A survey on new generation metaheuristic algorithms
Tóm tắt
Từ khóa
Tài liệu tham khảo
Abdel-Basset, 2018, A hybrid whale optimization algorithm based on local search strategy for the permutation flow shop scheduling problem, Future Generation Computer Systems, 85, 129, 10.1016/j.future.2018.03.020
Abedinia, 2016, A new metaheuristic algorithm based on shark smell optimization, Complexity, 21, 97, 10.1002/cplx.21634
Agrawal, 2012, Bacterial foraging optimization: A survey, 227
Al-Betar, 2012, Novel selection schemes for harmony search, Applied Mathematics and Computation, 218, 6095, 10.1016/j.amc.2011.11.095
Alatas, 2010, Chaotic harmony search algorithms, Applied Mathematics and Computation, 216, 2687, 10.1016/j.amc.2010.03.114
Alatas, 2011, Acroa: Artificial chemical reaction optimization algorithm for global optimization, Expert Systems with Applications, 38, 13170, 10.1016/j.eswa.2011.04.126
Alba, 2005, Vol. 47
Alba, 2013, Parallel metaheuristics: Recent advances and new trends, International Transactions in Operational Research, 20, 1, 10.1111/j.1475-3995.2012.00862.x
Alba, 1999, A survey of parallel distributed genetic algorithms, Complexity, 4, 31, 10.1002/(SICI)1099-0526(199903/04)4:4<31::AID-CPLX5>3.0.CO;2-4
Aljarah, 2018, Optimizing connection weights in neural networks using the whale optimization algorithm, Soft Computing, 22, 1, 10.1007/s00500-016-2442-1
Amini, 2018, Object-based classification of hyperspectral data using random forest algorithm, Geo-spatial Information Science, 21, 127, 10.1080/10095020.2017.1399674
Askarzadeh, 2016, A novel metaheuristic method for solving constrained engineering optimization problems: crow search algorithm, Computers & Structures, 169, 1, 10.1016/j.compstruc.2016.03.001
Au, 2003, Automatic gain control in the echolocation system of dolphins, Nature, 423, 861, 10.1038/nature01727
Awasthi, 2019, A goal-oriented approach based on fuzzy axiomatic design for sustainable mobility project selection, International Journal of Systems Science: Operations & Logistics, 6, 86
Baghel, 2012, Survey of metaheuristic algorithms for combinatorial optimization, International Journal of Computer Applications, 58, 10.5120/9391-3813
Banzhaf, 1998, Vol. 1
Bartz-Beielstein, 2010
Basturk, 2006, An artificial bee colony (abc) algorithm for numeric function optimization
Basu, 2013, Cuckoo search algorithm for economic dispatch, Energy, 60, 99, 10.1016/j.energy.2013.07.011
Beyaz, 2015, Robust hyper-heuristic algorithms for the offline oriented/non-oriented 2d bin packing problems, Applied Soft Computing, 36, 236, 10.1016/j.asoc.2015.06.063
Bhandari, 2014, Cuckoo search algorithm and wind driven optimization based study of satellite image segmentation for multilevel thresholding using Kapur’s entropy, Expert Systems with Applications, 41, 3538, 10.1016/j.eswa.2013.10.059
Bhattacharya, 2010, Hybrid differential evolution with biogeography-based optimization for solution of economic load dispatch, IEEE Transactions on Power Systems, 25, 1955, 10.1109/TPWRS.2010.2043270
Bianchi, L., Dorigo, M., Gambardella, L. M., & Gutjahr, W. J. (2006). Metaheuristics in stochastic combinatorial optimization: A survey. TechReport: Dalle Molle Institute for Artificial Intelligence.
Bianchi, 2009, A survey on metaheuristics for stochastic combinatorial optimization, Natural Computing, 8, 239, 10.1007/s11047-008-9098-4
Binitha, 2012, A survey of bio inspired optimization algorithms, International Journal of Soft Computing and Engineering, 2, 137
Birattari, 2009, Vol. 197
Blum, 2010, A brief survey on hybrid metaheuristics, Proceedings of BIOMA, 3
Blum, 2011, Hybrid metaheuristics in combinatorial optimization: A survey, Applied Soft Computing, 11, 4135, 10.1016/j.asoc.2011.02.032
Bolaji, 2016, A comprehensive review: Krill herd algorithm (kh) and its applications, Applied Soft Computing, 49, 437, 10.1016/j.asoc.2016.08.041
BoussaïD, 2013, A survey on optimization metaheuristics, Information Sciences, 237, 82, 10.1016/j.ins.2013.02.041
Burke, 2013, Hyper-heuristics: A survey of the state of the art, Journal of the Operational Research Society, 64, 1695, 10.1057/jors.2013.71
Burke, E. K., Hyde, M., Kendall, G., Ochoa, G., Ozcan, E., & Qu, R. (2009). A survey of hyper-heuristics. Computer Science Technical Report No. NOTTCS-TR-SUB-0906241418-2747, School of Computer Science and Information Technology, University of Nottingham.
Burke, 2010, A classification of hyper-heuristic approaches, 449
Burke, 2003, A tabu-search hyperheuristic for timetabling and rostering, Journal of Heuristics, 9, 451, 10.1023/B:HEUR.0000012446.94732.b6
Cahon, 2004, Paradiseo: A framework for the reusable design of parallel and distributed metaheuristics, Journal of Heuristics, 10, 357, 10.1023/B:HEUR.0000026900.92269.ec
Camacho-Villalón, 2019, The intelligent water drops algorithm: Why it cannot be considered a novel algorithm, Swarm Intelligence, 1
Cantú-Paz, 1998, A survey of parallel genetic algorithms, Calculateurs paralleles, reseaux et systems repartis, 10, 141
Chakhlevitch, 2008, Hyperheuristics: Recent developments, 3
Chandrasekaran, 2012, Multi-objective scheduling problem: Hybrid approach using fuzzy assisted cuckoo search algorithm, Swarm and Evolutionary Computation, 5, 1, 10.1016/j.swevo.2012.01.001
Chen, 2017, A novel bacterial foraging optimization algorithm for feature selection, Expert Systems with Applications, 83, 1, 10.1016/j.eswa.2017.04.019
Cheng, 2014, Symbiotic organisms search: A new metaheuristic optimization algorithm, Computers & Structures, 139, 98, 10.1016/j.compstruc.2014.03.007
Cheng, 2015, Optimizing multiple-resources leveling in multiple projects using discrete symbiotic organisms search, Journal of Computing in Civil Engineering, 30, 04015036, 10.1061/(ASCE)CP.1943-5487.0000512
Cheng, 2016, Brain storm optimization algorithm: A review, Artificial Intelligence Review, 46, 445, 10.1007/s10462-016-9471-0
Chiarandini, M., Paquete, L., Preuss, M., & Ridge, E. (2007). Experiments on metaheuristics: Methodological overview and open issues.
