American Journal of Software Engineering and Applications
Volume 3, Issue 2, April 2014, Pages: 12-20
Received: May 11, 2014;
Accepted: Jun. 11, 2014;
Published: Jun. 30, 2014
Views 3425 Downloads 180
Elnaz Shafigh Fard, Faculty of Computer Engineering, Najafabad branch, Islamic Azad University, Isfahan, Iran
Khalil Monfaredi, Engineering Faculty, Department of Electrical and Electronic Engineering, Azarbaijan Shahid Madani University, Tabriz, Iran
Mohammad H. Nadimi, Faculty of Computer Engineering, Najafabad branch, Islamic Azad University, Isfahan, Iran
As one of the most prestigious and beneficial methods of artificial intelligence, ant colony takes the advantage of communal behavior of ants in nature for solving optimization problems in various fields. However, this useful algorithm requires extensive and repetitious computation, as a result, the processing duration of the present algorithm seems to be one of the most serious challenges about it. In order to solve optimization problems in which duration is very important, this paper attempts to review the previously applied methods and consider the advantages and the disadvantages of each method through highlighting the problems algorithm designers encounter.
Elnaz Shafigh Fard,
Mohammad H. Nadimi,
Application Methods of Ant Colony Algorithm, American Journal of Software Engineering and Applications.
Vol. 3, No. 2,
2014, pp. 12-20.
B. Scheuermann, S. Janson, M. Middendorf, Hardware-oriented ant colony optimization, Journal of Systems Archi-tecture 53 (7) (2007) 386–402.
C. Blum, A. Roli, Metaheuristics in combinatorial optimization: overview and conceptual comparison, ACM Computing Surveys 35 (3) (2003) 268–308
Conference on Parallel Problem Solving from Nature, Lecture Notes in Computer Science 1498 (1998) 692–701
D. Merkle, M. Middendorf, Fast ant colony optimization on runtime reconfigurable processor arrays, Genetic Programming and Evolvable Ma-chines 3 (4) (2002) 345–361
E. Talbi, O. Roux, C. Fonlupt, D. Robillard, Parallel ant colonies for the qua-dratic assignement problem, Future Generation Computer Systems 17 (4) (2001) 441–449
F. Glover, G. Kochenberger (Eds.), Handbook of Metaheuristics, International Series in Operations Research & Management Science, 57, Springer, 2003.
H. Bai, D. OuYang, X. Li, L. He, H. Yu, Max-min ant system on gpu with cuda, in:Proceedings of the 2009 Fourth International Conference on Innovative Computing,Information and Control, IEEE Computer Society, 2009, pp. 801–804 .
H.Duan ,Yaxiang.Yu , JieZou and Xing Feng .Ant colony optimiza-tion-based bio-inspired hardware.
K.Gheysari,A.Khoei,B.Mashoufi High speed ant colony optimization CMOS Chip .
M. Bolondi, M. Bondaza, Parallelizzazione di unalgoritmo per la risoluzione del problema del com-messoviaggiatore, Master’s thesis, Politecnico di Milano, Italy, 1993
M. Dorigo, G. Di Caro, L. Gambardella, Ant algorithms for discrete optimization, Artificial Life 5 (2) (1999) 137–172
M. Dorigo, Parallel ant system: an experimental study, unpublished manuscript, Cited by , 1993
M. Pedemonte, H. Cancela, A cellular ant colony optimisation for the generalized Steiner problem, International Journal of Innovative Computing and Applications 2 (3) (2010) 188–201.
M. Pedemonte, S. Nesmachnow, H.Cancela Survey on parallel ant colony optimization .Applied Soft computing hournal.(2011.)
P. Delisle, M. Gravel, M. Krajecki, C. Gagné, W. Price, Comparing parallelization of an ACO: message passing vs. shared memory, in: Proceedings of the 2nd International Workshop on Hybrid Metaheuristics, Lecture Notes in Computer Science vol. 3636 (2005) 1–11
R. Michel, M. Middendorf, An island model based ant system with lookahead for the shortest supersequence problem, in: Proceedings of the 5th International.
R.Vaidyanathan and J.L.Trahan :Dynamic Reconfiguration :Architectures and Algorithms .Kluwer,(2004 )
S.Li,M.Hao Yang ,Chung-Wei WENG,Yi –Hong Chen Ant Colony Optimization design and its FPGA implementation
Yoshikawa ,M and Terai,H 2007 : Architecture for high –speed ant colony optimization .oroceedings of IEEE International Conference on information Reuse and integration ,lasVegas,NV, 1-5 .
Yoshikawa,M and Terai,H 2008:Hardware-oriented ant colony optimization considering intensification and diversification in:Bednorz, W.editor. Advances in greedy algorithms,I-Tech,359-68.