caro, T. Stützle (eds.), Special issue on “Ant Algorithms”. Futur. Gener. Comput. Syst. 16(8), 851–956 (2000)

72.

M. Dorigo, L.M. Gambardella, M. Middendorf, T. Stützle (eds.), Special issue on “Ant Algorithms and Swarm Intelligence”. IEEE Trans. Evol. Comput. 6(4), 317–365 (2002)

73.

J. Dréo, P. Siarry, Continuous interacting ant colony algorithm based on dense heterarchy. Futur. Gener. Comput. Syst. 20(5), 841–856 (2004)

74.

F. Ducatelle, G. Di Caro, L.M. Gambardella, 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 (2005)

75.

F. Ducatelle, G. Di Caro, L.M. Gambardella, Principles and applications of swarm intelligence for adaptive routing in telecommunications networks. Swarm Intell. 4(3), 173–198 (2010)

76.

C.J. Eyckelhof, M. Snoek, Ant systems for a dynamic TSP: ants caught in a traffic jam, in Ant Algorithms: Third International Workshop, ANTS 2002, ed. by M. Dorigo, G. Di Caro, M. Sampels. Lecture Notes in Computer Science, vol. 2463 (Springer, Heidelberg, 2002), pp. 88–99

77.

J.G. Falcón-Cardona, C.A. Coello Coello, A new indicator-based many-objective ant colony optimizer for continuous search spaces. Swarm Intell. 11(1), 71–100 (2017)

78.

M. Farooq, G. Di Caro, Routing protocols for next-generation intelligent networks inspired by collective behaviors of insect societies, in Swarm Intelligence: Introduction and Applications, ed. by C. Blum, D. Merkle. Natural Computing Series (Springer, Berlin, 2008), pp. 101–160

79.

D. Favaretto, E. Moretti, P. Pellegrini, Ant colony system for a VRP with multiple time windows and multiple visits. J. Interdiscip. Math. 10(2), 263–284 (2007)

80.

S. Fernández, S. Álvarez, D. Díaz, M. Iglesias, B. Ena, Scheduling a galvanizing line by ant colony optimization, in Swarm Intelligence, 9th International Conference, ANTS 2014, ed. by M. Dorigo, M. Birattari, S. Garnier, H. Hamann, M. Montes de Oca, C. Solnon, T. Stützle. Lecture Notes in Computer Science, vol. 8667 (Springer, Heidelberg, 2014), pp. 146–157

81.

G. Fuellerer, K.F. Doerner, R.F. Hartl, M. Iori, Ant colony optimization for the two-dimensional loading vehicle routing problem. Comput. Oper. Res. 36(3), 655–673 (2009)

82.

L.M. Gambardella, M. Dorigo, Ant-Q: a reinforcement learning approach to the traveling salesman problem. in Proceedings of the Twelfth International Conference on Machine Learning (ML-95), ed. by A. Prieditis, S. Russell (Morgan Kaufmann Publishers, Palo Alto, 1995), pp. 252–260

83.

L.M. Gambardella, M. Dorigo, Solving symmetric and asymmetric TSPs by ant colonies, in Proceedings of the 1996 IEEE International Conference on Evolutionary Computation (ICEC’96) (IEEE Press, Piscataway, 1996), pp. 622–627

84.

L.M. Gambardella, M. Dorigo, Ant Colony System hybridized with a new local search for the sequential ordering problem. INFORMS J. Comput. 12(3), 237–255 (2000)

85.

L.M. Gambardella, É.D. Taillard, G. Agazzi, MACS-VRPTW: a multiple ant colony system for vehicle routing problems with time windows, in New Ideas in Optimization, ed. by D. Corne, M. Dorigo, F. Glover (McGraw Hill, London, 1999), pp. 63–76

86.

L.M. Gambardella, R. Montemanni, D. Weyland, Coupling ant colony systems with strong local searches. Eur. J. Oper. Res. 220(3), 831–843 (2012)

87.

C. García-Martínez, O. Cordón, F. Herrera, A taxonomy and an empirical analysis of multiple objective ant colony optimization algorithms for the bi-criteria TSP. Eur. J. Oper. Res. 180(1), 116–148 (2007)

88.

M.R. Garey, D.S. Johnson, Computers and Intractability: A Guide to the Theory of \(\mathcal{N}\mathcal{P}\)-Completeness (Freeman, San Francisco, 1979)

89.

