This paper introduces a new high-level domain-specific modelling language, LAnt, for the design of ant colony optimization algorithms. This language is used to represent the main elements required to define the structure of the algorithms and to capture the specific constraints associated to the problem to solve. It aims to support users with low experience in the development of solutions based on this paradigm. The proposal has been implemented as an Eclipse plug-in, including an editor and an integrated code generator.
BeckersR., DeneubourgJ. and GossS., Trails and u-turns in the selection of a path by the ant lasius niger, Journal of Theoretical Biology159(4) (1992), 397–415.
2.
Berners-LeeT. and ConnollyD., Hypertext markup language-2.0, 1995.
3.
BlumC., Theoretical and practical aspects of ant colony optimization, IOS press, 2004.
4.
BlumC., Beam-aco hybridizing ant colony optimization with beam search: An application to open shop scheduling, Computers & Operations Research32(6) (2005), 1565–1591.
5.
BonabeauE., DorigoM., TheraulazG., Swarm intelligence: From natural to artificial systems, Oxford University Press, 1999.
6.
CambroneroM.E. and ValeroV., Modelling distributed service systems with resources using uml, In 13th Int Conf On Computational Science, ICCS’13, Procedia Computer Science, Elsevier, 2013, pp. 140–148.
7.
ChenB. and ChenL., A link prediction algorithm based on ant colony optimization, Applied Intelligence41(3) (2014), 694–708.
8.
ChenB., ChenL. and ChenY., Efficient ant colony optimization for image feature selection, Signal processing93(6) (2013), 1566–1576.
9.
CostaD. and HertzA., Ants can colour graphs, Journal of the Operational Research Society48(3) (1997), 295–305.
10.
da SilvaA.R., Model-driven engineering: A survey supported by the unified conceptual model, Computer Languages, Systems & Structures43 (2015), 139–155.
11.
De-CamposL.M., Fernandez-LunaJ.M., GámezJ.A. and PuertaJ.M., Ant colony optimization for learning bayesian networks, Int Journal of Approximate Reasoning31(3) (2002), 291–311.
12.
DennisA., WixomB.H. and TegardenD., Systems analysis and design: An object-oriented approach with UML. John Wiley & Sons, 2015.
13.
DorigoM. and GambardellaL.M., Ant colonies for the traveling salesman problem, BioSystems43(2) (1997), 73–81.
14.
DorigoM. and GambardellaL.M., Ant colony system: A cooperative learning approach to the traveling salesman problem, IEEE Transactions on Evolutionary Computation1(1) (1997), 53–66.
15.
DorigoM., ManiezzoV. and ColorniA., Ant system: Optimization by a colony of cooperating agents, Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics26(1) (1996), 29–41.
16.
DorigoM. and StützleT., Handbook of Metaheuristics, volume 146 of Int Series in Operations Research & Management Science, chapter Ant Colony Optimization: Overview and Recent Advances, Springer, 2010, pp. 227–263.
EysholdtM. and BehrensH., Xtext: Implement your language faster than the quick and dirty way. In Int Conf Companion on Object Oriented Programming Systems Languages and Applications Companion, ACM Press, 2010, pp. 307–309.
19.
GuntschM. and MiddendorfM., A population based approach for aco. In Applications of Evolutionary Computing, Springer, 2002, pp. 72–81.
20.
HatamlouA., Black hole: A new heuristic optimization approach for data clustering, Information sciences222 (2013), 175–184.
21.
HermansF., PinzgerM. and Van-DeursenA., Domain-specific languages in practice: A user study on the success factors, Springer, 2009.
22.
HiltzS.R., TuroffM., The network nation: Human communication via computer, Mit Press, 1993.
23.
KarabogaD. and BasturkB., A powerful and efficient algorithm for numerical function optimization: Artificial bee colony (abc) algorithm, Journal of Global Optimization39(3) (2007), 459–471.
24.
KennedyJ., Particle swarm optimization. In Encyclopedia of Machine Learning, Springer, 2011, pp. 760–766.
25.
KentS., Model driven engineering, In 3rd Int Conf on Integrated Formal Methods, IFM’02, LNCS 2335, Springer, 2002, pp. 286–298.
26.
LamportL., LATEX: A Document Preparation System. Addison-Wesley, 2nd edition, 1994.
27.
Inc MathWorks. MATLAB: The language of technical computing. Desktop tools and development environment, volume 9. MathWorks, 7nd edition, 2005.
28.
MirjaliliS., MirjaliliS.M. and LewisA., Grey wolf optimizer, Advances in Engineering Software69 (2014), 46–61.
29.
NetoR.F.T. and Godinho-FilhoM., Literature review regarding ant colony optimization applied to scheduling problems: Guidelines for implementation and directions for future research, Engineering Applications of Artificial Intelligence26(1) (2013), 150–161.
30.
ParpinelliR.S., LopesH.S. and FreitasA.A., Data mining with an ant colony optimization algorithm, IEEE Transactions on Evolutionary Computation6(4) (2002), 321–332.
31.
RiadiI. and ChandraJ., Cognitive Ant colony optimization: A new framework in swarm intelligence, PhD thesis, University of Salford, 2014.
32.
RumbaughJ., JacobsonI., BoochG., The Unified Modeling Language Reference Manual, Addison-Wesley, 2004.
33.
SeidewitzE., What models mean, IEEE Software20(5) (2003), 26–32.
34.
ShmygelskaA., Aguirre-HernandezR. and HoosH., An ant colony optimization algorithm for the 2d hp protein folding problem. In Ant Algorithms, Springer, 2002, pp. 40–52.
35.
SolnonC., Ants can solve constraint satisfaction problems, IEEE Transactions on Evolutionary Computation6(4) (2002), 347–357.
StephanM. and AntkiewiczM., Ecore fmp: A tool for editing and instantiating class models as feature models, Technical report, University of Waterloo, 2008.
38.
StützleT. and HoosH.H., Max–min ant system, Future Generation Computer Systems16(8) (2000), 889–914.
39.
Van-DeursenA., KlintP. and VisserJ., Domain-specific languages: An annotated bibliography, SIGPLAN Notices35(6) (2000), 26–36.
40.
HungW. and HongW., Application of svr with improved ant colony optimization algorithms in exchange rate forecasting, Control and Cybernetics38(3) (2009), 863–891.
