A new firefly algorithm with improved global exploration and convergence with application to engineering optimization
Tài liệu tham khảo
Eslami, 2022, Aphid-ant mutualism: A novel nature-inspired metaheuristic algorithm for solving optimization problems, Math. Comput. Simulation, 10.1016/j.matcom.2022.05.015
Gang, 2022, Quadratic interpolation boosted black widow spider-inspired optimization algorithm with wavelet mutation, Math. Comput. Simulation, 200, 428, 10.1016/j.matcom.2022.04.031
Tian, 2022, An electronic transition-based bare bones particle swarm optimization algorithm for high dimensional optimization problems, PLoS One, 17, 10.1371/journal.pone.0271925
El-Shorbagy, 2022, Chaotic fruit fly algorithm for solving engineering design problems, Complexity, 2022, 10.1155/2022/6627409
Givi, 2021, GBUO:The good, the bad, and the ugly optimizer, Appl. Sci., 11, 2042, 10.3390/app11052042
Radosavljevic, 2018
Kaveh, 2020, Billiards-inspired optimization algorithm; A new meta-heuristic method, Structures, 27, 10.1016/j.istruc.2020.07.058
X.-S. Yang, S. Deb, Cuckoo search via Lévy _ights, in: Proc. World Congr. Nature Biologically Inspired Comput., NaBIC, 2009, pp. 210–214.
Blum, 2003, Metaheuristics in combinatorial optimization: Overview and conceptual comparison, ACM Comput. Surv., 35, 268, 10.1145/937503.937505
Woźniak, 2021, Heuristic optimization of multipulse rectifier for reduced energy consumption, IEEE Trans. Ind. Inform., 18, 5515, 10.1109/TII.2021.3117976
Goodarzi, 2022, An integrated multi-criteria decision-making and multi-objective optimization framework for green supplier evaluation and optimal order allocation under uncertainty, Decis. Anal. J., 4
Arora, 2022, An efficient ANFIS-EEBAT approach to estimate effort of scrum projects, Sci. Rep., 12, 1, 10.1038/s41598-022-11565-2
Sinha, 2022, A novel two-phase location analytics model for determining operating station locations of emerging air taxi services, Decis. Anal. J., 2
Basak, 2022, A union of deep learning and swarm-based optimization for 3D human action recognition, Sci. Rep., 12, 1, 10.1038/s41598-022-09293-8
Vaisi, 2022, A review of optimization models and applications in robotic manufacturing systems: Industry 4.0 and beyond, Decis. Anal. J., 10.1016/j.dajour.2022.100031
Azizi, 2022, Optimum design of truss structures by material generation algorithm with discrete variables, Decis. Anal. J., 3
Nguyen, 2022, Wild geese algorithm for the combination problem of network reconfiguration and distributed generation placement, Int. J. Electr. Eng. Inform., 14, 76
Azizi, 2022, Optimal design of low-and high-rise building structures by Tribe-Harmony search algorithm, Decis. Anal. J., 10.1016/j.dajour.2022.100067
Zhang, 2022, A salp swarm algorithm based on Harris eagle foraging strategy, Math. Comput. Simulation
Hu, 2022, Slope reliability evaluation using an improved Kriging active learning method with various active learning functions, Arab. J. Geosci., 15, 1, 10.1007/s12517-022-10315-y
Sakthivel, 2022, Short term scheduling of hydrothermal power systems with photovoltaic and pumped storage plants using quasi-oppositional turbulent water flow optimization, Renew. Energy, 191, 459, 10.1016/j.renene.2022.04.050
Sakthivel, 2022, Quasi-oppositional turbulent water flow-based optimization for cascaded short term hydrothermal scheduling with valve-point effects and multiple fuels, Energy, 251, 10.1016/j.energy.2022.123905
Çimen, 2022, A novel hybrid firefly–whale optimization algorithm and its application to optimization of MPC parameters, Soft Comput., 26, 1845, 10.1007/s00500-021-06441-6
Storn, 1997, Differential evolution - A simple and efficient heuristic for global optimization over continuous spaces, J. Global Optim., 11, 341, 10.1023/A:1008202821328
Koza, 1994, 32
Booker, 1989, Classifier systems and genetic algorithms, Artificial Intelligence, 40, 235, 10.1016/0004-3702(89)90050-7
Geem, 2001, A new heuristic optimization algorithm: Harmony search, J. Simul., 76, 60, 10.1177/003754970107600201
Koziel, 2011
Kirkpatrick, 1983, Optimization by simulated annealing, Science, 220, 671, 10.1126/science.220.4598.671
Yang, 2008, 242
J. Kennedy, R. Eberhart, Particle swarm optimization (PSO), in: Proc. IEEE Int. Conf. Neural Netw., Perth, WA, Australia, 1995, pp. 1942–1948.
