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Elsayed, S. M., Sarker, R. A., & Essam, D. L. (2014). A new genetic algorithm for solving
optimization problems. Engineering Applications of Arti
cial Intelligence, 27,57
-
69.
Elshazly, H. I., Azar, A. T., Hassanien, A. E., & Elkorany, A. M. (2013). Hybrid system based on
rough sets and genetic algorithms for medical data classifications. International Journal of
Fuzzy System Applications (IJFSA), 3(4), 31
-
46.
Engelbrecht, A. P. (2002). Computational intelligence: An introduction. New York: Wiley.
Engelbrecht, A. P. (2005). Fundamentals of computational swarm intelligence. New York: Wiley.
Fleming, P. J., & Purshouse, R. C. (2002). Evolutionary algorithms in control systems
engineering: A survey. Control Engineering Practice, 10(11), 1223
1241.
Fonseca, C. M., & Fleming, P. J. (1994). Multiobjective optimal controller design with genetic
algorithms. In The International Conference on Control, March 21
-
24, 1994, Coventry
-
749). doi:
10.1049/cp:19940225
.
Gaing, Z. L. (2004). A particle swarm optimization approach for optimum design of PID controller
in AVR system. IEEE Transactions on Energy Conversion, 19(2), 384
(pp. 745
-
391.
Gero, J., & Radford, A. (1978). A dynamic programming approach to the optimum lighting
problem. Engineering Optimization, 3,71
-
82.
Gosh, A., Das, S., Chowdhury, A., & Giri, R. (2011). An ecologically inspired direct search
method for solving optimal control problems with Bezier parameterization. Engineering
Applications of Arti
cial Intelligence, 24(7), 1195
-
1203.
Holland, J. H. (1975). Adaptation in natural and arti
cial systems: An introductory analysis with
applications to biology, control, and arti
cial intelligence. Ann Arbor, Michigan: University
of Michigan Press.
Jamili, A., Shafia, M. A., & Tavakkoli-Moghaddam, R. (2011). A hybrid algorithm based on
particle swarm optimization and simulated annealing for a periodic job shop scheduling
problem. The International Journal of Advanced Manufacturing Technology, 54(1
-
4),
-
322.
Jeong, S., Hasegawa, S., Shimoyama, K., & Obayashi, A. (2009). Development and investigation
of efficient GA/PSO-hybrid algorithm applicable to real-world design optimization. IEEE
Computational Intelligence Magazine, 4(3), 36
309
-
44.
Kennedy, J., & Eberhart, R. C. (1995). Particle swarm optimization. In The IEEE International
Conference on Neural Networks, November/December, 1995, Perth (pp. 1942
-
1948). doi:
10.
-
Ker-Wei, Y., & Shang-Chang, H. (2006). An application of AC servo motor by using particle
swarm optimization based sliding mode controller. In The IEEE International Conference on
Knowles, J., & Corne, D. (1999). The Pareto archived evolution strategy: A new baseline
algorithm for multiobjective optimization. In The Proceedings of the 1999 Congress on
781913
.
Li, Z., Yang, K., Bogdan, S., & Xu, B. (2013). On motion optimization of robotic manipulators
with strong nonlinear dynamic coupling using support area level set algorithm. International
Journal of Control, Automation and Systems, 11(6), 1266
1275.
Mahmoodabadi, M. J., Safaie, A. A., Bagheri, A., & Nariman-zadeh, N. (2013). A novel
combination of particle swarm optimization and genetic algorithm for pareto optimal design of
a
five-degree of freedom vehicle vibration model. Applied Soft Computing, 13(5), 2577
-
2591.
Mahmoodabadi, M. J., Bagheri, A., Arabani Mostaghim, S., & Bisheban, M. (2011). Simulation of
stability using Java application for Pareto design of controllers based on a new multi-objective
particle swarm optimization. Mathematical and Computer Modelling, 54(5
-
1607.
Mahmoodabadi, M. J., Momennejad, S., & Bagheri, A. (2014a). Online optimal decoupled sliding
mode control based on moving least squares and particle swarm optimization. Information
Sciences, 268, 342
-
356.
6), 1584
-
-
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