LSMA-TLBO: A hybrid SMA-TLBO algorithm with lévy flight based mutation for numerical optimization and engineering design problems

Mohamed, 2018, A novel differential evolution algorithm for solving constrained engineering optimization problems, J Intell Manuf, 29, 659, 10.1007/s10845-017-1294-6 Deb, 1991, Optimal design of a welded beam via genetic algorithms, AIAA J, 29, 2013, 10.2514/3.10834 Simon, 2008, Biogeography-based optimization, IEEE Trans Evol Comput, 12, 702, 10.1109/TEVC.2008.919004 Karaboga, 2008, On the performance of artificial bee colony (ABC) algorithm, Appl Soft Comput, 8, 687, 10.1016/j.asoc.2007.05.007 Sori, 2020, Elite artificial bees’ colony algorithm to solve robot’s fuzzy constrained routing problem, Comput Intell, 36, 659, 10.1111/coin.12258 Dorigo, 2006, Ant colony optimization, IEEE Comput Intell Mag, 1, 28, 10.1109/MCI.2006.329691 Kennedy J, Eberhart R. Particle swarm optimization. In: Proceedings of ICNN95 - International conference on neural networks, vol. 4. 1995, p. 1942–8. http://dx.doi.org/10.1109/ICNN.1995.488968. Mirjalili, 2014, Grey wolf optimizer, Adv Eng Softw, 69, 46, 10.1016/j.advengsoft.2013.12.007 Mirjalili, 2016, The whale optimization algorithm, Adv Eng Softw, 95, 51, 10.1016/j.advengsoft.2016.01.008 Mirjalili, 2016, Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective discrete, and multi-objective problems, Neural Comput Appl, 27, 1053, 10.1007/s00521-015-1920-1 Yang, 2012, Flower pollination algorithm for global optimization Yang, 2012, Bat algorithm: a novel approach for global engineering optimization, Eng Comput, 29, 464, 10.1108/02644401211235834 Yang XS, Deb S. Cuckoo search via Lévy flights. In: 2009 World Congress on Nature and Biologically Inspired Computing, NABIC 2009 - Proceedings. 2009, p. 210–4. http://dx.doi.org/10.1109/NABIC.2009.5393690. Li, 2020, Slime mould algorithm: A new method for stochastic optimization, Future Gener Comput Syst, 111, 300, 10.1016/j.future.2020.03.055 Heidari, 2019, Harris hawks optimization: Algorithm and applications, Future Gener Comput Syst, 97, 849, 10.1016/j.future.2019.02.028 Mirjalili, 2017, Salp swarm algorithm: A bio-inspired optimizer for engineering design problems, Adv Eng Softw, 114, 163, 10.1016/j.advengsoft.2017.07.002 Mirjalili, 2015, Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm, Knowl-Based Syst, 89, 228, 10.1016/j.knosys.2015.07.006 Rao, 2011, Teaching-learning-based optimization: A novel method for constrained mechanical design optimization problems, Comput Aided Des, 43, 303, 10.1016/j.cad.2010.12.015 Savsani, 2016, Passing vehicle search (PVS): A novel metaheuristic algorithm, Appl Math Model, 40, 3951, 10.1016/j.apm.2015.10.040 Sadollah, 2018, A dynamic metaheuristic optimization model inspired by biological nervous systems: Neural network algorithm, Appl Soft Comput, 71, 747, 10.1016/j.asoc.2018.07.039 Mirjalili, 2016, SCA: A Sine Cosine Algorithm for solving optimization problems, Knowl-Based Syst, 96, 120, 10.1016/j.knosys.2015.12.022 Bao, 2019, A novel hybrid harris hawks optimization for color image multilevel thresholding segmentation, IEEE Access, 7, 76529, 10.1109/ACCESS.2019.2921545 Kundu, 2022, A hybrid salp swarm algorithm based on TLBO for reliability redundancy allocation problems, Appl Intell, 10.1007/s10489-021-02862-w Kundu, 2022, A hybrid ITLHHO algorithm for numerical and engineering optimization problems, Int J Intell Syst, 37, 3900, 10.1002/int.22707 Kundu, 2022, A hybrid TLNNABC algorithm for reliability optimization and engineering design problems, Eng Comput, 10.1007/s00366-021-01572-8 Gao, 2020, The