S. Goss, S. Aron, J.L. Deneubourg, J.M. Pasteels, Self-organized shortcuts in the Argentine ant. Naturwissenschaften 76(12), 579–581 (1989)

90.

G.D. Guerrero, J.M. Cecilia, A. Llanes, J.M. García, M. Amos, M. Ujaldón, Comparative evaluation of platforms for parallel ant colony optimization. J. Supercomput. 69(1), 318–329 (2014)

91.

M. Guntsch, M. Middendorf, Pheromone modification strategies for ant algorithms applied to dynamic TSP, in Applications of Evolutionary Computing: Proceedings of EvoWorkshops 2001, ed. by E.J.W. Boers, J. Gottlieb, P.L. Lanzi, R.E. Smith, S. Cagnoni, E. Hart, G.R. Raidl, H. Tijink. Lecture Notes in Computer Science, vol. 2037 (Springer, Heidelberg, 2001), pp. 213–222

92.

M. Guntsch, M. Middendorf, A population based approach for ACO, in Applications of Evolutionary Computing, Proceedings of EvoWorkshops2002: EvoCOP, EvoIASP, EvoSTim, ed. by S. Cagnoni, J. Gottlieb, E. Hart, M. Middendorf, G.R. Raidl. Lecture Notes in Computer Science, vol. 2279 (Springer, Heidelberg, 2002), pp. 71–80

93.

W.J. Gutjahr, A Graph-based Ant System and its convergence. Futur. Gener. Comput. Syst. 16(8), 873–888 (2000)

94.

W.J. Gutjahr, ACO algorithms with guaranteed convergence to the optimal solution. Inf. Process. Lett. 82(3), 145–153 (2002)

95.

W.J. Gutjahr, S-ACO: an ant-based approach to combinatorial optimization under uncertainty, in Ant Colony Optimization and Swarm Intelligence: 4th International Workshop, ANTS 2004, ed. by M. Dorigo, L. Gambardella, F. Mondada, T. Stützle, M. Birratari, C. Blum. Lecture Notes in Computer Science, vol. 3172 (Springer, Heidelberg, 2004), pp. 238–249

96.

W.J. Gutjahr, On the finite-time dynamics of ant colony optimization. Methodol. Comput. Appl. Probab. 8(1), 105–133 (2006)

97.

W.J. Gutjahr, Mathematical runtime analysis of ACO algorithms: survey on an emerging issue. Swarm Intell. 1(1), 59–79 (2007)

98.

W.J. Gutjahr, First steps to the runtime complexity analysis of ant colony optimization. Comput. Oper. Res. 35(9), 2711–2727 (2008)

99.

W.J. Gutjahr, G. Sebastiani, Runtime analysis of ant colony optimization with best-so-far reinforcement. Methodol. Comput. Appl. Probab. 10(3), 409–433 (2008)

100.

R. Hadji, M. Rahoual, E. Talbi, V. Bachelet, Ant colonies for the set covering problem, in Abstract proceedings of ANTS 2000 – From Ant Colonies to Artificial Ants: Second International Workshop on Ant Algorithms, ed. by M. Dorigo, M. Middendorf, T. Stützle (Université Libre de Bruxelles, Brussels, 2000), pp. 63–66

101.

H. Hernández, C. Blum, Ant colony optimization for multicasting in static wireless ad-hoc networks. Swarm Intell. 3(2), 125–148 (2009)

102.

S. Iredi, D. Merkle, M. Middendorf, Bi-criterion optimization with multi colony ant algorithms, in First International Conference on Evolutionary Multi-Criterion Optimization, (EMO’01), ed. by E. Zitzler, K. Deb, L. Thiele, C.A. Coello Coello, and D. Corne. Lecture Notes in Computer Science, vol. 1993 (Springer, Heidelberg, 2001), pp. 359–372

103.

D.S. Johnson, L.A. McGeoch, The travelling salesman problem: a case study in local optimization, in Local Search in Combinatorial Optimization, ed. by E.H.L. Aarts, J.K. Lenstra (Wiley, Chichester, 1997), pp. 215–310

104.

M. Jünger, G. Reinelt, S. Thienel, Provably good solutions for the traveling salesman problem. Z. Oper. Res. 40(2), 183–217 (1994)

105.

