Modified Teaching–Learning-Based Optimization algorithm for global numerical optimization—A comparative study
Teaching–Learning-Based Optimization (TLBO) is recently being used as a new, reliable, accurate and robust optimization technique for global optimization over continuous spaces. Few variants of TLBO have been proposed by researchers to improve the performance of the basic TLBO algorithm. In this pap...
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| Published in: | Swarm and evolutionary computation Vol. 16; pp. 28 - 37 |
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| Format: | Journal Article |
| Language: | English |
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Elsevier B.V
01.06.2014
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| ISSN: | 2210-6502 |
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| Abstract | Teaching–Learning-Based Optimization (TLBO) is recently being used as a new, reliable, accurate and robust optimization technique for global optimization over continuous spaces. Few variants of TLBO have been proposed by researchers to improve the performance of the basic TLBO algorithm. In this paper the authors investigate the performance of a new variant of TLBO called modified TLBO (mTLBO) for global function optimization problems. The performance of mTLBO is compared with the state-of-the art forms of Particle Swarm Optimization (PSO), Differential Evolution (DE) and Artificial Bee Colony (ABC) algorithms. Several advanced variants of PSO, DE and ABC are considered for the comparison purpose. The suite of benchmark functions are chosen from the competition and special session on real parameter optimization under IEEE Congress on Evolutionary Computation (CEC) 2005. Statistical hypothesis testing is undertaken to demonstrate the significance of mTLBO over other investigated algorithms. Finally, the paper investigates the data clustering performance of mTLBO over other evolutionary algorithms on a few standard synthetic and artificial datasets. Results of our work reveal that mTLBO performs better than many other algorithms investigated in this work. |
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| AbstractList | Teaching–Learning-Based Optimization (TLBO) is recently being used as a new, reliable, accurate and robust optimization technique for global optimization over continuous spaces. Few variants of TLBO have been proposed by researchers to improve the performance of the basic TLBO algorithm. In this paper the authors investigate the performance of a new variant of TLBO called modified TLBO (mTLBO) for global function optimization problems. The performance of mTLBO is compared with the state-of-the art forms of Particle Swarm Optimization (PSO), Differential Evolution (DE) and Artificial Bee Colony (ABC) algorithms. Several advanced variants of PSO, DE and ABC are considered for the comparison purpose. The suite of benchmark functions are chosen from the competition and special session on real parameter optimization under IEEE Congress on Evolutionary Computation (CEC) 2005. Statistical hypothesis testing is undertaken to demonstrate the significance of mTLBO over other investigated algorithms. Finally, the paper investigates the data clustering performance of mTLBO over other evolutionary algorithms on a few standard synthetic and artificial datasets. Results of our work reveal that mTLBO performs better than many other algorithms investigated in this work. |
| Author | Satapathy, Suresh Chandra Naik, Anima |