Karaboga, 2007, A powerful and efficient algorithm for numerical function optimization: Artificial bee colony (ABC) algorithm, J. Global Optim., 39, 459, 10.1007/s10898-007-9149-x
Yang, 2010, A new metaheuristic bat-inspired algorithm, vol. SCI 284, 65
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
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
Yang, 2012, Flower pollination algorithm for global optimization, 240
Mirjalili, 2014, Grey wolf optimizer, Adv. Eng. Softw., 69, 46, 10.1016/j.advengsoft.2013.12.007
Cheng, 2015, A competitive swarm optimizer for large scale optimization, IEEE Trans. Cybern., 45, 191, 10.1109/TCYB.2014.2322602
Mirjalili, 2015, Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm, Knowl.-Based Syst., 89, 228, 10.1016/j.knosys.2015.07.006
Kaveh, 2016, Water evaporation optimization: A novel physically inspired optimization algorithm, Comput. Struct., 167, 69, 10.1016/j.compstruc.2016.01.008
Y., 2016, A new rooted tree optimization algorithm for economic dispatch with valve-point effect, Int. J. Electr. Power Energy Syst., 79, 298, 10.1016/j.ijepes.2016.01.028
M., 2016, Lion optimization algorithm (LOA): A nature-inspired metaheuristic algorithm, J. Comput. Des. Eng., 3, 24
Mirjalili, 2016, The whale optimization algorithm, Adv. Eng. Softw., 95, 51, 10.1016/j.advengsoft.2016.01.008
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
Huang, 2016, Artificial infectious disease optimization: A SEIQR epidemic dynamic model-based function optimization algorithm, Swarm Evol. Comput., 27, 31, 10.1016/j.swevo.2015.09.007
Q., 2017, Collective decision optimization algorithm: A new heuristic optimization method, Neurocomputing, 221, 123, 10.1016/j.neucom.2016.09.068
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
Baykasoglu, 2017, Weighted superposition attraction (WSA): A swarm intelligence algorithm for optimization problems–Part 1: Unconstrained optimization, Appl. Soft Comput., 56, 520, 10.1016/j.asoc.2015.10.036
S., 2017, Grasshopper optimisation algorithm: Theory and application, Adv. Eng. Softw., 105, 30, 10.1016/j.advengsoft.2017.01.004
A., 2018, Tree growth algorithm (TGA): A novel approach for solving optimization problems, Eng. Appl. Artif. Intell., 72, 393, 10.1016/j.engappai.2018.04.021
V.B., 2018, A very optimistic method of minimization (VOMMI) for unconstrained problems, Inform. Sci., 454, 255
N.A., 2018, Pity beetle algorithm—A new metaheuristic inspired by the behavior of bark beetles, Adv. Eng. Softw., 121, 147, 10.1016/j.advengsoft.2018.04.007
Ghasemi, 2018, CFA optimizer: A new and powerful algorithm inspired by Franklin’s and Coulomb’s laws theory for solving the economic load dispatch problems, Int. Trans. Electr. Energy Syst., 28, 10.1002/etep.2536
G., 2018, Emperor penguin optimizer: A bio-inspired algorithm for engineering problems, Knowl.-Based Syst., 159, 20, 10.1016/j.knosys.2018.06.001
Shadravan, 2019, The sailfish optimizer: A novel nature inspired metaheuristic algorithm for solving constrained engineering optimization problems, Eng. Appl. Artif. Intell., 80, 20, 10.1016/j.engappai.2019.01.001
M., 2020, Football game based optimization: An application to solve energy commitment problem, Int. J. Intell. Eng. Syst., 13, 514
Faramarzi, 2020, Marine predators algorithm: A nature-inspired metaheuristic, Expert Syst. Appl., 152, 10.1016/j.eswa.2020.113377
Ghasemi, 2020, A novel and effective optimization algorithm for global optimization and its engineering applications: Turbulent flow of water-based optimization (TFWO), Eng. Appl. Artif. Intell., 92, 10.1016/j.engappai.2020.103666
Abualigah, 2021, Aquila optimizer: A novel meta-heuristic optimization algorithm, Comput. Ind. Eng., 157, 10.1016/j.cie.2021.107250
Abualigah, 2021, The arithmetic optimization algorithm, Comput. Methods Appl. Mech. Engrg., 376, 10.1016/j.cma.2020.113609
Połap, 2021, Red fox optimization algorithm, Expert Syst. Appl., 166, 10.1016/j.eswa.2020.114107
Li, 2022, Integrated optimization algorithm: A metaheuristic approach for complicated optimization, Inform. Sci., 586, 424, 10.1016/j.ins.2021.11.043
Agushaka, 2022, Dwarf mongoose optimization algorithm, Comput. Methods Appl. Mech. Engrg., 391, 10.1016/j.cma.2022.114570
Zhong, 2022, Beluga whale optimization: A novel nature-inspired metaheuristic algorithm, Knowl.-Based Syst.