M. Khichane, P. Albert, C. Solnon, Integration of ACO in a constraint programming language, in Ant Colony Optimization and Swarm Intelligence, 6th International Conference, ANTS 2008, ed. by M. Dorigo, M. Birattari, C. Blum, M. Clerc, T. Stützle, A.F.T. Winfield. Lecture Notes in Computer Science, vol. 5217 (Springer, Heidelberg, 2008), pp. 84–95

106.

O. Korb, T. Stützle, T.E. Exner, Application of ant colony optimization to structure-based drug design, in Ant Colony Optimization and Swarm Intelligence, 5th International Workshop, ANTS 2006, ed. by M. Dorigo, M. Birattari, C. Blum, M. Clerc, T. Stützle, A.F.T. Winfield. Lecture Notes in Computer Science, vol. 4150 (Springer, Heidelberg, 2006), pp. 247–258

107.

O. Korb, T. Stützle, T.E. Exner, An ant colony optimization approach to flexible protein-ligand docking. Swarm Intell. 1(2), 115–134 (2007)

108.

T. Kötzing, F. Neumann, H. Röglin, C. Witt, Theoretical analysis of two ACO approaches for the traveling salesman problem. Swarm Intell. 6(1), 1–21 (2012)

109.

U. Kumar, Jayadeva, S. Soman, Enhancing IACOR local search by Mtsls1-BFGS for continuous global optimization, in Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2015, ed. by S. Silva, A.I. Esparcia-Alcázar (ACM Press, New York, 2015), pp. 33–40

110.

E.L. Lawler, J.K. Lenstra, A.H.G. Rinnooy Kan, D.B. Shmoys, The Travelling Salesman Problem (Wiley, Chichester, 1985), pp. 33–40

111.

G. Leguizamón, Z. Michalewicz, A new version of Ant System for subset problems, in Proceedings of the 1999 Congress on Evolutionary Computation (CEC’99) (IEEE Press, Piscataway, 1999), pp. 1459–1464

112.

L. Lessing, I. Dumitrescu, T. Stützle, A comparison between ACO algorithms for the set covering problem, in Ant Colony Optimization and Swarm Intelligence: 4th International Workshop, ANTS 2004, ed. by M. Dorigo, L. Gambardella, F. Mondada, T. Stützle, M. Birratari, C. Blum. Lecture Notes in Computer Science, vol. 3172 (Springer, Heidelberg, 2004), pp. 1–12

113.

T. Liao, M. Montes de Oca, D. Aydin, T. Stützle, M. Dorigo, An incremental ant colony algorithm with local search for continuous optimization, in Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2011, ed. by N. Krasnogor, P.L. Lanzi (ACM Press, New York, 2011), pp. 125–132

114.

T. Liao, M. Montes de Oca, T. Stützle, M. Dorigo, A unified ant colony optimization algorithm for continuous optimization. Eur. J. Oper. Res. 234(3), 597–609 (2014)

115.

T. Liao, K. Socha, M. Montes de Oca, T. Stützle, M. Dorigo, Ant colony optimization for mixed-variable optimization problems. IEEE Trans. Evol. Comput. 18(4), 503–518 (2014)

116.

A. Lissovoi, C. Witt, Runtime analysis of ant colony optimization on dynamic shortest path problems. Theor. Comput. Sci. 561, 73–85 (2015)

117.

M. López-Ibáñez, C. Blum, Beam-ACO for the travelling salesman problem with time windows. Comput. Oper. Res. 37(9), 1570–1583 (2010)

118.

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

119.

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

120.

M. López-Ibáñez, L. Paquete, T. Stützle, On the design of ACO for the biobjective quadratic assignment problem, in ANTS’2004, Fourth International Workshop on Ant Algorithms and Swarm Intelligence, ed. by M. Dorigo, L. Gambardella, F. Mondada, T. Stützle, M. Birratari, C. Blum. Lecture Notes in Computer Science, vol. 3172 (Springer, Heidelberg, 2004), pp. 214–225

121.

M. López-Ibáñez, C. Blum, D. Thiruvady, A.T. Ernst, B. Meyer, Beam-ACO based on stochastic sampling for makespan optimization concerning the TSP with time windows, in Evolutionary Computation in Combinatorial Optimization, ed. by C. Cotta, P. Cowling. Lecture Notes in Computer Science, vol. 5482 (Springer, Heidelberg, 2009), pp. 97–108

122.