| Author_xml | – sequence: 1 givenname: Suresh Chandra surname: Satapathy fullname: Satapathy, Suresh Chandra email: sureshsatapathy@ieee.org organization: Anil Neerukonda Institute of Technology and Sciences, Vishakapatnam, India – sequence: 2 givenname: Anima surname: Naik fullname: Naik, Anima email: animanaik@gmail.com organization: Majhighariani Institute of Technology and Science (MITS), Rayagada, India |
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| Cites_doi | 10.1016/j.engstruct.2011.08.035 10.2174/2213275911306010008 10.1080/10426914.2011.602792 10.1109/TEVC.2004.826074 10.1016/j.asoc.2009.11.032 10.1016/j.cad.2010.12.015 10.1016/j.ipl.2011.06.002 10.1109/TSMCB.2011.2167966 10.1109/TEVC.2006.872133 10.1109/TEVC.2008.927706 10.1109/TSMCB.2009.2015956 10.1109/ICNN.1995.488968 10.1016/j.eswa.2010.02.042 10.1504/IJAISC.2013.053401 10.2478/s13537-013-0102-4 10.1016/j.plrev.2005.10.001 10.1109/TEVC.2010.2052054 10.1109/TEVC.2005.857610 10.1109/TEVC.2010.2087271 10.1080/0305215X.2011.652103 10.1109/TEVC.2009.2014613 10.1016/j.cma.2004.09.007 10.1080/0305215X.2011.624183 10.1016/j.ins.2011.04.024 10.1016/j.ejor.2006.06.043 10.1016/j.asoc.2007.05.007 10.1109/CEC.2005.1554902 10.1016/j.asoc.2010.04.024 10.1016/j.ins.2011.08.006 10.4236/am.2013.43064 10.1016/j.amc.2010.08.049 10.1109/TEVC.2004.826071 |
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| References | Rao, Patel (bib14) 2012; 44 Satapathy, Naik, Parvathi (bib22) 2013; 2 Rao, Savsani, Vakharia (bib12) 2011; 43 Lee, Geem (bib10) 2004; 194 Garcia-Martinez, Lozano, Herrera, Molina, Sanchez (bib41) 2008; 185 Mendes, Kennedy, Neves (bib42) 2004; 8 V. Kennedy, R. Eberhart, Particle swarm optimization, in: Proceedings of the IEEE International Conference on Neural Networks, 1995, pp. 1942–1948. Clerc (bib4) 2006 Rao, Savsani, Balic (bib13) 2012; 44 Qin, Huang, Suganthan (bib37) 2009; 13 P.N. Suganthan, N. Hansen, J.J. Liang, K. Deb, Y.-P. Chen, A. Auger, S. Tiwari, Problem definitions and evaluation criteria for the CEC2005 special session on real-parameter optimization. Nanyang Techical. Univ., Singapore, IIT Kanpur, Kanpur, India, Tech. Rep. KanGAL#2005, May 2005. Zhan, Zhang, Li, Shi (bib28) 2011; 15 Alatas (bib32) 2010; 37 Dorigo, Stutzle (bib8) 2004 Goldberg (bib1) 1989 Ahrari, Atai (bib11) 2010; 10 Ratnaweera, Halgamuge, Watson (bib27) 2004; 8 Kang, Li, Ma (bib34) 2011; 12 Brest, Greiner, Boskovic, Mernik, Zumer (bib36) 2006; 10 D. Karaboga, An idea based on honey bee swarm for numerical optimization, Technical Report-TR06, Erciyes University, Engineering Faculty, Computer Engineering Department, 2005. Satapathy, Naik, Parvathi (bib21) 2013; 4 Back (bib2) 1996 B. Basturk, D. Karaboga, An Artificial Bee Colony (ABC) algorithm for numeric function optimization, in: Proceedings of the IEEE Swarm Intelligence Symposium 2006, Indianapolis, Indiana, USA, May 12–14, 2006. Satapathy, Naik (bib15) 2011 Mallipeddi, Suganthan, Pan, Tasgetiren (bib30) 2011; 11 Minhazul Islam, Das, Ghosh, Roy, Suganthan (bib38) 2012; 42 Zhu, Kwong (bib33) 2010; 217 Wang, Cai, Zhang (bib29) 2011; 15 Liang, Qin, Suganthan, Baskar (bib39) 2006; 10 Auger, N. Hansen, A restart CMA evolution strategy with increasing population size, in Proceedings of the IEEE CEC, Sep. 2005, pp. 1769–1776. Satapathy, Naik, Parvathi (bib20) 2013; 3 Rao, Kalyankar (bib17) 2012; 27 Karaboga, Basturk (bib7) 2008; 8 Rao, Savsani, Vakharia (bib18) 2012; 183 Toğan (bib16) 2012; 34 Zhan, Zhang, Li, Chung (bib26) 2009; 39 Satapathy, Naik (bib23) 2013; 6 Blum (bib9) 2005; 2 Satapathy, Naik, Parvathi (bib19) 2013; 3 Gao, Liu (bib35) 2011; 111 Swagatam Das, Amit Konar, Uday K. Chakraborty, Two improved differential evolution schemes for faster global search, GECCO′05, June 25–29, 2005, Washington, DC, USA, 2005, ACM 1-59593-010-8/05/0006. Zhang, Sanderson (bib31) 2009; 13 Rao (10.1016/j.swevo.2013.12.005_bib14) 2012; 44 Satapathy (10.1016/j.swevo.2013.12.005_bib23) 