Emami, 2022, Stock exchange trading optimization algorithm: A human-inspired method for global optimization, J. Supercomput., 78, 2125, 10.1007/s11227-021-03943-w
Akbari, 2022, The cheetah optimizer: A nature-inspired metaheuristic algorithm for large-scale optimization problems, Sci. Rep., 12, 1, 10.1038/s41598-022-14338-z
Ahmadianfar, 2022, INFO: An efficient optimization algorithm based on weighted mean of vectors, Expert Syst. Appl., 10.1016/j.eswa.2022.116516
Ghasemi, 2022, Circulatory system based optimization (CSBO): An expert multilevel biologically inspired meta-heuristic algorithm, Eng. Appl. Comput. Fluid Mech., 16, 1483
Moazenzadeh, 2018, Coupling a firefly algorithm with support vector regression to predict evaporation in northern Iran, Eng. Appl. Comput. Fluid Mech., 12, 584
Chhikara, 2018, An improved dynamic discrete firefly algorithm for blind image steganalysis, Int. J. Mach. Learn. Cybern., 9, 821, 10.1007/s13042-016-0610-3
Lagunes, 2018, Parameter optimization for membership functions of type-2 fuzzy controllers for autonomous mobile robots using the firefly algorithm, 569
Shaik, 2020, A power system restoration method using voltage source converter–high-voltage direct current technology, aided by time-series neural network with firefly algorithm, Soft Comput., 24, 9495, 10.1007/s00500-019-04459-5
Pereira, 2021, A proposal to use the inverse problem for determining parameters in a constitutive model for concrete, Soft Comput., 1
Zhang, 2021, Gender-based deep learning firefly optimization method for test data generation, Comput. Intell. Neurosci., 2021
Zhou, 2020, Inverse identification of modified Johnson–Cook model for cutting titanium alloy Ti6Al4V using firefly algorithm, Proc. Inst. Mech. Eng. B, 234, 584, 10.1177/0954405419864003
Devanathan, 2021, Multi objective optimization of process parameters by firefly algorithm during the friction stir welding of metal matrix composites, Trans. FAMENA, 45, 10.21278/TOF.451018520
Zhao, 2020, A hybrid chaos firefly algorithm for three-dimensional irregular packing problem, J. Ind. Manag. Optim., 16, 409, 10.3934/jimo.2018160
Shahdoosti, 2020, Object-based feature extraction for hyperspectral data using firefly algorithm, Int. J. Mach. Learn. Cybern., 11, 1277, 10.1007/s13042-019-01038-w
O. Abedinia, N. Amjady, M.S. Naderi, 2012. Multi-objective environmental/economic dispatch using firefly technique, in: 2012 11th International Conference on Environment and Electrical Engineering, pp 461–466.
M.H. Sulaiman, M.W. Mustafa, A. Azmi, O. Aliman, Rahim S.R. Abdul, Optimal allocation and sizing of distributed generation in distribution system via firefly algorithm, in: 2012 IEEE International Power Engineering and Optimization Conference, Melaka, Malaysia, 2012, pp. 84–89.