Cinar, 2018, Similarity and logic gate-based tree-seed algorithms for binary optimization, Computers & Industrial Engineering, 115, 631, 10.1016/j.cie.2017.12.009
Cowling, 2000, A hyperheuristic approach to scheduling a sales summit, 176
Črepinšek, 2012, A note on teaching–learning-based optimization algorithm, Information Sciences, 212, 79, 10.1016/j.ins.2012.05.009
Cuevas, 2014, A new algorithm inspired in the behavior of the social-spider for constrained optimization, Expert Systems with Applications, 41, 412, 10.1016/j.eswa.2013.07.067
Cuevas, 2013, A swarm optimization algorithm inspired in the behavior of the social-spider, Expert Systems with Applications, 40, 6374, 10.1016/j.eswa.2013.05.041
Dai, 2009, Seeker optimization algorithm for optimal reactive power dispatch, IEEE Transactions on Power Systems, 24, 1218, 10.1109/TPWRS.2009.2021226
Damaševičius, 2017, State flipping based hyper-heuristic for hybridization of nature inspired algorithms, 337
Das, 2009, Bacterial foraging optimization algorithm: Theoretical foundations, analysis, and applications, Vol. 3, 23
Dasgupta, 2012
Dasgupta, 2013
Dasgupta, 2009, Adaptive computational chemotaxis in bacterial foraging optimization: An analysis, IEEE Transactions on Evolutionary Computation, 13, 919, 10.1109/TEVC.2009.2021982
de Castro, 2003, Artificial immune systems as a novel soft computing paradigm, Soft Computing, 7, 526, 10.1007/s00500-002-0237-z
Dede, 2015, Combined size and shape optimization of structures with a new meta-heuristic algorithm, Applied Soft Computing, 28, 250, 10.1016/j.asoc.2014.12.007
Del Ser, 2019, Bio-inspired computation: Where we stand and what’s next, Swarm and Evolutionary Computation, 10.1016/j.swevo.2019.04.008
Doğan, 2015, A new metaheuristic for numerical function optimization: Vortex search algorithm, Information Sciences, 293, 125, 10.1016/j.ins.2014.08.053
Dokeroglu, 2015, Hybrid teaching–learning-based optimization algorithms for the quadratic assignment problem, Computers & Industrial Engineering, 85, 86, 10.1016/j.cie.2015.03.001
Dokeroglu, 2016, A novel multistart hyper-heuristic algorithm on the grid for the quadratic assignment problem, Engineering Applications of Artificial Intelligence, 52, 10, 10.1016/j.engappai.2016.02.004
Dokeroglu, 2019, Artificial bee colony optimization for the quadratic assignment problem, Applied Soft Computing, 76, 595, 10.1016/j.asoc.2019.01.001
Dorigo, 2010
Dorigo, 2005, Ant colony optimization theory: A survey, Theoretical Computer Science, 344, 243, 10.1016/j.tcs.2005.05.020
Duan, 2018, Selective maintenance scheduling under stochastic maintenance quality with multiple maintenance actions, International Journal of Production Research, 56, 7160, 10.1080/00207543.2018.1436789
Dubey, 2015, Building theory of sustainable manufacturing using total interpretive structural modelling, International Journal of Systems Science: Operations & Logistics, 2, 231
Duman, 2012, Migrating birds optimization: A new metaheuristic approach and its performance on quadratic assignment problem, Information Sciences, 217, 65, 10.1016/j.ins.2012.06.032
Duman, 2012, Optimal power flow using gravitational search algorithm, Energy Conversion and Management, 59, 86, 10.1016/j.enconman.2012.02.024
Durgun, 2012, Structural design optimization of vehicle components using cuckoo search algorithm, Materials Testing, 54, 185, 10.3139/120.110317
e Silva, 2012, Multiobjective biogeography-based optimization based on predator-prey approach, IEEE Transactions on Magnetics, 48, 951, 10.1109/TMAG.2011.2174205
Eita, 2014, Group counseling optimization, Applied Soft Computing, 22, 585, 10.1016/j.asoc.2014.03.043
El Aziz, 2017, Whale optimization algorithm and moth-flame optimization for multilevel thresholding image segmentation, Expert Systems with Applications, 83, 242, 10.1016/j.eswa.2017.04.023
El Aziz, 2018, Multi-objective whale optimization algorithm for multilevel thresholding segmentation, 23
El-Bages, 2017, Social spider algorithm for solving the transmission expansion planning problem, Electric Power Systems Research, 143, 235, 10.1016/j.epsr.2016.09.002
Elaziz, 2019, A hyper-heuristic for improving the initial population of whale optimization algorithm, Knowledge-Based Systems, 172, 42, 10.1016/j.knosys.2019.02.010
Elsayed, 2016, Modified social spider algorithm for solving the economic dispatch problem, Engineering Science and Technology, An International Journal, 19, 1672, 10.1016/j.jestch.2016.09.002
Emary, 2016, Binary grey wolf optimization approaches for feature selection, Neurocomputing, 172, 371, 10.1016/j.neucom.2015.06.083
Ergezer, 2009, Oppositional biogeography-based optimization, 1009
Erol, 2006, A new optimization method: Big bang–big crunch, Advances in Engineering Software, 37, 106, 10.1016/j.advengsoft.2005.04.005
Eskandar, 2012, Water cycle algorithm–a novel metaheuristic optimization method for solving constrained engineering optimization problems, Computers & Structures, 110, 151, 10.1016/j.compstruc.2012.07.010
Espejo, 2010, A survey on the application of genetic programming to classification, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 40, 121, 10.1109/TSMCC.2009.2033566
Eusuff, 2003, Optimization of water distribution network design using the shuffled frog leaping algorithm, Journal of Water Resources Planning and Management, 129, 210, 10.1061/(ASCE)0733-9496(2003)129:3(210)
Ezugwu, 2017, Simulated annealing based symbiotic organisms search optimization algorithm for traveling salesman problem, Expert Systems with Applications, 77, 189, 10.1016/j.eswa.2017.01.053
Farahani, 2011, A gaussian firefly algorithm, International Journal of Machine Learning and Computing, 1, 448, 10.7763/IJMLC.2011.V1.67
Faris, 2018, An efficient binary salp swarm algorithm with crossover scheme for feature selection problems, Knowledge-Based Systems, 154, 43, 10.1016/j.knosys.2018.05.009
Fister, 2013, A comprehensive review of firefly algorithms, Swarm and Evolutionary Computation, 13, 34, 10.1016/j.swevo.2013.06.001
Fister Jr, I., Fister, D., & Yang, X.-S. (2013). A hybrid bat algorithm. arXiv preprint arXiv: 1303.6310.