2013; 6 Karaboga (10.1016/j.swevo.2013.12.005_bib7) 2008; 8 Brest (10.1016/j.swevo.2013.12.005_bib36) 2006; 10 Minhazul Islam (10.1016/j.swevo.2013.12.005_bib38) 2012; 42 Toğan (10.1016/j.swevo.2013.12.005_bib16) 2012; 34 Liang (10.1016/j.swevo.2013.12.005_bib39) 2006; 10 Qin (10.1016/j.swevo.2013.12.005_bib37) 2009; 13 Goldberg (10.1016/j.swevo.2013.12.005_bib1) 1989 Satapathy (10.1016/j.swevo.2013.12.005_bib21) 2013; 4 10.1016/j.swevo.2013.12.005_bib5 10.1016/j.swevo.2013.12.005_bib3 Wang (10.1016/j.swevo.2013.12.005_bib29) 2011; 15 Lee (10.1016/j.swevo.2013.12.005_bib10) 2004; 194 Gao (10.1016/j.swevo.2013.12.005_bib35) 2011; 111 Blum (10.1016/j.swevo.2013.12.005_bib9) 2005; 2 Zhang (10.1016/j.swevo.2013.12.005_bib31) 2009; 13 Rao (10.1016/j.swevo.2013.12.005_bib17) 2012; 27 Rao (10.1016/j.swevo.2013.12.005_bib18) 2012; 183 Clerc (10.1016/j.swevo.2013.12.005_bib4) 2006 10.1016/j.swevo.2013.12.005_bib6 10.1016/j.swevo.2013.12.005_bib25 10.1016/j.swevo.2013.12.005_bib24 Satapathy (10.1016/j.swevo.2013.12.005_bib19) 2013; 3 Zhan (10.1016/j.swevo.2013.12.005_bib28) 2011; 15 Ahrari (10.1016/j.swevo.2013.12.005_bib11) 2010; 10 Rao (10.1016/j.swevo.2013.12.005_bib13) 2012; 44 Back (10.1016/j.swevo.2013.12.005_bib2) 1996 10.1016/j.swevo.2013.12.005_bib40 Alatas (10.1016/j.swevo.2013.12.005_bib32) 2010; 37 Garcia-Martinez (10.1016/j.swevo.2013.12.005_bib41) 2008; 185 Ratnaweera (10.1016/j.swevo.2013.12.005_bib27) 2004; 8 Dorigo (10.1016/j.swevo.2013.12.005_bib8) 2004 Satapathy (10.1016/j.swevo.2013.12.005_bib15) 2011 Satapathy (10.1016/j.swevo.2013.12.005_bib22) 2013; 2 Rao (10.1016/j.swevo.2013.12.005_bib12) 2011; 43 Kang (10.1016/j.swevo.2013.12.005_bib34) 2011; 12 Mallipeddi (10.1016/j.swevo.2013.12.005_bib30) 2011; 11 Zhu (10.1016/j.swevo.2013.12.005_bib33) 2010; 217 Zhan (10.1016/j.swevo.2013.12.005_bib26) 2009; 39 Mendes (10.1016/j.swevo.2013.12.005_bib42) 2004; 8 Satapathy (10.1016/j.swevo.2013.12.005_bib20) 2013; 3 |
| References_xml | – volume: 13 start-page: 398 year: 2009 end-page: 417 ident: bib37 article-title: Differential evolution algorithm with strategy adaptation for global numerical optimization publication-title: IEEE Trans. Evol. Comput. – reference: Swagatam Das, Amit Konar, Uday K. Chakraborty, Two improved differential evolution schemes for faster global search, GECCO′05, June 25–29, 2005, Washington, DC, USA, 2005, ACM 1-59593-010-8/05/0006. – volume: 15 start-page: 832 year: 2011 end-page: 847 ident: bib28 article-title: Orthogonal learning particle swarm optimization publication-title: IEEE Trans. Evol. Comput. – volume: 8 start-page: 240 year: 2004 end-page: 255 ident: bib27 article-title: Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients publication-title: IEEE Trans. Evol. Comput. – year: 1996 ident: bib2 article-title: Evolutionary Algorithms in Theory and Practice: Evolution Strategies, Evolutionary Programming, Genetic Algorithms – volume: 194 start-page: 3902 year: 2004 end-page: 3933 ident: bib10 article-title: A new meta-heuristic algorithm for continuous engineering optimization: harmony search theory and practice publication-title: Comput. Methods Appl. Mech. Eng. – start-page: 148 year: 2011 end-page: 156 ident: bib15 publication-title: Data Clustering using Teaching Learning based Optimization, SEMCCO 2011, Part-II, LNCS 7077 – volume: 11 start-page: 1679 year: 2011 end-page: 1996 ident: bib30 article-title: Differential evolution algorithm with ensemble of parameters and mutation strategies publication-title: Appl. Soft Comput. – volume: 8 start-page: 204 year: 2004 end-page: 210 ident: bib42 article-title: The fully informed particle swarm: simpler, maybe better publication-title: IEEE Trans. Evol. Comput. – volume: 