hybrid grey wolf optimization-slime mould algorithm, J Phys Conf Ser, 1617, 10.1088/1742-6596/1617/1/012034 Abualigah, 2021, Improved slime mould algorithm by opposition-based learning and Lévy flight distribution for global optimization and advances in real-world engineering problems, J Ambient Intell Humaniz Comput Liu, 2021, Boosting slime mould algorithm for parameter identification of photovoltaic models, Energy, 12, 1 Zubaidi, 2020, Hybridised artificial neural network model with slime mould algorithm: A novel methodology for prediction of urban stochastic water demand, Water, 12 Yu, 2021, Boosting quantum rotation gate embedded slime mould algorithm, Expert Syst Appl, 181, 10.1016/j.eswa.2021.115082 Price, 2018 Brajevic, 2013, An upgraded artificial bee colony (ABC) algorithm for constrained optimization problems, J Intell Manuf, 24, 729, 10.1007/s10845-011-0621-6 Kumar, 2017, An astrophysics-inspired grey wolf algorithm for numerical optimization and its application to engineering design problems, Adv Eng Softw, 112, 231, 10.1016/j.advengsoft.2017.05.008 Mezura-Montes, 2005, A simple multimembered evolution strategy to solve constrained optimization problems, IEEE Trans Evol Comput, 9, 1, 10.1109/TEVC.2004.836819 Kaveh, 2017, A novel meta-heuristic optimization algorithm: Thermal exchange optimization, Adv Eng Softw, 110, 69, 10.1016/j.advengsoft.2017.03.014 Askarzadeh, 2016, A novel metaheuristic method for solving constrained engineering optimization problems: Crow search algorithm, Comput Struct, 169, 1, 10.1016/j.compstruc.2016.03.001 Zhang, 2018, Queuing search algorithm: A novel metaheuristic algorithm for solving engineering optimization problems, Appl Math Model, 63, 464, 10.1016/j.apm.2018.06.036 Eskandar, 2012, Water cycle algorithm-a novel metaheuristic optimization method for solving constrained engineering optimization problems, Comput Struct, 110, 151, 10.1016/j.compstruc.2012.07.010 Zhang, 2008, Differential evolution with dynamic stochastic selection for constrained optimization, Inform Sci, 178, 3043, 10.1016/j.ins.2008.02.014 Wang, 2010, An effective differential evolution with level comparison for constrained engineering design, Struct Multidiscip Optim, 41, 947, 10.1007/s00158-009-0454-5 Gandomi, 2013, Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems, Eng Comput, 29, 17, 10.1007/s00366-011-0241-y He, 2007, An effective co-evolutionary particle swarm optimization for constrained engineering design problems, Eng Appl Artif Intell, 20, 89, 10.1016/j.engappai.2006.03.003 Rashedi, 2009, Gsa: a gravitational search algorithm, Inform Sci, 179, 2232, 10.1016/j.ins.2009.03.004 Lee, 2005, A new meta-heuristic algorithm for continuous engineering optimization: harmony search theory and practice, Comput Methods Appl Mech Engrg, 194, 3902, 10.1016/j.cma.2004.09.007 Gandomi, 2013, Bat algorithm for constrained optimization tasks, Neural Comput Appl, 22, 1239, 10.1007/s00521-012-1028-9 Coello, 2002, Constraint-handling in genetic algorithms through the use of dominance-based tournament selection, Adv Eng Inf, 16, 193, 10.1016/S1474-0346(02)00011-3 Mirjalili, 2016, Multi-verse optimizer: a nature-inspired algorithm for global optimization, Neural Comput Appl, 27, 495, 10.1007/s00521-015-1870-7 Rocha, 2009, Hybridizing the electromagnetism-like algorithm with descent search for solving engineering design problems, Int J Comput Math, 86, 1932, 10.1080/00207160902971533 Zhang, 2020, Group teaching optimization algorithm: A novel metaheuristic method for solving global optimization problems, Expert Syst Appl, 148, 10.1016/j.eswa.2020.113246