Fister Jr, I., Yang, X.-S., Fister, I., ’& Brest, J. (2012). Memetic firefly algorithm for combinatorial optimization. arXiv preprint arXiv: 1204.5165.
Formato, 2007, Central force optimization, Progress in Electromagnetics Research, 77, 425, 10.2528/PIER07082403
Gandomi, 2014, Interior search algorithm (isa): A novel approach for global optimization, ISA Transactions, 53, 1168, 10.1016/j.isatra.2014.03.018
Gandomi, 2012, Krill herd: a new bio-inspired optimization algorithm, Communications in Nonlinear Science and Numerical Simulation, 17, 4831, 10.1016/j.cnsns.2012.05.010
Gandomi, 2014, Chaotic bat algorithm, Journal of Computational Science, 5, 224, 10.1016/j.jocs.2013.10.002
Gandomi, 2011, Mixed variable structural optimization using firefly algorithm, Computers & Structures, 89, 2325, 10.1016/j.compstruc.2011.08.002
Gandomi, 2013, Cuckoo search algorithm: A metaheuristic approach to solve structural optimization problems, Engineering with Computers, 29, 17, 10.1007/s00366-011-0241-y
Gandomi, 2013, Bat algorithm for constrained optimization tasks, Neural Computing and Applications, 22, 1239, 10.1007/s00521-012-1028-9
Gandomi, 2013, Firefly algorithm with chaos, Communications in Nonlinear Science and Numerical Simulation, 18, 89, 10.1016/j.cnsns.2012.06.009
Gao, 2012, A modified artificial bee colony algorithm, Computers & Operations Research, 39, 687, 10.1016/j.cor.2011.06.007
Geem, 2006, Optimal cost design of water distribution networks using harmony search, Engineering Optimization, 38, 259, 10.1080/03052150500467430
Geem, 2001, A new heuristic optimization algorithm: Harmony search, Simulation, 76, 60, 10.1177/003754970107600201
Gharaei, 2019, Modelling and optimal lot-sizing of the replenishments in constrained, multi-product and bi-objective epq models with defective products: Generalised cross decomposition, International Journal of Systems Science: Operations &, Logistics, 1
Gharaei, 2019, Joint economic lot-sizing in multi-product multi-level integrated supply chains: Generalized benders decomposition, International Journal of Systems Science: Operations & Logistics, 1
Gharaei, 2019, An integrated multi-product, multi-buyer supply chain under penalty, green, and quality control polices and a vendor managed inventory with consignment stock agreement: The outer approximation with equality relaxation and augmented penalty algorithm, Applied Mathematical Modelling, 69, 223, 10.1016/j.apm.2018.11.035
Ghorbani, 2014, Exchange market algorithm, Applied Soft Computing, 19, 177, 10.1016/j.asoc.2014.02.006
Giri, 2014, Coordinating a supply chain with backup supplier through buyback contract under supply disruption and uncertain demand, International Journal of Systems Science: Operations & Logistics, 1, 193
Giri, 2018, Developing a closed-loop supply chain model with price and quality dependent demand and learning in production in a stochastic environment, International Journal of Systems Science: Operations & Logistics, 1
Glover, 1998, Tabu search, 2093
Goldberg, 1989, Genetic algorithms in search, Optimization, and MachineLearning
Goldberg, 1988, Genetic algorithms and machine learning, Machine Learning, 3, 95, 10.1023/A:1022602019183
Goli, 2019, A multi-objective invasive weed optimization algorithm for robust aggregate production planning under uncertain seasonal demand, Computing, 101, 499, 10.1007/s00607-018-00692-2
Gonçalves, 2015, Search group algorithm: A new metaheuristic method for the optimization of truss structures, Computers & Structures, 153, 165, 10.1016/j.compstruc.2015.03.003
Gong, 2010, A real-coded biogeography-based optimization with mutation, Applied Mathematics and Computation, 216, 2749, 10.1016/j.amc.2010.03.123
Guo, 2014, A new improved krill herd algorithm for global numerical optimization, Neurocomputing, 138, 392, 10.1016/j.neucom.2014.01.023
Gupta, 2019, Hybrid grey wolf optimizer with mutation operator, 961
Hancer, 2018, Pareto front feature selection based on artificial bee colony optimization, Information Sciences, 422, 462, 10.1016/j.ins.2017.09.028
Hao, 2018, Virtual factory system design and implementation: Integrated sustainable manufacturing, International Journal of Systems Science: Operations & Logistics, 5, 116
Hardy, 1935, The plankton of the south Georgia whaling grounds and adjacent waters, 1926–1932, Discovery Rep., 11, 1
Hassanzadeh, 2010, A multi-objective gravitational search algorithm, 7
Hatamlou, 2013, Black hole: A new heuristic optimization approach for data clustering, Information Sciences, 222, 175, 10.1016/j.ins.2012.08.023
Hatamlou, 2012, A combined approach for clustering based on k-means and gravitational search algorithms, Swarm and Evolutionary Computation, 6, 47, 10.1016/j.swevo.2012.02.003
He, 2006, A novel group search optimizer inspired by animal behavioural ecology, 1272
Ho, 2002, Simple explanation of the no-free-lunch theorem and its implications, Journal of Optimization Theory and Applications, 115, 549, 10.1023/A:1021251113462
Hoseini Shekarabi, 2019, Modelling and optimal lot-sizing of integrated multi-level multi-wholesaler supply chains under the shortage and limited warehouse space: Generalised outer approximation, International Journal of Systems Science: Operations & Logistics, 6, 237
Hota, 2010, Economic emission load dispatch through fuzzy based bacterial foraging algorithm, International Journal of Electrical Power & Energy Systems, 32, 794, 10.1016/j.ijepes.2010.01.016
Jadhav, 2018, Wgc: Hybridization of exponential grey wolf optimizer with whale optimization for data clustering, Alexandria Engineering Journal, 57, 1569, 10.1016/j.aej.2017.04.013
Jain, 2019, A novel nature-inspired algorithm for optimization: Squirrel search algorithm, Swarm and Evolutionary Computation, 44, 148, 10.1016/j.swevo.2018.02.013
James, 2015, A social spider algorithm for global optimization, Applied Soft Computing, 30, 614, 10.1016/j.asoc.2015.02.014
James, 2016, A social spider algorithm for solving the non-convex economic load dispatch problem, Neurocomputing, 171, 955, 10.1016/j.neucom.2015.07.037
Jati, 2011, Evolutionary discrete firefly algorithm for travelling salesman problem, 393
Karaboga, D. (2005). An idea based on honey bee swarm for numerical optimization. Tech. rep. Technical report-tr06, Erciyes university, engineering faculty, computer.