34 start-page: 225 year: 2012 end-page: 232 ident: bib16 article-title: Design of planar steel frames using Teaching–Learning Based Optimization publication-title: Eng. Struct. – volume: 10 start-page: 281 year: 2006 end-page: 295 ident: bib39 article-title: Comprehensive learning particle swarm optimizer for global optimization of multimodal functions publication-title: IEEE Trans. Evolut. Comput. – year: 2004 ident: bib8 article-title: Ant Colony Optimization – volume: 10 start-page: 646 year: 2006 end-page: 657 ident: bib36 article-title: Self adapting control parameters in differential evolution: a comparative study on numerical benchmark problems publication-title: IEEE Trans. Evol. Comput. – reference: B. Basturk, D. Karaboga, An Artificial Bee Colony (ABC) algorithm for numeric function optimization, in: Proceedings of the IEEE Swarm Intelligence Symposium 2006, Indianapolis, Indiana, USA, May 12–14, 2006. – volume: 2 start-page: 1 year: 2013 end-page: 12 ident: bib22 article-title: A teaching–learning based optimization based on orthogonal design for solving global optimization problems publication-title: Springer Plus – reference: Auger, N. Hansen, A restart CMA evolution strategy with increasing population size, in Proceedings of the IEEE CEC, Sep. 2005, pp. 1769–1776. – reference: V. Kennedy, R. Eberhart, Particle swarm optimization, in: Proceedings of the IEEE International Conference on Neural Networks, 1995, pp. 1942–1948. – year: 2006 ident: bib4 article-title: Particle Swarm Optimization – volume: 27 start-page: 978 year: 2012 end-page: 985 ident: bib17 article-title: Parameter optimization of machining processes using a new optimization algorithm publication-title: Mater. Manuf. Processes – volume: 3 start-page: 27 year: 2013 end-page: 42 ident: bib20 article-title: Rough set and teaching learning based optimization technique for optimal feature selection publication-title: Central Eur. J. Comput. Sci. – volume: 217 start-page: 3166 year: 2010 end-page: 3173 ident: bib33 article-title: Gbest-guided artificial bee colony algorithm for numerical function optimization publication-title: Appl. Math. Comput. – volume: 111 start-page: 871 year: 2011 end-page: 882 ident: bib35 article-title: Improved artificial bee colony algorithm for global optimization publication-title: Inf. Process. Lett. – reference: D. Karaboga, An idea based on honey bee swarm for numerical optimization, Technical Report-TR06, Erciyes University, Engineering Faculty, Computer Engineering Department, 2005. – volume: 185 start-page: 1088 year: 2008 end-page: 1113 ident: bib41 article-title: Global and local real-coded genetic algorithms based on parent-centric crossover operators publication-title: Eur. J. Oper. Res. – volume: 4 start-page: 429 year: 2013 end-page: 439 ident: bib21 article-title: Weighted teaching–learning based optimization for global function optimization publication-title: Appl. Math. – volume: 15 start-page: 55 year: 2011 end-page: 66 ident: bib29 article-title: Differential evolution with composite trial vector generation strategies and control parameters publication-title: IEEE Trans. Evol. Comput. – reference: P.N. Suganthan, N. Hansen, J.J. Liang, K. Deb, Y.