M. López-Ibáñez, J. Dubois-Lacoste, L. Perez Cáceres, T. Stützle, M. Birattari, The irace package: iterated racing for automatic algorithm configuration. Oper. Res. Perspect. 3, 43–58 (2016)

123.

M. Manfrin, M. Birattari, T. Stützle, M. Dorigo, Parallel ant colony optimization for the traveling salesman problem, in ed. by Ant Colony Optimization and Swarm Intelligence: 5th International Workshop, ANTS 2006, ed. by M. Dorigo, L.M. Gambardella, M. Birattari, A. Martinoli, R. Poli, T. Stützle. Lecture Notes in Computer Science, vol. 4150 (Springer, Heidelberg, 2006), pp. 224–234

124.

V. Maniezzo, Exact and approximate nondeterministic tree-search procedures for the quadratic assignment problem, Technical Report CSR 98-1, Scienze dell’Informazione, Universitá di Bologna, Sede di Cesena, Italy, 1998

125.

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

126.

V. Maniezzo, A. Carbonaro, An ANTS heuristic for the frequency assignment problem. Futur. Gener. Comput. Syst. 16(8), 927–935 (2000)

127.

D. Martens, M. De Backer, R. Haesen, J. Vanthienen, M. Snoeck, B. Baesens, Classification with ant colony optimization. IEEE Trans. Evol. Comput. 11(5), 651–665 (2007)

128.

F. Massen, Y. Deville, P. van Hentenryck, Pheromone-based heuristic column generation for vehicle routing problems with black box feasibility, in Integration of AI and OR Techniques in Contraint Programming for Combinatorial Optimization Problems, CPAIOR 2012, ed. by N. Beldiceanu, N. Jussien, E. Pinson. Lecture Notes in Computer Science, vol. 7298 (Springer, Berlin, 2012), pp. 260–274

129.

F. Massen, M. López-Ibá nez, T. Stützle, Y. Deville, Experimental analysis of pheromone-based heuristic column generation using irace, in Hybrid Metaheuristics, ed. by M. J. Blesa, C. Blum, P. Festa, A. Roli, M. Sampels. Lecture Notes in Computer Science, vol. 7919 (Springer, Berlin, 2013), pp. 92–106

130.

M. Mavrovouniotis, S. Yang, Ant colony optimization with immigrants schemes for the dynamic travelling salesman problem with traffic factors. Appl. Soft Comput. 13(10), 4023–4037 (2013)

131.

M. Mavrovouniotis, S. Yang, Ant algorithms with immigrants schemes for the dynamic vehicle routing problem. Inf. Sci. 294, 456–477 (2015)

132.

M. Mavrovouniotis, C. Li, S. Yang, A survey of swarm intelligence for dynamic optimization: algorithms and applications. Swarm Evol. Comput. 33, 1–17 (2017)

133.

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

134.

D. Merkle, M. Middendorf, Modeling the dynamics of ant colony optimization. Evol. Comput. 10(3), 235–262 (2002)

135.

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

136.

D. Merkle, M. Middendorf, H. Schmeck, Ant colony optimization for resource-constrained project scheduling, in Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2000), ed. by D. Whitley, D. Goldberg, E. Cantu-Paz, L. Spector, I. Parmee, H.-G. Beyer (Morgan Kaufmann Publishers, San Francisco, 2000), pp. 893–900

137.

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

138.

N. Meuleau, M. Dorigo, Ant colony optimization and stochastic gradient descent. Artif. Life 8(2), 103–121 (2002)

139.

B. Meyer, A. Ernst, Integrating ACO and constraint propagation, in Ant Colony Optimization and Swarm Intelligence, 4th International Workshop, ANTS 2004, M. Dorigo, M. Birattari, C. Blum, L.M. Gambardella, F. Mondada, T. Stützle. Lecture Notes in Computer Science, vol. 3172 (Springer, Heidelberg, 2004), pp. 166–177

140.

R. Michel, M. Middendorf, An ACO algorithm for the shortest supersequence problem, in New Ideas in Optimization, ed. by D. Corne, M. Dorigo, F. Glover (McGraw Hill, London, 1999), pp. 51–61

141.

M. Middendorf, F. Reischle, H. Schmeck, Multi colony ant algorithms. J. Heuristics 8(3), 305–320 (2002)

142.

N. Monmarché, G. Venturini, M. Slimane, On how Pachycondyla apicalis ants suggest a new search algorithm. Futur. Gener. Comput. Syst. 16(8), 937–946 (2000)

143.