Karaboga, 2007, A powerful and efficient algorithm for numerical function optimization: artificial bee colony (abc) algorithm, Journal of Global Optimization, 39, 459, 10.1007/s10898-007-9149-x
Karaboga, 2008, On the performance of artificial bee colony (abc) algorithm, Applied Soft Computing, 8, 687, 10.1016/j.asoc.2007.05.007
Karaboga, 2014, A comprehensive survey: Artificial bee colony (abc) algorithm and applications, Artificial Intelligence Review, 42, 21, 10.1007/s10462-012-9328-0
Karaboga, 2011, A novel clustering approach: Artificial bee colony (abc) algorithm, Applied Soft Computing, 11, 652, 10.1016/j.asoc.2009.12.025
Karaboga, 2009, A new design method based on artificial bee colony algorithm for digital iir filters, Journal of the Franklin Institute, 346, 328, 10.1016/j.jfranklin.2008.11.003
Kashan, 2015, A new metaheuristic for optimization: optics inspired optimization (oio), Computers & Operations Research, 55, 99, 10.1016/j.cor.2014.10.011
Kaur, 2018, Chaotic whale optimization algorithm, Journal of Computational Design and Engineering, 5, 275, 10.1016/j.jcde.2017.12.006
Kaveh, 2016, A new metaheuristic for continuous structural optimization: Water evaporation optimization, Structural and Multidisciplinary Optimization, 54, 23, 10.1007/s00158-015-1396-8
Kaveh, 2017, A novel meta-heuristic optimization algorithm: Thermal exchange optimization, Advances in Engineering Software, 110, 69, 10.1016/j.advengsoft.2017.03.014
Kaveh, 2013, A new optimization method: Dolphin echolocation, Advances in Engineering Software, 59, 53, 10.1016/j.advengsoft.2013.03.004
Kaveh, 2014, Enhanced colliding bodies optimization for design problems with continuous and discrete variables, Advances in Engineering Software, 77, 66, 10.1016/j.advengsoft.2014.08.003
Kaveh, 2017, A new meta-heuristic algorithm: Vibrating particles system, Scientia Iranica. Transaction A, Civil Engineering, 24, 551
Kaveh, 2012, A new meta-heuristic method: Ray optimization, Computers & Structures, 112, 283, 10.1016/j.compstruc.2012.09.003
Kaveh, 2014, Colliding bodies optimization: A novel meta-heuristic method, Computers & Structures, 139, 18, 10.1016/j.compstruc.2014.04.005
Kaveh, 2010, A novel heuristic optimization method: Charged system search, Acta Mechanica, 213, 267, 10.1007/s00707-009-0270-4
Kavitha, 2018, An efficient social spider optimization for flexible job shop scheduling problem, Journal of Advanced Manufacturing Systems, 17, 181, 10.1142/S0219686718500117
Kazemi, 2018, Economic order quantity models for items with imperfect quality and emission considerations, International Journal of Systems Science: Operations & Logistics, 5, 99
Khan, 2012, A comparison of ba, ga, pso, bp and lm for training feed forward neural networks in e-learning context, International Journal of Intelligent Systems and Applications, 4, 23, 10.5815/ijisa.2012.07.03
Kim, 2007, A hybrid genetic algorithm and bacterial foraging approach for global optimization, Information Sciences, 177, 3918, 10.1016/j.ins.2007.04.002
Kiziloz, 2018, Novel multiobjective tlbo algorithms for the feature subset selection problem, Neurocomputing, 306, 94, 10.1016/j.neucom.2018.04.020
Kohli, 2018, Chaotic grey wolf optimization algorithm for constrained optimization problems, Journal of Computational Design and Engineering, 5, 458, 10.1016/j.jcde.2017.02.005
Komaki, 2015, Grey wolf optimizer algorithm for the two-stage assembly flow shop scheduling problem with release time, Journal of Computational Science, 8, 109, 10.1016/j.jocs.2015.03.011
Krishnanand, 2009, Glowworm swarm optimization for simultaneous capture of multiple local optima of multimodal functions, Swarm Intelligence, 3, 87, 10.1007/s11721-008-0021-5
Ks, 2017, Memory based hybrid dragonfly algorithm for numerical optimization problems, Expert Systems with Applications, 83, 63, 10.1016/j.eswa.2017.04.033
Kumar, 2014, Parameter adaptive harmony search algorithm for unimodal and multimodal optimization problems, Journal of Computational Science, 5, 144, 10.1016/j.jocs.2013.12.001
Kurdi, M. (2018). A social spider optimization algorithm for hybrid flow shop scheduling with multiprocessor task. Available at SSRN 3301792.
