[1]
|
S. Akhtar, K. Tai and T. Ray, A socio-behavioural simulation model for engineering design optimization, Engineering Optimization, 34 (2002), 341-354.
doi: 10.1080/03052150212723.
|
[2]
|
A. D. Belegundu and J. S. Arora, A study of mathematical programming methods for structural optimization. Part I: Theory, International Journal for Numerical Methods in Engineering, 21 (1985), 1583-1599.
doi: 10.1002/nme.1620210904.
|
[3]
|
M. Y. Cheng and D. Prayogo, Symbiotic organisms search: a new metaheuristic optimization algorithm, Computers & Structures, 139 (2014), 98-112.
doi: 10.1016/j.compstruc.2014.03.007.
|
[4]
|
H. Chickermane and H. C. Gea, Structural optimization using a new local approximation method, International Journal for Numerical Methods in Engineering, 39 (1996), 829-846.
doi: 10.1002/(SICI)1097-0207(19960315)39:5<829::AID-NME884>3.0.CO;2-U.
|
[5]
|
C. A. C. Coello, Constraint-handling using an evolutionary multiobjective optimization technique, Civil Engineering Systems, 17 (2000), 319-346.
doi: 10.1080/02630250008970288.
|
[6]
|
C. A. C. Coello and E. M. Montes, Constraint-handling in genetic algorithms through the use of dominance-based tournament selection, Advanced Engineering Informatics, 16 (2002), 193-203.
doi: 10.1016/S1474-0346(02)00011-3.
|
[7]
|
K. Deb, Optimal design of a welded beam via genetic algorithms, AIAA Journal, 29 (1991), 2013-2015.
doi: 10.2514/3.10834.
|
[8]
|
K. Deb, An efficient constraint handling method for genetic algorithms, Computer Methods in Applied Mechanics and Engineering, 186 (2000), 311-338.
doi: 10.1016/S0045-7825(99)00389-8.
|
[9]
|
K. Deb, GeneAS: A robust optimal design technique for mechanical component design, in Evolutionary Algorithms in Engineering Applications, Springer, Berlin, Heidelberg, 1997,497–514.
doi: 10.1007/978-3-662-03423-1_27.
|
[10]
|
K. Deb, A. Pratap and S. Agarwal, A fast and elitist multiobjective genetic algorithm: NSGA-II, IEEE Transactions on Evolutionary Computation, 6 (2002), 182-197.
doi: 10.1109/4235.996017.
|
[11]
|
D. Deutsch, Quantum theory, the Church-Turing principle and the universal quantum computer, Proceedings of the Royal Society of London Series A, 400 (1985), 97-117.
doi: 10.1098/rspa.1985.0070.
|
[12]
|
M. Dorigo, M. Birattari and T. Stutzle, Ant colony optimization, IEEE Computational Intelligence Magazine, (2006), 28–39.
|
[13]
|
A. H. Gandomi and X. S. Yang, Benchmark problems in structural optimization, in Computational Optimization, Methods and Algorithms, Springer, Berlin, 2011,259–281.
doi: 10.1007/978-3-642-20859-1_12.
|
[14]
|
A. H. Gandomi, X. S. Yang and A. H. Alavi, Cuckoo search algorithm: A metaheuristic approach to solve structural optimization problems, Engineering with Computers, 29 (2013), 17-35.
|
[15]
|
K. H. Han and J. H. Kim, Genetic quantum algorithm and its application to combinatorial optimization problem, Proceedings of the 2000 Congress on Evolutionary Computation, 2 (2000), 1354-1360.
doi: 10.1109/CEC.2000.870809.
|
[16]
|
Q. He and L. Wang, An effective co-evolutionary particle swarm optimization for constrained engineering design problems, Engineering Applications of Artificial Intelligence, 20 (2007), 89-99.
doi: 10.1016/j.engappai.2006.03.003.
|
[17]
|
C. Hui, Z. Jiashu and Z. Chao, Chaos updating rotated gates quantum-inspired genetic algorithm. Communications, Circuits and Systems, 2004 International Conference on Communications, Circuits and Systems, Chengdu, 2 (2004), 1108–1112.
doi: 10.1109/ICCCAS.2004.1346370.
|
[18]
|
B. K. Kannan and S. N. Kramer, An augmented Lagrange multiplier based method for mixed integer discrete continuous optimization and its applications to mechanical design, Journal of Mechanical Design, 116 (1994), 405-411.
doi: 10.1115/1.2919393.
|
[19]
|
D. Karaboga and B. Basturk, A powerful and efficient algorithm for numerical function optimization: Artificial bee colony (ABC) algorithm, Journal of Global Optimization, 2007, 39(4), 459–471.
doi: 10.1007/s10898-007-9149-x.
|
[20]
|
A. Kaveh and S. Talatahari, An improved ant colony optimization for constrained engineering design problems, Engineering Computations, 27 (2010), 155-182.
doi: 10.1108/02644401011008577.
|
[21]
|
J. Kennedy, Particle Swarm Optimization. Encyclopedia of Machine Learning, Springer, Boston, MA, 2011,760–766.
doi: 10.1007/978-0-387-30164-8_630.
