Information Technology Reference
In-Depth Information
References
Abdel-Kader, R. F. (2010). Generically improved PSO algorithm for ef
cient data clustering. In
The 2010 Second International Conference on Machine Learning and Computing (ICMLC),
February 9
75). doi:
10.1109/ICMLC.2010.19
.
Ahmadi, M. H., Aghaj, S. S. G., & Nazeri, A. (2013). Prediction of power in solar stirling heat
engine by using neural network based on hybrid genetic algorithm and particle swarm
optimization. Neural Computing and Applications, 22(6), 1141
-
1150.
Altun, A. A. (2013). A combination of genetic algorithm, particle swarm optimization and neural
network for palmprint recognition. Neural Computing and Applications, 22(1), 27
-
33.
Aziz, A. S. A., Azar, A. T., Salama, M. A., Hassanien, A. E., & Hanafy, S. E. O. (2013). In The
2013 Federated Conference on Computer Science and Information Systems (FedCSIS),
September 8-11, 2013, Krak
รณ
w (pp. 769
-
774).
Bhuvaneswari, R., Sakthivel, V. P., Subramanian, S., & Bellarmine, G. T. (2009). Hybrid
approach using GA and PSO for alternator design. In The 2009. SOUTHEASTCON
11, 2010, Bangalore (pp. 71
-
-
09. IEEE
'
174). doi:
10.1109/SECON.2009.5174070
.
Blake, A. (1989). Comparison of the efficiency of deterministic and stochastic algorithms for
visual reconstruction. IEEE Transactions on Pattern Analysis and Machine Intelligence, 11(1),
2
Southeastcon, March 5
8, 2009, Atlanta (pp. 169
-
-
12.
Castillo-Villar, K. K., Smith, N. R., & Herbert-Acero, J. F. (2014). Design and optimization of
capacitated supply chain networks including quality measures. Mathematical Problems in
Engineering, 2014, 17, Article ID 218913. doi:
10.1155/2014/218913
.
Castillo-Villar, K. K., Smith, N. R., & Simonton, J. L. (2012). The impact of the cost of quality on
serial supply-chain network design. International Journal of Production Research, 50(19),
5544
-
5566.
Chang, W. D. (2007). A multi-crossover genetic approach to multivariable PID controllers tuning.
Expert Systems with Applications, 33(3), 620
-
626.
Chen, J. L., & Chang, W. D. (2009). Feedback linearization control of a two link robot using a
multi-crossover genetic algorithm. Expert Systems with Applications, 36(2), 4154
-
4159.
Chen, C.-H., & Liao, Y.- Y. (2014). Tribal particle swarm optimization for neurofuzzy inference
systems and its prediction applications. Communications in Nonlinear Science and Numerical
Simulation, 19(4), 914
-
929.
Chen, Z., Meng, W., Zhang, J., & Zeng, J. (2009). Scheme of sliding mode control based on
modified particle swarm optimization. Systems Engineering-Theory & Practice, 29(5),
137
-
141.
Chutarat, A. (2001). Experience of light: The use of an inverse method and a genetic algorithm in
day lighting design. Ph.D. Thesis, Department of Architecture, MIT, Massachusetts, USA.
Cordella, F., Zollo, L., Guglielmelli, E., & Siciliano, B. (2012). A bio-inspired grasp optimization
algorithm for an anthropomorphic robotic hand. International Journal on Interactive Design
and Manufacturing (IJIDeM), 6(2), 113
-
122.
Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. (2002). A fast and elitist multiobjective genetic
algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6(2), 182
-
197.
Deb, K., & Padhye, N. (2013). Enhancing performance of particle swarm optimization through an
algorithmic link with genetic algorithms. Computational Optimization and Applications, 57(3),
761
-
794.
Dhadwal, M. K., Jung, S. N., & Kim, C. J. (2014). Advanced particle swarm assisted genetic
algorithm for constrained optimization problems. Computational Optimization and Applica-
tions, 58(3), 781
-
806.
Eberhart, R., Simpson, P., & Dobbins, R.
-
(1996). Computational
intelligence PC tools.
Massachusetts: Academic Press Professional Inc.
Eberhart, R. C., Kennedy, J. (1995). A new optimizer using particle swarm theory. In The
Proceedings of the Sixth International Symposium on Micro Machine and Human Science,
October 4
6, 1995, Nagoya (pp. 39
43). doi:
10.1109/MHS.1995.494215
.
-
-
Search WWH ::
Custom Search