Farahani, 2011, A multiswarm based firefly algorithm in dynamic environments, vol. 3, 68
Abshouri, 2011, New firefly algorithm based on multi swarm & learning automata in dynamic environments, IEEE Proc., 13, 989
Wang, 2012, A modified firefly algorithm for ucav path planning, Int. J. Hybrid Inf. Technol., 5, 123
Liu, 2015, Three-dimensional path planning method for autonomous underwater vehicle based on modified firefly algorithm, Math. Probl. Eng.
Wang, 2016, A modified firefly algorithm based on light intensity difference, J. Comb. Optim., 31, 1045, 10.1007/s10878-014-9809-y
Meena, 2015, Modified approach of firefly algorithm for non-minimum phase systems, Indian J. Sci. Technol., 8, 1, 10.17485/ijst/2015/v8i23/72264
Abdel-Basset, 2020, Chaotic firefly algorithm for solving definite integral, Int. J. Inf. Technol. Comput. Sci., 6, 19
Yelghi, 2018, A modified firefly algorithm for global minimum optimization, Appl. Soft Comput., 62, 29, 10.1016/j.asoc.2017.10.032
Baykasoğlu, 2015, Adaptive firefly algorithm with chaos for mechanical design optimization problems, Appl. Soft Comput., 36, 152, 10.1016/j.asoc.2015.06.056
Zhou, 2019, Centroid opposition with a two-point full crossover for the partially attracted firefly algorithm, Soft Comput., 23, 12241, 10.1007/s00500-019-04221-x
Wang, 2017, Firefly algorithm with adaptive control parameters, Soft Comput., 21, 5091, 10.1007/s00500-016-2104-3
Aydilek, 2018, A hybrid firefly and particle swarm optimization algorithm for computationally expensive numerical problems, Appl. Soft Comput., 66, 232, 10.1016/j.asoc.2018.02.025
Hassan, 2021, CSCF: A chaotic sine cosine firefly algorithm for practical application problems, Neural Comput. Appl., 33, 7011, 10.1007/s00521-020-05474-6
Tighzert, 2019, Towards compact swarm intelligence: A new compact firefly optimisation technique, Int. J. Comput. Appl. Technol., 60, 108, 10.1504/IJCAT.2019.100137
Peng, 2021, Enhancing firefly algorithm with courtship learning, Inform. Sci., 543, 18, 10.1016/j.ins.2020.05.111
chuan Wang, 2020, Yin-yang firefly algorithm based on dimensionally Cauchy mutation, Expert Syst. Appl., 150
Farahani, 2012, Some hybrid models to improve firefly algorithm performance, Int. J. Artif. Intell., 8, 97
Liang, 2013, 490
Tighzert, 2018, A set of new compact firefly algorithms, Swarm Evol. Comput., 40, 92, 10.1016/j.swevo.2017.12.006
Yang, 2010, Firefly algorithm, Levy flights and global optimization, 209
Coello, 2000, Use of a self-adaptive penalty approach for engineering optimization problems, Comput. Ind., 41, 113, 10.1016/S0166-3615(99)00046-9
Faramarzi, 2020, Equilibrium optimizer: A novel optimization algorithm, Knowl.-Based Syst., 191, 10.1016/j.knosys.2019.105190
H.S. Bernardino, H.J.C. Barbosa, A.C.C. Lemonge, A hybrid genetic algorithm for constrained optimization problems in mechanical engineering, in: 2007 IEEE Congress on Evolutionary Computation, 2007, pp. 646–653.