Lam, 2010, Chemical-reaction-inspired metaheuristic for optimization, IEEE Transactions on Evolutionary Computation, 14, 381, 10.1109/TEVC.2009.2033580
Lee, 2004, A new structural optimization method based on the harmony search algorithm, Computers & Structures, 82, 781, 10.1016/j.compstruc.2004.01.002
Lee, 2005, A new meta-heuristic algorithm for continuous engineering optimization: Harmony search theory and practice, Computer Methods in Applied Mechanics and Engineering, 194, 3902, 10.1016/j.cma.2004.09.007
Lee, 2005, The harmony search heuristic algorithm for discrete structural optimization, Engineering Optimization, 37, 663, 10.1080/03052150500211895
Lewis, 2008, A survey of metaheuristic-based techniques for university timetabling problems, OR Spectrum, 30, 167, 10.1007/s00291-007-0097-0
Li, 1997, Chaos optimization method and its application, Control Theory & Applications, 4
Li, 2011, Parameters identification of hydraulic turbine governing system using improved gravitational search algorithm, Energy Conversion and Management, 52, 374, 10.1016/j.enconman.2010.07.012
Li, 2002, An optimizing method based on autonomous animats: Fish-swarm algorithm, Systems Engineering-Theory & Practice, 22, 32
Ling, 2017, Lévy flight trajectory-based whale optimization algorithm for global optimization, IEEE Access, 5, 6168, 10.1109/ACCESS.2017.2695498
Lourenço, 2003, Iterated local search, 320
Ma, 2010, An analysis of the equilibrium of migration models for biogeography-based optimization, Information Sciences, 180, 3444, 10.1016/j.ins.2010.05.035
Ma, 2011, Blended biogeography-based optimization for constrained optimization, Engineering Applications of Artificial Intelligence, 24, 517, 10.1016/j.engappai.2010.08.005
Mafarja, 2018, Evolutionary population dynamics and grasshopper optimization approaches for feature selection problems, Knowledge-Based Systems, 145, 25, 10.1016/j.knosys.2017.12.037
Mafarja, 2018, Whale optimization approaches for wrapper feature selection, Applied Soft Computing, 62, 441, 10.1016/j.asoc.2017.11.006
Mafarja, 2017, Hybrid whale optimization algorithm with simulated annealing for feature selection, Neurocomputing, 260, 302, 10.1016/j.neucom.2017.04.053
Majumder, 2018, A hybrid cuckoo search algorithm in parallel batch processing machines with unequal job ready times, Computers & Industrial Engineering, 124, 65, 10.1016/j.cie.2018.07.001
Manjarres, 2013, A survey on applications of the harmony search algorithm, Engineering Applications of Artificial Intelligence, 26, 1818, 10.1016/j.engappai.2013.05.008
Marques-Silva, 1999, Grasp: A search algorithm for propositional satisfiability, IEEE Transactions on Computers, 48, 506, 10.1109/12.769433
Martí, 2006, Principles of scatter search, european Journal of operational Research, 169, 359, 10.1016/j.ejor.2004.08.004
Martin, 2006, o: A swarm intelligent routing algorithm for mobilewireless ad-hoc networks, 155
McGeoch, 2001, Experimental analysis of algorithms, Notices of the AMS, 48, 304
Meng, 2014, A new bio-inspired algorithm: Chicken swarm optimization, 86
Meng, 2016, A new bio-inspired optimisation algorithm: Bird swarm algorithm, Journal of Experimental & Theoretical Artificial Intelligence, 28, 673, 10.1080/0952813X.2015.1042530
Mirjalili, 2015, The ant lion optimizer, Advances in Engineering Software, 83, 80, 10.1016/j.advengsoft.2015.01.010
Mirjalili, 2015, Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm, Knowledge-Based Systems, 89, 228, 10.1016/j.knosys.2015.07.006
Mirjalili, 2016, Dragonfly algorithm: A new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems, Neural Computing and Applications, 27, 1053, 10.1007/s00521-015-1920-1
Mirjalili, 2016, Sca: A sine cosine algorithm for solving optimization problems, Knowledge-Based Systems, 96, 120, 10.1016/j.knosys.2015.12.022
Mirjalili, 2017, Salp swarm algorithm: A bio-inspired optimizer for engineering design problems, Advances in Engineering Software, 114, 163, 10.1016/j.advengsoft.2017.07.002
Mirjalili, 2012, Training feedforward neural networks using hybrid particle swarm optimization and gravitational search algorithm, Applied Mathematics and Computation, 218, 11125, 10.1016/j.amc.2012.04.069
Mirjalili, 2016, The whale optimization algorithm, Advances in Engineering Software, 95, 51, 10.1016/j.advengsoft.2016.01.008
Mirjalili, 2016, Multi-verse optimizer: A nature-inspired algorithm for global optimization, Neural Computing and Applications, 27, 495, 10.1007/s00521-015-1870-7
Mirjalili, 2014, Grey wolf optimizer, Advances in Engineering Software, 69, 46, 10.1016/j.advengsoft.2013.12.007
Mirjalili, 2014, Binary bat algorithm, Neural Computing and Applications, 25, 663, 10.1007/s00521-013-1525-5
Mirjalili, 2016, Multi-objective grey wolf optimizer: A novel algorithm for multi-criterion optimization, Expert Systems with Applications, 47, 106, 10.1016/j.eswa.2015.10.039
Mittal, 2016, Modified grey wolf optimizer for global engineering optimization, Applied Computational Intelligence and Soft Computing, 2016, 8, 10.1155/2016/7950348
Mladenović, 2007, The p-median problem: A survey of metaheuristic approaches, European Journal of Operational Research, 179, 927, 10.1016/j.ejor.2005.05.034
Mladenović, 1997, Variable neighborhood search, Computers & Operations Research, 24, 1097, 10.1016/S0305-0548(97)00031-2
Moghaddam, F. F., Moghaddam, R. F., & Cheriet, M. (2012). Curved space optimization: a random search based on general relativity theory. arXiv preprint arXiv: 1208.2214.