-P. Chen, A. Auger, S. Tiwari, Problem definitions and evaluation criteria for the CEC2005 special session on real-parameter optimization. Nanyang Techical. Univ., Singapore, IIT Kanpur, Kanpur, India, Tech. Rep. KanGAL#2005, May 2005. – volume: 3 start-page: 244 year: 2013 end-page: 256 ident: bib19 article-title: Unsupervised feature selection using roughest and teaching-based optimization publication-title: Int. J. Artif. Intell. Soft Comput. – volume: 13 start-page: 945 year: 2009 end-page: 958 ident: bib31 article-title: JADE: adaptive differential evolution with optional external archive publication-title: IEEE Trans. Evol. Comput. – volume: 42 start-page: 482 year: 2012 end-page: 500 ident: bib38 article-title: An adaptive differential evolution algorithm with novel mutation and crossover strategies for global numerical optimization publication-title: IEEE Trans. Syst., Man, Cybern., Part B – volume: 43 start-page: 303 year: 2011 end-page: 315 ident: bib12 article-title: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems publication-title: Comput.-Aided Des. – volume: 44 start-page: 965 year: 2012 end-page: 983 ident: bib14 article-title: Multi-objective optimization of combined Brayton and inverse Brayton cycles using advanced optimization algorithms publication-title: Eng. Optim. – volume: 39 start-page: 1362 year: 2009 end-page: 1381 ident: bib26 article-title: Adaptive particle swarm optimization publication-title: IEEE Trans.Syst. Man Cybern. Part B: Cybern. – volume: 8 start-page: 687 year: 2008 end-page: 697 ident: bib7 article-title: On the performance of Artificial Bee Colony (ABC) algorithm publication-title: Appl. Soft Comput. – volume: 37 start-page: 5682 year: 2010 end-page: 5687 ident: bib32 article-title: Chaotic bee colony algorithms for global numerical optimization publication-title: Expert Syst. Appl. – volume: 6 start-page: 60 year: 2013 end-page: 72 ident: bib23 article-title: A modified teaching learning based optimization ( publication-title: Recent Pat. Comput. Sci. – volume: 10 start-page: 1132 year: 2010 end-page: 1140 ident: bib11 article-title: Grenade explosion method—a novel tool for optimization of multimodal functions publication-title: Appl. Soft Comput. – volume: 12 start-page: 3508 year: 2011 end-page: 3531 ident: bib34 article-title: Artificial bee colony algorithm for accurate global optimization of numerical functions publication-title: Inf. Sci. – year: 1989 ident: bib1 article-title: Genetic Algorithms in Search Optimization and Machine Learning – volume: 2 start-page: 353 year: 2005 end-page: 373 ident: bib9 article-title: Ant colony optimization: introduction and recent trends publication-title: Phys. Life Rev. – volume: 183 start-page: 1 year: 2012 end-page: 15 ident: bib18 article-title: Teaching–Learning-Based Optimization: an optimization method for continuous non-linear large scale problems publication-title: Inf. Sci. – volume: 44 start-page: 1447 year: 2012 end-page: 1462 ident: bib13 article-title: Teaching–learning based optimization algorithm for constrained and unconstrained real parameter optimization problems publication-title: Eng. Optim. – volume: 34 start-page: 225 year: 2012 ident: 10.1016/j.swevo.2013.12.005_bib16 article-title: Design of planar steel frames using Teaching–Learning Based Optimization publication-title: Eng. Struct. doi: 10.1016/j.engstruct.2011.08.035 – volume: 6 start-page: 60 issue: 1 year: 2013 ident: 10.1016/j.swevo.2013.12.005_bib23 article-title: A modified teaching learning based optimization (mTLBO) for global search publication-title: Recent Pat. Comput. Sci. doi: 10.2174/2213275911306010008 – volume: 27 start-page: 978 issue: 9 year: 2012 ident: 10.1016/j.swevo.2013.12.005_bib17 article-title: Parameter optimization of machining processes using a new optimization algorithm publication-title: Mater. Manuf. Processes doi: 10.1080/10426914.2011.602792 – volume: 8 start-page: 204 year: 2004 ident: 10.1016/j.swevo.2013.12.005_bib42 article-title: The fully informed particle swarm: simpler, maybe better publication-title: IEEE Trans. Evol. Comput. doi: 10.1109/TEVC.2004.826074 – ident: 10.1016/j.swevo.2013.12.005_bib6 – volume: 10 start-page: 1132 issue: 4 year: 2010 ident: 10.1016/j.swevo.2013.12.005_bib11 article-title: Grenade explosion method—a novel tool for optimization of multimodal functions publication-title: Appl. Soft Comput. doi: 10.1016/j.asoc.2009.11.032 – volume: 43 start-page: 303 year: 2011 ident: 10.1016/j.swevo.2013.12.005_bib12 article-title: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems publication-title: Comput.