R. Montemanni, L.M. Gambardella, A.E. Rizzoli, A.V. Donati, Ant colony system for a dynamic vehicle routing problem. J. Comb. Optim. 10(4), 327–343 (2005)

144.

T.E. Morton, R.M. Rachamadugu, A. Vepsalainen, Accurate myopic heuristics for tardiness scheduling, GSIA Working Paper 36-83-84, Carnegie Mellon University, Pittsburgh, PA, 1984

145.

F. Neumann, D. Sudholt, C. Witt, Analysis of different MMAS ACO algorithms on unimodal functions and plateaus. Swarm Intell. 3(1), 35–68 (2009)

146.

F. Neumann, C. Witt, Algorithmica 54, 243 (2009). https://doi.org/10.1007/s00453-007-9134-2

147.

F. Neumann, C. Witt, Ant colony optimization and the minimum spanning tree problem. Theor. Comput. Sci. 411(25), 2406–2413 (2010)

148.

F.E.B. Otero, A.A. Freitas, C.G. Johnson, cAnt-Miner: an ant colony classification algorithm to cope with continuous attributes, in Ant Colony Optimization and Swarm Intelligence, 6th International Workshop, ANTS 2008, ed. by M. Dorigo, M. Birattari, C. Blum, M. Clerc, T. Stützle, A.F.T. Winfield. Lecture Notes in Computer Science, vol. 5217 (Springer, Heidelberg, 2008), pp. 48–59

149.

P.S. Ow, T.E. Morton, Filtered beam search in scheduling. Int. J. Prod. Res. 26(1), 297–307 (1988)

150.

C.H. Papadimitriou, Computational Complexity (Addison-Wesley, Reading, 1994)

151.

R.S. Parpinelli, H.S. Lopes, A.A. Freitas, Data mining with an ant colony optimization algorithm. IEEE Trans. Evol. Comput. 6(4), 321–332 (2002)

152.

C. Rajendran, H. Ziegler, Ant-colony algorithms for permutation flowshop scheduling to minimize makespan/total flowtime of jobs. Eur. J. Oper. Res. 155(2), 426–438 (2004)

153.

M. Randall, A. Lewis, A parallel implementation of ant colony optimization. J. Parallel Distrib. Comput. 62(9), 1421–1432 (2002)

154.

M. Reimann, K. Doerner, R.F. Hartl, D-ants: savings based ants divide and conquer the vehicle routing problems. Comput. Oper. Res. 31(4), 563–591 (2004)

155.

G. Reinelt, The Traveling Salesman: Computational Solutions for TSP Applications. Lecture Notes in Computer Science, vol. 840 (Springer, Heidelberg, 1994)

156.

Z.-G. Ren, Z.-R. Feng, L.-J. Ke, Z.-J. Zhang, New ideas for applying ant colony optimization to the set covering problem. Comput. Ind. Eng. 58(4), 774–784 (2010)

157.

A.E. Rizzoli, R. Montemanni, E. Lucibello, L.M. Gambardella, Ant colony optimization for real-world vehicle routing problems. From theory to applications. Swarm Intell. 1(2), 135–151 (2007)

158.

R. Ruiz, T. Stützle, A simple and effective iterated greedy algorithm for the permutation flowshop scheduling problem. Eur. J. Oper. Res. 177(3), 2033–2049 (2007)

159.

R. Schoonderwoerd, O. Holland, J. Bruten, L. Rothkrantz, Ant-based load balancing in telecommunications networks. Adapt. Behav. 5(2), 169–207 (1996)

160.

A. Shmygelska, H.H. Hoos, An ant colony optimisation algorithm for the 2D and 3D hydrophobic polar protein folding problem. BMC Bioinf. 6, 30 (2005)

161.

K.M. Sim, W.H. Sun, Ant colony optimization for routing and load-balancing: Survey and new directions. IEEE Trans. Syst. Man Cybern. Syst. Hum. 33(5), 560–572 (2003)

162.

K. Socha, ACO for continuous and mixed-variable optimization, in Ant Colony Optimization and Swarm Intelligence: 4th International Workshop, ANTS 2004, ed. by M. Dorigo, L. Gambardella, F. Mondada, T. Stützle, M. Birratari, C. Blum. Lecture Notes in Computer Science, vol. 3172 (Springer, Heidelberg, 2004), pp. 25–36

163.

