Information Technology Reference
In-Depth Information
10. Gandomi, A., Yang, X.S., Talatahari, S., Alavi, A.: Firefly algorithm with chaos.
Comm Nonlinear Sci Numer Simulat 18(1), 89-98 (2013)
11. Garcıa-Nieto, J.M., Olivera, A.C., Alba, E.: Optimal cycle program of trac lights
with particle swarm optimization. IEEE Transactions On Evolutionary Computa-
tion 17(6), 823-839 (2013)
12. Guerrero, G., Cecilia, J., Llanes, A., Garcıa, J., Amos, M., Ujaldon, M.: Compar-
ative evaluation of platforms for parallel ant colony optimization. The Journal of
Supercomputing, 1-12 (2014)
13. Husselmann, A., Hawick, K.: Parallel parametric optimisation with firefly algo-
rithms on graphical processing units. In: Hamid (ed.) 2012 World Congress in
Computer Science, Computer Engineering, and Applied Computing (2012)
14. Jati, G.K., Manurung, R.: Suyanto: Discrete firefly algorithm for traveling salesman
problem: A new movement scheme. In: Yang, X.S., Cui, Z., Xiao, R., Gandomi,
A.H., Karamanoglu, M. (eds.) Swarm Intelligence and Bio-Inspired Computation,
pp. 295-312. Elsevier, Oxford (2013)
15. Jati, G.K., Suyanto: Evolutionary discrete firefly algorithm for travelling salesman
problem. In: Bouchachia, A. (ed.) ICAIS 2011. LNCS, vol. 6943, pp. 393-403.
Springer, Heidelberg (2011)
16. Johar, F., Azmin, F., Suaidi, M., Shibghatullah, A., Ahmad, B., Salleh, S., Aziz, M.,
Md Shukor, M.: A review of genetic algorithms and parallel genetic algorithms on
Graphics Processing Unit (GPU). In: 2013 IEEE International Conference on Con-
trol System, Computing and Engineering (ICCSCE), pp. 264-269 (November 2013)
17. Jones, N.C., Preface, P.A.P.: An Introduction to Bioinformatics Algorithms. Mas-
sachusetts Institute of Technology (2004)
18. Kallrath, J., Schreieck, A.: Discrete optimisation and real-world problems. In:
Hertzberger, B., Serazzi, G. (eds.) HPCN-Europe 1995. LNCS, vol. 919, pp. 351-
359. Springer, Heidelberg (1995)
19. Kavousi-Fard, A., Samet, H., Marzbani, F.: A new hybrid modified firefly algorithm
and support vector regression model for accurate short term load forecasting. Ex-
pert Systems with Applications 41(13), 6047-6056 (2014)
20. Kessaci, Y., Melab, N., Talbi, E.G.: A pareto-based metaheuristic for scheduling
HPC applications on a geographically distributed cloud federation. Cluster Com-
puting 16(3), 451-468 (2013)
21. Liao, T., Chang, P., Kuo, R., Liao, C.J.: A comparison of five hybrid metaheuristic
algorithms for unrelated parallel-machine scheduling and inbound trucks sequenc-
ing in multi-door cross docking systems. Appl Soft Comput 21(0), 180-193 (2014)
22. Luo, G.H., Huang, S.K., Chang, Y.S., Yuan, S.M.: A parallel bees algorithm im-
plementation on
{
GPU
}
. Journal of Systems Architecture 60(3), 271-279 (2014),
real-Time Embedded Software for Multi-Core Platforms
23. Van Luong, T., Melab, N., Talbi, E.-G.: GPU-based approaches for multiobjec-
tive local search algorithms. A case study: The flowshop scheduling problem. In:
Merz, P., Hao, J.-K. (eds.) EvoCOP 2011. LNCS, vol. 6622, pp. 155-166. Springer,
Heidelberg (2011)
24. Ma, W., Krishnamoorthy, S., Villa, O., Kowalski, K., Agrawal, G.: Optimizing ten-
sor contraction expressions for hybrid cpu-gpu execution. Cluster Computing 16(1),
131-155 (2013)
25. Maher, B., et al.: A firefly-inspired method for protein structure prediction in
lattice models. Biomhc. 4(1), 56-75 (2014)
26. Mallen-Fullerton, G.M., Hughes, J.A., Houghten, S., Fernandez-Anaya, G.: Bench-
mark datasets for the DNA fragment assembly problem. International Journal of
Bio-Inspired Computation 5(6), 384-394 (2013)