Aragón, 2010, A modified version of a T-cell algorithm for constrained optimization problems, Internat. J. Numer. Methods Engrg., 84, 351, 10.1002/nme.2904
Coello Coello, 2004, Efficient evolutionary optimization through the use of a cultural algorithm, Eng. Optim., 36, 219, 10.1080/03052150410001647966
dos Santos Coelho, 2010, Gaussian quantum-behaved particle swarm optimization approaches for constrained engineering design problems, Expert Syst. Appl., 37, 1676, 10.1016/j.eswa.2009.06.044
Ray, 2001, Engineering design optimization using a swarm with an intelligent information sharing among individuals, Eng. Optim., 33, 735, 10.1080/03052150108940941
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
Huang, 2007, An effective co-evolutionary differential evolution for constrained optimization, Appl. Math. Comput., 186, 340, 10.1016/j.amc.2006.07.105
Meng, 2019, An adaptive reinforcement learning-based bat algorithm for structural design problems, Int. J. Bio-Inspired Comput., 14, 114, 10.1504/IJBIC.2019.101639
Mezura-Montes, 2008
He, 2007, A hybrid particle swarm optimization with a feasibility-based rule for constrained optimization, Appl. Math. Comput., 186, 1407, 10.1016/j.amc.2006.07.134
Ghafil, 2020, Dynamic differential annealed optimization: New metaheuristic optimization algorithm for engineering applications, Appl. Soft Comput., 93, 10.1016/j.asoc.2020.106392
Coello, 2002, Constraint-handling in genetic algorithms through the use of dominance-based tournament selection, Adv. Eng. Inform., 16, 193, 10.1016/S1474-0346(02)00011-3
Ray, 2003, Society and civilization: An optimization algorithm based on the simulation of social behavior, IEEE Trans. Evol. Comput., 7, 386, 10.1109/TEVC.2003.814902
Zhao, 2019, Supply–demand-based optimization: A novel economics-inspired algorithm for global optimization, IEEE Access, 7, 73182, 10.1109/ACCESS.2019.2918753
Mazhoud, 2013, Particle swarm optimization for solving engineering problems: A new constraint-handling mechanism, Eng. Appl. Artif. Intell., 26, 1263, 10.1016/j.engappai.2013.02.002
Montemurro, 2013, The automatic dynamic penalisation method (ADP) for handling constraints with genetic algorithms, Comput. Methods Appl. Mech. Engrg., 256, 70, 10.1016/j.cma.2012.12.009
Gupta, 2021, A partition cum unification based genetic-firefly algorithm for single objective optimization, Sādhanā, 46, 1, 10.1007/s12046-021-01641-0
Gupta, 2020, A memory-based grey wolf optimizer for global optimization tasks, Appl. Soft Comput., 93, 10.1016/j.asoc.2020.106367
Gandomi, 2013, Bat algorithm for constrained optimization tasks, Neural Comput. Appl., 22, 1239, 10.1007/s00521-012-1028-9
Yapici, 2019, A new meta-heuristic optimizer: Pathfinder algorithm, Appl. Soft Comput., 78, 545, 10.1016/j.asoc.2019.03.012
Song, 2021, Dimension decided Harris Hawks optimization with Gaussian mutation: Balance analysis and diversity patterns, Knowl.-Based Syst., 215, 10.1016/j.knosys.2020.106425
E. Mezura-Montes, C.A.C. Coello, Useful infeasible solutions in engineering optimization with evolutionary algorithms, in: Mexican International Conference on Artificial Intelligence, 2005, pp. 652–662.
Rezaei, 2022, An enhanced grey wolf optimizer with a velocity-aided global search mechanism, Mathematics, 10, 351, 10.3390/math10030351
K.E. Parsopoulos, M.N. Vrahatis, Unified particle swarm optimization for solving constrained engineering optimization problems, in: International Conference on Natural Computation, 2005, pp. 582–591.
Kassoul, 2022, Exponential particle swarm optimization for global optimization, IEEE Access, 10, 78320, 10.1109/ACCESS.2022.3193396
Kaveh, 2017, A novel meta-heuristic optimization algorithm: Thermal exchange optimization, Adv. Eng. Softw., 110, 69, 10.1016/j.advengsoft.2017.03.014
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
Liu, 2010, Hybridizing particle swarm optimization with differential evolution for constrained numerical and engineering optimization, Appl. Soft Comput., 10, 629, 10.1016/j.asoc.2009.08.031
Gandomi, 2013, Cuckoo search algorithm: A metaheuristic approach to solve structural optimization problems, Eng. Comput., 29, 17, 10.1007/s00366-011-0241-y
Ayyarao, 2022, War strategy optimization algorithm: A new effective metaheuristic algorithm for global optimization, IEEE Access, 10, 25073, 10.1109/ACCESS.2022.3153493