Moghdani, 2018, Volleyball premier league algorithm, Applied Soft Computing, 64, 161, 10.1016/j.asoc.2017.11.043
Mohan, 2012, A survey: Ant colony optimization based recent research and implementation on several engineering domain, Expert Systems with Applications, 39, 4618, 10.1016/j.eswa.2011.09.076
Moosavian, 2014, Soccer league competition algorithm, a new method for solving systems of nonlinear equations, International Journal of Intelligence Science, 4, 7, 10.4236/ijis.2014.41002
Mucherino, 2007, Monkey search: A novel metaheuristic search for global optimization, Vol. 953, 162
Mühlenbein, 1992, Parallel genetic algorithms in combinatorial optimization, 441
Muthiah-Nakarajan, 2016, Galactic swarm optimization: A new global optimization metaheuristic inspired by galactic motion, Applied Soft Computing, 38, 771, 10.1016/j.asoc.2015.10.034
Nakamura, 2012, Bba: A binary bat algorithm for feature selection, 291
Nanda, 2014, A survey on nature inspired metaheuristic algorithms for partitional clustering, Swarm and Evolutionary Computation, 16, 1, 10.1016/j.swevo.2013.11.003
Neshat, 2014, Artificial fish swarm algorithm: A survey of the state-of-the-art, hybridization, combinatorial and indicative applications, Artificial Intelligence Review, 42, 965, 10.1007/s10462-012-9342-2
Neumann, 2010, Combinatorial optimization and computational complexity, 9
Niroomand, 2015, Modified migrating birds optimization algorithm for closed loop layout with exact distances in flexible manufacturing systems, Expert Systems with Applications, 42, 6586, 10.1016/j.eswa.2015.04.040
Oftadeh, 2010, A novel meta-heuristic optimization algorithm inspired by group hunting of animals: Hunting search, Computers & Mathematics with Applications, 60, 2087, 10.1016/j.camwa.2010.07.049
Oliva, 2017, Parameter estimation of photovoltaic cells using an improved chaotic whale optimization algorithm, Applied Energy, 200, 141, 10.1016/j.apenergy.2017.05.029
Omkar, 2011, Artificial bee colony (abc) for multi-objective design optimization of composite structures, Applied Soft Computing, 11, 489, 10.1016/j.asoc.2009.12.008
Omran, 2008, Global-best harmony search, Applied Mathematics and Computation, 198, 643, 10.1016/j.amc.2007.09.004
Ouaarab, 2014, Discrete cuckoo search algorithm for the travelling salesman problem, Neural Computing and Applications, 24, 1659, 10.1007/s00521-013-1402-2
Pan, 2012, A new fruit fly optimization algorithm: Taking the financial distress model as an example, Knowledge-Based Systems, 26, 69, 10.1016/j.knosys.2011.07.001
Panda, 2016, A symbiotic organisms search algorithm with adaptive penalty function to solve multi-objective constrained optimization problems, Applied Soft Computing, 46, 344, 10.1016/j.asoc.2016.04.030
Parejo, 2012, Metaheuristic optimization frameworks: A survey and benchmarking, Soft Computing, 16, 527, 10.1007/s00500-011-0754-8
Passino, 2002, Biomimicry of bacterial foraging for distributed optimization and control, IEEE Control Systems Magazine, 22, 52, 10.1109/MCS.2002.1004010
Passino, 2010, Bacterial foraging optimization, International Journal of Swarm Intelligence Research (IJSIR), 1, 1, 10.4018/jsir.2010010101
Pedemonte, 2011, A survey on parallel ant colony optimization, Applied Soft Computing, 11, 5181, 10.1016/j.asoc.2011.05.042
Pereira, 2016, Social-spider optimization-based support vector machines applied for energy theft detection, Computers & Electrical Engineering, 49, 25, 10.1016/j.compeleceng.2015.11.001
Pinto, 2007, Wasp swarm algorithm for dynamic max-sat problems, 350
Prakash, 2017, Optimal siting of capacitors in radial distribution network using whale optimization algorithm, Alexandria Engineering Journal, 56, 499, 10.1016/j.aej.2016.10.002
Prasad, 2016, A novel symbiotic organisms search algorithm for optimal power flow of power system with facts devices, Engineering Science and Technology, An International Journal, 19, 79, 10.1016/j.jestch.2015.06.005
Puchinger, 2005, Combining metaheuristics and exact algorithms in combinatorial optimization: A survey and classification, 41
Qin, 2019, An effective hybrid discrete grey wolf optimizer for the casting production scheduling problem with multi-objective and multi-constraint, Computers & Industrial Engineering, 128, 458, 10.1016/j.cie.2018.12.061
Rabbani, 2019, Sustainable supplier selection by a new decision model based on interval-valued fuzzy sets and possibilistic statistical reference point systems under uncertainty, International Journal of Systems Science: Operations & Logistics, 6, 162
Rabbani, 2018, A hybrid robust possibilistic approach for a sustainable supply chain location-allocation network design, International Journal of Systems Science: Operations & Logistics, 1
Rajabioun, 2011, Cuckoo optimization algorithm, Applied Soft Computing, 11, 5508, 10.1016/j.asoc.2011.05.008
Ramezani, 2013, Social-based algorithm (sba), Applied Soft Computing, 13, 2837, 10.1016/j.asoc.2012.05.018
Rao, 2012, An elitist teaching-learning-based optimization algorithm for solving complex constrained optimization problems, International Journal of Industrial Engineering Computations, 3, 535, 10.5267/j.ijiec.2012.03.007
Rao, 2016, Teaching-learning-based optimization algorithm, 9
Rao, 2013, Multi-objective optimization of heat exchangers using a modified teaching-learning-based optimization algorithm, Applied Mathematical Modelling, 37, 1147, 10.1016/j.apm.2012.03.043
Rao, 2011, Teaching–learning-based optimization: A novel method for constrained mechanical design optimization problems, Computer-Aided Design, 43, 303, 10.1016/j.cad.2010.12.015
Rao, 2012, Teaching-learning-based optimization: An optimization method for continuous non-linear large scale problems, Information Sciences, 183, 1, 10.1016/j.ins.2011.08.006
Rashedi, 2009, Gsa: A gravitational search algorithm, Information Sciences, 179, 2232, 10.1016/j.ins.2009.03.004
Rashedi, 2010, Bgsa: Binary gravitational search algorithm, Natural Computing, 9, 727, 10.1007/s11047-009-9175-3
Rashedi, 2011, Filter modeling using gravitational search algorithm, Engineering Applications of Artificial Intelligence, 24, 117, 10.1016/j.engappai.2010.05.007