K. Socha, C. Blum, An ant colony optimization algorithm for continuous optimization: an application to feed-forward neural network training. Neural Comput. Appl. 16(3), 235–248 (2007)

164.

K. Socha, M. Dorigo, Ant colony optimization for mixed-variable optimization problems, Technical Report TR/IRIDIA/2007-019, IRIDIA, Université Libre de Bruxelles, Brussels, 2007

165.

K. Socha, M. Dorigo, Ant colony optimization for continuous domains. Eur. J. Oper. Res. 185(3), 1155–1173 (2008)

166.

K. Socha, J. Knowles, M. Sampels, A \(\mathcal{M}\mathcal{A}\mathcal{X}-\mathcal{M}\mathcal{I}\mathcal{N}\) Ant System for the university course timetabling problem, in Ant Algorithms: Third International Workshop, ANTS 2002, ed. by M. Dorigo, G. Di Caro, M. Sampels. Lecture Notes in Computer Science, vol. 2463 (Springer, Heidelberg, 2002), pp. 1–13

167.

K. Socha, M. Sampels, M. Manfrin, Ant algorithms for the university course timetabling problem with regard to the state-of-the-art, in Applications of Evolutionary Computing, Proceedings of EvoWorkshops 2003, ed. by G.R. Raidl, J.-A. Meyer, M. Middendorf, S. Cagnoni, J.J.R. Cardalda, D.W. Corne, J. Gottlieb, A. Guillot, E. Hart, C.G. Johnson, E. Marchiori. Lecture Notes in Computer Science, vol. 2611 (Springer, Heidelberg, 2003), pp. 334–345

168.

C. Solnon, Combining two pheromone structures for solving the car sequencing problem with ant colony optimization. Eur. J. Oper. Res. 191(3), 1043–1055 (2008)

169.

C. Solnon, S. Fenet, A study of ACO capabilities for solving the maximum clique problem. J. Heuristics 12(3), 155–180 (2006)

170.

T. Stützle, An ant approach to the flow shop problem, in Proceedings of the Sixth European Congress on Intelligent Techniques & Soft Computing (EUFIT’98), vol. 3 (Verlag Mainz, Wissenschaftsverlag, Aachen, 1998), pp. 1560–1564

171.

T. Stützle, Parallelization strategies for ant colony optimization, in Proceedings of PPSN-V, Fifth International Conference on Parallel Problem Solving from Nature, ed. by A.E. Eiben, T. Bäck, M. Schoenauer, H.-P. Schwefel. Lecture Notes in Computer Science, vol. 1498 (Springer, Heidelberg, 1998), pp. 722–731

172.

T. Stützle, Local Search Algorithms for Combinatorial Problems: Analysis, Improvements, and New Applications. Dissertationen zur künstlichen Intelligenz, vol. 220 (Infix, Sankt Augustin, 1999)

173.

T. Stützle, M. Dorigo, A short convergence proof for a class of ACO algorithms. IEEE Trans. Evol. Comput. 6(4), 358–365 (2002)

174.

T. Stützle, H.H. Hoos, Improving the Ant System: A detailed report on the \(\mathcal{M}\mathcal{A}\mathcal{X}\)–\(\mathcal{M}\mathcal{I}\mathcal{N}\) Ant System, Technical Report AIDA–96–12, FG Intellektik, FB Informatik, TU Darmstadt, 1996

175.

T. Stützle, H.H. Hoos, The \(\mathcal{M}\mathcal{A}\mathcal{X}\)–\(\mathcal{M}\mathcal{I}\mathcal{N}\) Ant System and local search for the traveling salesman problem, in Proceedings of the 1997 IEEE International Conference on Evolutionary Computation (ICEC’97), ed. by T. Bäck, Z. Michalewicz, X. Yao (IEEE Press, Piscataway, 1997), pp. 309–314

176.

T. Stützle, H.H. Hoos, \(\mathcal{M}\mathcal{A}\mathcal{X}\)–\(\mathcal{M}\mathcal{I}\mathcal{N}\) Ant System. Futur. Gener. Comput. Syst. 16(8), 889–914 (2000)

177.

D. Sudholt, Theory of swarm intelligence: tutorial at GECCO 2017, in Genetic and Evolutionary Computation Conference, Berlin, July 15–19, 2017, Companion Material Proceedings, ed. by P.A.N. Bosman (ACM Press, New York, 2017), pp. 902–921

178.