|
[22]
|
R. A. Krohling and L. dos Santos Coelho, Coevolutionary particle swarm optimization using Gaussian distribution for solving constrained optimization problems, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 36 (2006), 1407-1416.
doi: 10.1109/TSMCB.2006.873185.
|
[23]
|
K. S. Lee and Z. W. Geem, A new meta-heuristic algorithm for continuous engineering optimization: Harmony search theory and practice, Computer Methods in Applied Mechanics and Engineering, 194 (2005), 3902-3933.
doi: 10.1016/j.cma.2004.09.007.
|
[24]
|
L. J. Li, Z. B. Huang and F. Liu, A heuristic particle swarm optimizer for optimization of pin connected structures, Computers & Structures, 85 (2007), 340-349.
doi: 10.1016/j.compstruc.2006.11.020.
|
[25]
|
P. Li and S. Li, Quantum-inspired evolutionary algorithm for continuous space optimization based on Bloch coordinates of qubits, Neurocomputing, 72 (2008), 581-591.
doi: 10.1016/j.neucom.2007.11.017.
|
[26]
|
J. Liu, C. Wu, G. Wu and X. Wang, A novel differential search algorithm and applications for structure design, Applied Mathematics and Computation, 268 (2015), 246-269.
doi: 10.1016/j.amc.2015.06.036.
|
[27]
|
F. S. Lobato, V. Steffen and Jr ., Fish swarm optimization algorithm applied to engineering system design, Latin American Journal of Solids and Structures, 11 (2014), 143-156.
|
[28]
|
W. Long, W. Zhang, Y. Huang and Y. Chen, A hybrid cuckoo search algorithm with feasibility-based rule for constrained structural optimization, Journal of Central South University, 21 (2014), 3197-3204.
doi: 10.1007/s11771-014-2291-y.
|
[29]
|
E. Mezura-Montes and C. A. C. Coello, An empirical study about the usefulness of evolution strategies to solve constrained optimization problems, International Journal of General Systems, 37 (2008), 443-473.
doi: 10.1080/03081070701303470.
|
[30]
|
E. Mezura-Montes, C. A. C. Coello and R. Landa-Becerra, Engineering optimization using simple evolutionary algorithm, in Proceedings. 15th IEEE International Conference on Tools with Artificial Intelligence, Sacramento, CA, 2003,149–156.
doi: 10.1109/TAI.2003.1250183.
|
[31]
|
S. Mirjalili, Dragonfly algorithm: A new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems, Neural Computing and Applications, 27 (2016), 1053-1073.
|
[32]
|
S. Mirjalili, S. M. Mirjalili and A. Lewis, Grey wolf optimizer, Advances in Engineering Software, 69 (2014), 46-61.
|
[33]
|
S. H. S. Moosavi and V. K. Bardsiri, Satin bowerbird optimizer: A new optimization algorithm to optimize ANFIS for software development effort estimation, Engineering Applications of Artificial Intelligence, 60 (2012), 1-15.
doi: 10.1016/j.engappai.2017.01.006.
|
[34]
|
K. M. Ragsdell and D. T. Phillips, Optimal design of a class of welded structures using geometric programming, Journal of Manufacturing Science and Engineering, 98 (1976), 1021-1025.
doi: 10.1115/1.3438995.
|
[35]
|
S. S. Rao, Engineering Optimization: Theory and Practice, John Wiley & Sons, Inc., New York, 2009.
|
[36]
|
T. Ray and K. M. Liew, Society and civilization: An optimization algorithm based on the simulation of social behavior, IEEE Transactions on Evolutionary Computation, 7 (2003), 386-396.
doi: 10.1109/TEVC.2003.814902.
|
[37]
|
E. Rashedi, H. Nezamabadi-Pour and S. Saryazdi, GSA: A gravitational search algorithm, Information Sciences, 179 (2009), 2232-2248.
|
[38]
|
E. Sandgren, Nonlinear integer and discrete programming in mechanical design optimization, Journal of Mechanical Design, 112 (1990), 223-229.
doi: 10.1115/1.2912596.
|
[39]
|
R. Storn and K. Price, Differential evolution – A simple and efficient heuristic for global optimization over continuous spaces, Journal of Global Optimization, 11 (1997), 341-359.
doi: 10.1023/A:1008202821328.
|
[40]
|
L. Wang, F. Tang and H. Wu, Hybrid genetic algorithm based on quantum computing for numerical optimization and parameter estimation, Applied Mathematics and Computation, 171 (2005), 1141-1156.
doi: 10.1016/j.amc.2005.01.115.
|
[41]
|
D. H. Wolpert and W. G. Macready, No free lunch theorems for optimization, IEEE Transactions on Evolutionary Computation, 1 (1997), 67-82.
doi: 10.1109/4235.585893.
|
[42]
|
X. S. Yang, Flower pollination algorithm for global optimization, Unconventional Computation and Natural Computation, Springer, Berlin, Heidelberg, 2012,240–249.
doi: 10.1007/978-3-642-32894-7_27.
|
[43]
|
G. Zhang, W. Jin and L. Hu, A novel parallel quantum genetic algorithm, Proceedings of the Fourth International Conference on Parallel and Distributed Computing, Applications and Technologies, Chengdu, China, 2003,693–697.
doi: 10.1109/PDCAT.2003.1236393.
|