Wang, 2009, Constrained optimization based on hybrid evolutionary algorithm and adaptive constraint-handling technique, Struct. Multidiscip. Optim., 37, 395, 10.1007/s00158-008-0238-3
Zhang, 2022, Differential evolution-assisted salp swarm algorithm with chaotic structure for real-world problems, Eng. Comput., 1, 10.1007/s00366-022-01609-6
Singh, 2022, Mutation-driven grey wolf optimizer with modified search mechanism, Expert Syst. Appl., 194, 10.1016/j.eswa.2021.116450
Cheng, 2014, Symbiotic organisms search: A new metaheuristic optimization algorithm, Comput. Struct., 139, 98, 10.1016/j.compstruc.2014.03.007
Liu, 2020, Multipopulation ensemble particle swarm optimizer for engineering design problems, Math. Probl. Eng., 2020
Gupta, 2019, A hybrid self-adaptive sine cosine algorithm with opposition based learning, Expert Syst. Appl., 119, 210, 10.1016/j.eswa.2018.10.050
Zhao, 2022, Artificial hummingbird algorithm: A new bio-inspired optimizer with its engineering applications, Comput. Methods Appl. Mech. Engrg., 388, 10.1016/j.cma.2021.114194
Ngo, 2016, A cooperative particle swarm optimizer with stochastic movements for computationally expensive numerical optimization problems, J. Comput. Sci., 13, 68, 10.1016/j.jocs.2016.01.004
Akhtar, 2002, A socio-behavioural simulation model for engineering design optimization, Eng. Optim., 34, 341, 10.1080/03052150212723
Hedar, 2006, Derivative-free filter simulated annealing method for constrained continuous global optimization, J. Global Optim., 35, 521, 10.1007/s10898-005-3693-z
He, 2004, An improved particle swarm optimizer for mechanical design optimization problems, Eng. Optim., 36, 585, 10.1080/03052150410001704854
Zhang, 2009, An effective multiagent evolutionary algorithm integrating a novel roulette inversion operator for engineering optimization, Appl. Math. Comput., 211, 392, 10.1016/j.amc.2009.01.048
Hwang, 2006, A hybrid real-parameter genetic algorithm for function optimization, Adv. Eng. Inform., 20, 7, 10.1016/j.aei.2005.09.001
Zhao, 2020, Manta ray foraging optimization: An effective bioinspired optimizer for engineering applications, Eng. Appl. Artif. Intell., 87, 10.1016/j.engappai.2019.103300
Braik, 2022, A novel meta-heuristic algorithm for solving numerical optimization problems: Ali Baba and the forty thieves, Neural Comput. Appl., 34, 409, 10.1007/s00521-021-06392-x
Zhao, 2022, Opposition-based ant colony optimization with all-dimension neighborhood search for engineering design, J. Comput. Des. Eng., 9, 1007
Mirrashid, 2022, Transit search: An optimization algorithm based on exoplanet exploration, Res. Control Optim., 7
Savsani, 2016, Passing vehicle search (PVS): A novel metaheuristic algorithm, Appl. Math. Model., 40, 3951, 10.1016/j.apm.2015.10.040
Mezura-Montes, 2008, An empirical study about the usefulness of evolution strategies to solve constrained optimization problems, Int. J. Gen. Syst., 37, 443, 10.1080/03081070701303470
Coello, 2004, Hybridizing a genetic algorithm with an artificial immune system for global optimization, Eng. Optim., 36, 607, 10.1080/03052150410001704845
Brajevic, 2015, Crossover-based artificial bee colony algorithm for constrained optimization problems, Neural Comput. Appl., 26, 1587, 10.1007/s00521-015-1826-y
H.S. Bernardino, H.J.C. Barbosa, A.C.C. Lemonge, L.G. Fonseca, A new hybrid AIS-GA for constrained optimization problems in mechanical engineering, in: 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence), 2008, pp. 1455–1462.
Akay, 2012, Artificial bee colony algorithm for large-scale problems and engineering design optimization, J. Intell. Manuf., 23, 1001, 10.1007/s10845-010-0393-4
Brammya, 2019, Deer hunting optimization algorithm: A new nature-inspired meta-heuristic paradigm, Comput. J., 10.1093/comjnl/bxy133
Chun, 2013, A diversity-enhanced constrained particle swarm optimizer for mixed integer-discrete-continuous engineering design problems, Adv. Mech. Eng., 5, 10.1155/2013/130750
Sadollah, 2013, Mine blast algorithm: A new population based algorithm for solving constrained engineering optimization problems, Appl. Soft Comput., 13, 2592, 10.1016/j.asoc.2012.11.026
Hussien, 2022, A self-adaptive Harris Hawks optimization algorithm with opposition-based learning and chaotic local search strategy for global optimization and feature selection, Int. J. Mach. Learn. Cybern., 13, 309, 10.1007/s13042-021-01326-4