Rashedi, 2018, A comprehensive survey on gravitational search algorithm, Swarm and Evolutionary Computation, 41, 141, 10.1016/j.swevo.2018.02.018
Sabri, 2013, A review of gravitational search algorithm, International Journal of Advances in Soft Computing & its Applications, 5, 1
Sadollah, 2013, Mine blast algorithm: A new population based algorithm for solving constrained engineering optimization problems, Applied Soft Computing, 13, 2592, 10.1016/j.asoc.2012.11.026
Salimi, 2015, Stochastic fractal search: A powerful metaheuristic algorithm, Knowledge-Based Systems, 75, 1, 10.1016/j.knosys.2014.07.025
Saremi, 2017, Grasshopper optimisation algorithm: Theory and application, Advances in Engineering Software, 105, 30, 10.1016/j.advengsoft.2017.01.004
Sarkar, 2018, Stochastic supply chain model with imperfect production and controllable defective rate, International Journal of Systems Science: Operations & Logistics, 1
Sayed, 2019, Feature selection via a novel chaotic crow search algorithm, Neural Computing and Applications, 31, 171, 10.1007/s00521-017-2988-6
Sayyadi, 2018, An integrated approach based on system dynamics and anp for evaluating sustainable transportation policies, International Journal of Systems Science: Operations & Logistics, 1
Sayyadi, 2018, A simulation-based optimisation approach for identifying key determinants for sustainable transportation planning, International Journal of Systems Science: Operations & Logistics, 5, 161
Schaffer, 1992, Combinations of genetic algorithms and neural networks: A survey of the state of the art, 1
Senthilnath, 2011, Clustering using firefly algorithm: Performance study, Swarm and Evolutionary Computation, 1, 164, 10.1016/j.swevo.2011.06.003
Sevinc, 2019, A novel hybrid teaching-learning-based optimization algorithm for the classification of data by using extreme learning machines, Turkish Journal of Electrical Engineering & Computer Sciences, 27, 1523, 10.3906/elk-1802-40
Shabani, 2017, Selective refining harmony search: A new optimization algorithm, Expert Systems with Applications, 81, 423, 10.1016/j.eswa.2017.03.044
Shah, 2018, Integrating credit and replenishment policies for deteriorating items under quadratic demand in a three echelon supply chain, International Journal of Systems Science: Operations & Logistics, 1
Sharafi, 2016, Cooa: Competitive optimization algorithm, Swarm and Evolutionary Computation, 30, 39, 10.1016/j.swevo.2016.04.002
Shehab, 2017, A survey on applications and variants of the cuckoo search algorithm, Applied Soft Computing, 61, 1041, 10.1016/j.asoc.2017.02.034
Shen, 2011, Forecasting stock indices using radial basis function neural networks optimized by artificial fish swarm algorithm, Knowledge-Based Systems, 24, 378, 10.1016/j.knosys.2010.11.001
Shiqin, 2009, A dolphin partner optimization, Vol. 1, 124
Simon, 2008, Biogeography-based optimization, IEEE Transactions on Evolutionary Computation, 12, 702, 10.1109/TEVC.2008.919004
Simon, 2011, Markov models for biogeography-based optimization, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 41, 299, 10.1109/TSMCB.2010.2051149
Singh, 2009, An artificial bee colony algorithm for the leaf-constrained minimum spanning tree problem, Applied Soft Computing, 9, 625, 10.1016/j.asoc.2008.09.001
Song, 2015, Grey wolf optimizer for parameter estimation in surface waves, Soil Dynamics and Earthquake Engineering, 75, 147, 10.1016/j.soildyn.2015.04.004
Sörensen, 2015, Metaheuristics-the metaphor exposed, International Transactions in Operational Research, 22, 3, 10.1111/itor.12001
Sörensen, 2018, A history of metaheuristics, Handbook of Heuristics, 1
Storn, 1997, Differential evolution–A simple and efficient heuristic for global optimization over continuous spaces, Journal of Global Optimization, 11, 341, 10.1023/A:1008202821328
Taillard, 2001, Adaptive memory programming: A unified view of metaheuristics, European Journal of Operational Research, 135, 1, 10.1016/S0377-2217(00)00268-X
Talbi, 2009, Vol. 74
Tamura, 2011, Spiral dynamics inspired optimization, Journal of Advanced Computational Intelligence and Intelligent Informatics, 15, 1116, 10.20965/jaciii.2011.p1116
Tang, 2006, Bacterial foraging algorithm for dynamic environments, 1324
Tawhid, 2017, A hybrid grey wolf optimizer and genetic algorithm for minimizing potential energy function, Memetic Computing, 9, 347, 10.1007/s12293-017-0234-5
Tejani, 2018, Multiobjective adaptive symbiotic organisms search for truss optimization problems, Knowledge-based Systems, 161, 398, 10.1016/j.knosys.2018.08.005
Tejani, 2016, Adaptive symbiotic organisms search (sos) algorithm for structural design optimization, Journal of Computational Design and Engineering, 3, 226, 10.1016/j.jcde.2016.02.003
Tilahun, 2015, Prey-predator algorithm: A new metaheuristic algorithm for optimization problems, International Journal of Information Technology & Decision Making, 14, 1331, 10.1142/S021962201450031X
Tirkolaee, 2019, Multi-objective multi-mode resource constrained project scheduling problem using pareto-based algorithms, Computing, 101, 547, 10.1007/s00607-018-00693-1
Toğan, 2012, Design of planar steel frames using teaching–learning based optimization, Engineering Structures, 34, 225, 10.1016/j.engstruct.2011.08.035
Tran, 2016, A novel multiple objective symbiotic organisms search (mosos) for time–cost–labor utilization tradeoff problem, Knowledge-Based Systems, 94, 132, 10.1016/j.knosys.2015.11.016
TSai, 2009, Enhanced artificial bee colony optimization, International Journal of Innovative Computing, Information and Control, 5, 5081
Tsao, 2015, Design of a carbon-efficient supply-chain network under trade credits, International Journal of Systems Science: Operations & Logistics, 2, 177
Tuba, 2011, Modified cuckoo search algorithm for unconstrained optimization problems, 263
Valian, 2011, Improved cuckoo search algorithm for global optimization, International Journal of Communications and Information Technology, 1, 31
Van Laarhoven, 1987, Simulated annealing, 7