-Aided Des. doi: 10.1016/j.cad.2010.12.015 – volume: 111 start-page: 871 year: 2011 ident: 10.1016/j.swevo.2013.12.005_bib35 article-title: Improved artificial bee colony algorithm for global optimization publication-title: Inf. Process. Lett. doi: 10.1016/j.ipl.2011.06.002 – year: 1996 ident: 10.1016/j.swevo.2013.12.005_bib2 – volume: 42 start-page: 482 issue: 2 year: 2012 ident: 10.1016/j.swevo.2013.12.005_bib38 article-title: An adaptive differential evolution algorithm with novel mutation and crossover strategies for global numerical optimization publication-title: IEEE Trans. Syst., Man, Cybern., Part B doi: 10.1109/TSMCB.2011.2167966 – ident: 10.1016/j.swevo.2013.12.005_bib24 – volume: 10 start-page: 646 issue: 6 year: 2006 ident: 10.1016/j.swevo.2013.12.005_bib36 article-title: Self adapting control parameters in differential evolution: a comparative study on numerical benchmark problems publication-title: IEEE Trans. Evol. Comput. doi: 10.1109/TEVC.2006.872133 – volume: 13 start-page: 398 issue: 2 year: 2009 ident: 10.1016/j.swevo.2013.12.005_bib37 article-title: Differential evolution algorithm with strategy adaptation for global numerical optimization publication-title: IEEE Trans. Evol. Comput. doi: 10.1109/TEVC.2008.927706 – volume: 39 start-page: 1362 year: 2009 ident: 10.1016/j.swevo.2013.12.005_bib26 article-title: Adaptive particle swarm optimization publication-title: IEEE Trans.Syst. Man Cybern. Part B: Cybern. doi: 10.1109/TSMCB.2009.2015956 – ident: 10.1016/j.swevo.2013.12.005_bib3 doi: 10.1109/ICNN.1995.488968 – volume: 37 start-page: 5682 year: 2010 ident: 10.1016/j.swevo.2013.12.005_bib32 article-title: Chaotic bee colony algorithms for global numerical optimization publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2010.02.042 – volume: 3 start-page: 244 issue: 3 year: 2013 ident: 10.1016/j.swevo.2013.12.005_bib19 article-title: Unsupervised feature selection using roughest and teaching-based optimization publication-title: Int. J. Artif. Intell. Soft Comput. doi: 10.1504/IJAISC.2013.053401 – volume: 3 start-page: 27 issue: 1 year: 2013 ident: 10.1016/j.swevo.2013.12.005_bib20 article-title: Rough set and teaching learning based optimization technique for optimal feature selection publication-title: Central Eur. J. Comput. Sci. doi: 10.2478/s13537-013-0102-4 – volume: 2 start-page: 353 year: 2005 ident: 10.1016/j.swevo.2013.12.005_bib9 article-title: Ant colony optimization: introduction and recent trends publication-title: Phys. Life Rev. doi: 10.1016/j.plrev.2005.10.001 – volume: 15 start-page: 832 issue: 6 year: 2011 ident: 10.1016/j.swevo.2013.12.005_bib28 article-title: Orthogonal learning particle swarm optimization publication-title: IEEE Trans. Evol. Comput. doi: 10.1109/TEVC.2010.2052054 – volume: 10 start-page: 281 issue: 3 year: 2006 ident: 10.1016/j.swevo.2013.12.005_bib39 article-title: Comprehensive learning particle swarm optimizer for global optimization of multimodal functions publication-title: IEEE Trans. Evolut. Comput. doi: 10.1109/TEVC.2005.857610 – volume: 15 start-page: 55 issue: 1 year: 2011 ident: 10.1016/j.swevo.2013.12.005_bib29 article-title: Differential evolution with composite trial vector generation strategies and control parameters publication-title: IEEE Trans. Evol. Comput. doi: 10.1109/TEVC.2010.2087271 – volume: 44 start-page: 1447 issue: 12 