Swarm Intelligence Techniques and Their Adaptive Nature with Applications - Complex System Modelling and Control Through Intelligent Soft Computations - page 267

which not only meets quality requirements but also can easily deal with imple-
mentation related issues. Despite of numerous dif culties SI techniques gaining

popularity and also consuming enormously in various applications.

References

Al Rashidi, M. R., & El-Hawary, M. E. (2009). A survey of particle swarm optimization
applications in electric power systems. IEEE Transactions on Evolutionary Computation, 13
(4), 913 - 918.
Alzalg, B., Anghel, C., Gan, W., Huang, Q., Rahman, M., & Shum, A. (2011). A computational
analysis of the optimal power problem. In Institute of Mathematics and its Application. IMA
Preprint Series 2396. University of Minnesota.
Amit, Y. (2002). 2D object detection and recognition: Models, algorithms, and networks.
Cambridge: MIT Press.
Borwein, J. M. & Lewis, A. S. (2010). Convex analysis and nonlinear optimization: Theory and
examples (2nd ed.). Berlin, Springer.
Bullnheimer, B., Hartl, R. F., & Strauss, C. (1997). A new rank based version of the ant system.
A computational study. SFB Adaptive Information Systems and Modelling in Economics and
Management Science, 7,25

38.
Chakraborty, B., & Chakraborty, G. (2013). Fuzzy consistency measure with particle swarm
optimization for feature selection. In 2013 IEEE International Conference on Systems, Man,
and Cybernetics (SMC) (pp. 4311

-

4315), October 13 16, 2013, Manchester. IEEE. doi: 10.

- -

1109/SMC.2013.735 .
Chandra Mohan, B., & Baskaran, R. (2012). A survey: Ant colony optimization based recent
research and implementation on several engineering domain. Expert Systems with Applications,
39(4), 4618 - 4627.
Chandrasekhar, U., & Naga, P. (2011). Recent trends in ant colony optimization and data
clustering: A brief survey. In 2011 2nd International Conference on Intelligent Agent and
Multi-agent Systems (IAMA) (pp. 32 - 36), September 7 - 9, 2011, Chennai. IEEE. doi: 10.1109/
IAMA.2011.6048999 .
Chu, S.-C., Roddick, J. F., & Pan, J.-S. (2003). Parallel particle swarm optimization algorithm with
communication strategies. Journal of Information Science and Engineering, 21(4), 809 818.

Clerc, M., & Kennedy, J. (2002). The particle swarm-explosion, stability, and convergence in a
multidimensional complex space. IEEE Transactions on Evolutionary Computation, 6(1),
58

-

73.
Das, G., Pattnaik, P. K., & Padhy, S. K. (2014). Artificial neural network trained by particle swarm
optimization for non-linear channel equalization. Expert Systems with Applications, 41(7),
3491

-

3496.
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.
Dorigo, M. (1992). Optimization, learning and natural algorithms. Ph.D. Thesis, Politecnico di
Milano, Italy.
Dorigo, M., Birattari, M., & Stutzle, T. (2006). Ant colony optimization. Computational
Intelligence Magazine, IEEE, 1(4), 28 - 39.
Eslami, M., Shareef, H., Khajehzadeh, M., & Mohamed, A. (2012). A survey of the state of the art
in particle swarm optimization. Research Journal of Applied Sciences, Engineering and
Technology, 4(9), 1181 - 1197.
Ganapathy, K., Vaidehi, V., Kannan, B., & Murugan, H. (2014). Hierarchical particle swarm
optimization with ortho-cyclic circles. Expert Systems with Applications, 41(7), 3460 - 3476.
Gonzalez, R. C., & Woods, R. E. (2002). Digital image processing. New Jersey: Prentice Hall.

-