Vincent, 2017, Symbiotic organisms search and two solution representations for solving the capacitated vehicle routing problem, Applied Soft Computing, 52, 657, 10.1016/j.asoc.2016.10.006
Walton, 2011, Modified cuckoo search: A new gradient free optimisation algorithm, Chaos, Solitons & Fractals, 44, 710, 10.1016/j.chaos.2011.06.004
Wang, 2010, Self-adaptive harmony search algorithm for optimization, Expert Systems with Applications, 37, 2826, 10.1016/j.eswa.2009.09.008
Wang, 2013, A novel hybrid bat algorithm with harmony search for global numerical optimization, Journal of Applied Mathematics, 2013
Wang, 2013, Lévy-flight krill herd algorithm, Mathematical Problems in Engineering, 2013
Wang, 2014, Incorporating mutation scheme into krill herd algorithm for global numerical optimization, Neural Computing and Applications, 24, 853, 10.1007/s00521-012-1304-8
Wang, 2018, Moth search algorithm: A bio-inspired metaheuristic algorithm for global optimization problems, Memetic Computing, 1
Wang, 2015, Earthworm optimization algorithm: A bio-inspired metaheuristic algorithm for global optimization problems, International Journal of Bio-Inspired Computation, 7, 1, 10.1504/IJBIC.2015.10004283
Wang, 2016, A new metaheuristic optimisation algorithm motivated by elephant herding behaviour, International Journal of Bio-Inspired Computation, 8, 394, 10.1504/IJBIC.2016.081335
Wang, 2014, Hybrid krill herd algorithm with differential evolution for global numerical optimization, Neural Computing and Applications, 25, 297, 10.1007/s00521-013-1485-9
Wang, 2016, A new hybrid method based on krill herd and cuckoo search for global optimisation tasks, International Journal of Bio-Inspired Computation, 8, 286, 10.1504/IJBIC.2016.079569
Wang, 2016, Hybridizing harmony search algorithm with cuckoo search for global numerical optimization, Soft Computing, 20, 273, 10.1007/s00500-014-1502-7
Wang, 2013, A chaotic particle-swarm krill herd algorithm for global numerical optimization, Kybernetes, 42, 962, 10.1108/K-11-2012-0108
Wang, 2017, Randomly attracted firefly algorithm with neighborhood search and dynamic parameter adjustment mechanism, Soft Computing, 21, 5325, 10.1007/s00500-016-2116-z
Wang, 2017, Firefly algorithm with neighborhood attraction, Information Sciences, 382, 374, 10.1016/j.ins.2016.12.024
Wang, 2017, A novel hybrid system based on a new proposed algorithm-multi-objective whale optimization algorithm for wind speed forecasting, Applied Energy, 208, 344, 10.1016/j.apenergy.2017.10.031
Wang, 2018, Robust optimization for volume variation in timber processing, Journal of Forestry Research, 29, 247, 10.1007/s11676-017-0416-5
Wang, 2015
Webster, B., & Bernhard, P. J. (2003). A local search optimization algorithm based on natural principles of gravitation. Tech. rep.
Wei, 2004, Survey on particle swarm optimization algorithm, Engineering Science, 5, 87
Wolpert, 1997, No free lunch theorems for optimization, IEEE Transactions on Evolutionary Computation, 1, 67, 10.1109/4235.585893
Wu, 2015, Improved chicken swarm optimization, 681
Yang, 2009, Harmony search as a metaheuristic algorithm, 1
Yang, X.-S. (2010a). Firefly algorithm, stochastic test functions and design optimisation. arXiv preprint arXiv: 1003.1409.
Yang, 2010
Yang, 2010, A new metaheuristic bat-inspired algorithm, 65
Yang, X.-S. (2012a). Bat algorithm for multi-objective optimisation. arXiv preprint arXiv: 1203.6571.
Yang, 2012, Flower pollination algorithm for global optimization, 240
Yang, X.-S. (2013a). Bat algorithm: literature review and applications. arXiv preprint arXiv: 1308.3900.
Yang, 2013, Vol. 516
Yang, 2013, Multiobjective firefly algorithm for continuous optimization, Engineering with Computers, 29, 175, 10.1007/s00366-012-0254-1
Yang, 2009, Cuckoo search via lévy flights, 210
Yang, X.-S., & Deb, S. (2010). Engineering optimisation by cuckoo search. arXiv preprint arXiv: 1005.2908.
Yang, 2014, Cuckoo search: Recent advances and applications, Neural Computing and Applications, 24, 169, 10.1007/s00521-013-1367-1
Yang, X.-S., & He, X. (2013). Firefly algorithm: recent advances and applications. arXiv preprint arXiv: 1308.3898.
Yang, 2012, Bat algorithm: A novel approach for global engineering optimization, Engineering Computations, 29, 464, 10.1108/02644401211235834
Yang, 2012, Firefly algorithm for solving non-convex economic dispatch problems with valve loading effect, Applied Soft Computing, 12, 1180, 10.1016/j.asoc.2011.09.017
Yazdani, 2016, Lion optimization algorithm (loa): A nature-inspired metaheuristic algorithm, Journal of Computational Design and Engineering, 3, 24, 10.1016/j.jcde.2015.06.003
Yildiz, 2013, Cuckoo search algorithm for the selection of optimal machining parameters in milling operations, The International Journal of Advanced Manufacturing Technology, 64, 55, 10.1007/s00170-012-4013-7
Yılmaz, 2015, A new modification approach on bat algorithm for solving optimization problems, Applied Soft Computing, 28, 259, 10.1016/j.asoc.2014.11.029
Yin, 2016, A game theoretic model for coordination of single manufacturer and multiple suppliers with quality variations under uncertain demands, International Journal of Systems Science: Operations & Logistics, 3, 79
Zavala, 2014, A survey of multi-objective metaheuristics applied to structural optimization, Structural and Multidisciplinary Optimization, 49, 537, 10.1007/s00158-013-0996-4
Zheng, 2015, Water wave optimization: A new nature-inspired metaheuristic, Computers & Operations Research, 55, 1, 10.1016/j.cor.2014.10.008
Zhou, 2017, A simplex method-based social spider optimization algorithm for clustering analysis, Engineering Applications of Artificial Intelligence, 64, 67, 10.1016/j.engappai.2017.06.004
Zhu, 2010, Gbest-guided artificial bee colony algorithm for numerical function optimization, Applied Mathematics and Computation, 217, 3166, 10.1016/j.amc.2010.08.049
Zou, 2010, Novel global harmony search algorithm for unconstrained problems, Neurocomputing, 73, 3308, 10.1016/j.neucom.2010.07.010
Zou, 2019, A survey of teaching–learning-based optimization, Neurocomputing, 335, 366, 10.1016/j.neucom.2018.06.076