year: 2012 ident: 10.1016/j.swevo.2013.12.005_bib13 article-title: Teaching–learning based optimization algorithm for constrained and unconstrained real parameter optimization problems publication-title: Eng. Optim. doi: 10.1080/0305215X.2011.652103 – volume: 13 start-page: 945 issue: 5 year: 2009 ident: 10.1016/j.swevo.2013.12.005_bib31 article-title: JADE: adaptive differential evolution with optional external archive publication-title: IEEE Trans. Evol. Comput. doi: 10.1109/TEVC.2009.2014613 – volume: 194 start-page: 3902 year: 2004 ident: 10.1016/j.swevo.2013.12.005_bib10 article-title: A new meta-heuristic algorithm for continuous engineering optimization: harmony search theory and practice publication-title: Comput. Methods Appl. Mech. Eng. doi: 10.1016/j.cma.2004.09.007 – volume: 44 start-page: 965 year: 2012 ident: 10.1016/j.swevo.2013.12.005_bib14 article-title: Multi-objective optimization of combined Brayton and inverse Brayton cycles using advanced optimization algorithms publication-title: Eng. Optim. doi: 10.1080/0305215X.2011.624183 – volume: 12 start-page: 3508 year: 2011 ident: 10.1016/j.swevo.2013.12.005_bib34 article-title: Artificial bee colony algorithm for accurate global optimization of numerical functions publication-title: Inf. Sci. doi: 10.1016/j.ins.2011.04.024 – volume: 185 start-page: 1088 issue: 3 year: 2008 ident: 10.1016/j.swevo.2013.12.005_bib41 article-title: Global and local real-coded genetic algorithms based on parent-centric crossover operators publication-title: Eur. J. Oper. Res. doi: 10.1016/j.ejor.2006.06.043 – ident: 10.1016/j.swevo.2013.12.005_bib5 – volume: 8 start-page: 687 issue: 1 year: 2008 ident: 10.1016/j.swevo.2013.12.005_bib7 article-title: On the performance of Artificial Bee Colony (ABC) algorithm publication-title: Appl. Soft Comput. doi: 10.1016/j.asoc.2007.05.007 – start-page: 148 year: 2011 ident: 10.1016/j.swevo.2013.12.005_bib15 – ident: 10.1016/j.swevo.2013.12.005_bib40 doi: 10.1109/CEC.2005.1554902 – ident: 10.1016/j.swevo.2013.12.005_bib25 – volume: 2 start-page: 1 issue: 130 year: 2013 ident: 10.1016/j.swevo.2013.12.005_bib22 article-title: A teaching–learning based optimization based on orthogonal design for solving global optimization problems publication-title: Springer Plus – volume: 11 start-page: 1679 issue: 2 year: 2011 ident: 10.1016/j.swevo.2013.12.005_bib30 article-title: Differential evolution algorithm with ensemble of parameters and mutation strategies publication-title: Appl. Soft Comput. doi: 10.1016/j.asoc.2010.04.024 – year: 2006 ident: 10.1016/j.swevo.2013.12.005_bib4 – volume: 183 start-page: 1 issue: 1 year: 2012 ident: 10.1016/j.swevo.2013.12.005_bib18 article-title: Teaching–Learning-Based Optimization: an optimization method for continuous non-linear large scale problems publication-title: Inf. Sci. doi: 10.1016/j.ins.2011.08.006 – volume: 4 start-page: 429 year: 2013 ident: 10.1016/j.swevo.2013.12.005_bib21 article-title: Weighted teaching–learning based optimization for global function optimization publication-title: Appl. Math. doi: 10.4236/am.2013.43064 – year: 2004 ident: 10.1016/j.swevo.2013.12.005_bib8 – volume: 217 start-page: 3166 year: 2010 ident: 10.1016/j.swevo.2013.12.005_bib33 article-title: Gbest-guided artificial bee colony algorithm for numerical function optimization publication-title: Appl. Math. Comput. doi: 10.1016/j.amc.2010.08.049 – year: 1989 ident: 10.1016/j.swevo.2013.12.005_bib1 – volume: 8 start-page: 240 year: 2004 ident: 10.1016/j.swevo.2013.12.005_bib27 article-title: Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients publication-title: IEEE Trans. Evol. Comput. doi: 10.1109/TEVC.2004.826071 |
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