D. Sudholt, C. Thyssen, Running time analysis of ant colony optimization for shortest path problems. J. Discret. Algorithms 10, 165–180 (2012)

179.

R.S. Sutton, A.G. Barto, Reinforcement Learning: An Introduction (MIT Press, Cambridge, 1998)

180.

E.-G. Talbi, O.H. Roux, C. Fonlupt, D. Robillard, Parallel ant colonies for the quadratic assignment problem. Futur. Gener. Comput. Syst. 17(4), 441–449 (2001)

181.

S. Tsutsui, Ant colony optimisation for continuous domains with aggregation pheromones metaphor, in Proceedings of the 5th International Conference on Recent Advances in Soft Computing (RASC-04), Nottingham (2004), pp. 207–212

182.

S. Tsutsui, cAS: ant colony optimization with cunning ants, in Parallel Problem Solving from Nature–PPSN IX, 9th International Conference, ed. by T.P. Runarsson, H.-G. Beyer, E.K. Burke, J.J. Merelo Guervós, L.D. Whitley, X. Yao. Lecture Notes in Computer Science, vol. 4193 (Springer, Heidelberg, 2006), pp. 162–171

183.

S. Tsutsui, An enhanced aggregation pheromone system for real-parameter optimization in the ACO metaphor, in Ant Colony Optimization and Swarm Intelligence: 5th International Workshop, ANTS 2006, ed. by M. Dorigo, L. M. Gambardella, M. Birattari, A. Martinoli, R. Poli, T. Stützle. Lecture Notes in Computer Science, vol. 4150 (Springer, Berlin, 2006), pp. 60–71

184.

C. Twomey, T. Stützle, M. Dorigo, M. Manfrin, M. Birattari, An analysis of communication policies for homogeneous multi-colony ACO algorithms. Inf. Sci. 180(12), 2390–2404 (2010)

185.

W. Wiesemann, T. Stützle, Iterated ants: an experimental study for the quadratic assignment problem. in Ant Colony Optimization and Swarm Intelligence: 5th International Workshop, ANTS 2006, ed. by M. Dorigo, L.M. Gambardella, M. Birattari, A. Martinoli, R. Poli, T. Stützle. Lecture Notes in Computer Science, vol. 4150 (Springer, Heidelberg, 2006), pp. 179–190

186.

M. Yagiura, M. Kishida, T. Ibaraki, A 3-flip neighborhood local search for the set covering problem. Eur. J. Oper. Res. 172(2), 472–499 (2006)

187.

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

188.

M. Yannakakis, Computational complexity, in Local Search in Combinatorial Optimization, ed. by E.H.L. Aarts, J.K. Lenstra (Wiley, Chichester, 1997), pp. 19–55

189.

Z. Yuan, A. Fügenschuh, H. Homfeld, P. Balaprakash, T. Stützle, M. Schoch, Iterated greedy algorithms for a real-world cyclic train scheduling problem, in Hybrid Metaheuristics, 5th International Workshop, HM 2008, ed. by M.J. Blesa, C. Blum, C. Cotta, A.J. Fernández, J.E. Gallardo, A. Roli, M. Sampels. Lecture Notes in Computer Science, vol. 5296 (Springer, Heidelberg, 2008), pp. 102–116

190.

Y. Zhang, L.D. Kuhn, M.P.J. Fromherz, Improvements on ant routing for sensor networks, in Ant Colony Optimization and Swarm Intelligence: 4th International Workshop, ANTS 2004, ed. by M. Dorigo, L.M. Gambardella, F. Mondada, T. Stützle, M. Birattari, C. Blum. Lecture Notes in Computer Science, vol. 3172 (Springer, Heidelberg, 2004), pp. 154–165

191.

M. Zlochin, M. Birattari, N. Meuleau, M. Dorigo, Model-based search for combinatorial optimization: a critical survey. Ann. Oper. Res. 131(1–4), 373–395 (2004)

Titel: Ant Colony Optimization: Overview and Recent Advances
verfasst von: Marco Dorigo
Thomas Stützle
Verlag: Springer International Publishing
Buch: Handbook of Metaheuristics
Print ISBN: 978-3-319-91085-7

Electronic ISBN: 978-3-319-91086-4

Copyright-Jahr: 2019
DOI: https://doi.org/10.1007/978-3-319-91086-4_10

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