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Volumn 297, Issue , 2015, Pages 171-190

An improved teaching-learning-based optimization algorithm for solving global optimization problem

Author keywords

Artificial bee colony (ABC) algorithm; Global optimization; Improved teaching learning based optimization (ITLBO); Particle swarm optimization (PSO); Teaching learning based optimization (TLBO)

Indexed keywords

EVOLUTIONARY ALGORITHMS; GLOBAL OPTIMIZATION; LEARNING ALGORITHMS; OPTIMIZATION; PARTICLE SWARM OPTIMIZATION (PSO); PROBLEM SOLVING; TEACHING;

EID: 84961289757     PISSN: 00200255     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ins.2014.11.001     Document Type: Article
Times cited : (97)

References (39)
  • 1
    • 84885067256 scopus 로고    scopus 로고
    • Performance analysis of the coarse-grained parallel model of the artificial bee colony algorithm
    • A. Basturk, and R. Akay Performance analysis of the coarse-grained parallel model of the artificial bee colony algorithm Inf. Sci. 253 2013 34 55
    • (2013) Inf. Sci. , vol.253 , pp. 34-55
    • Basturk, A.1    Akay, R.2
  • 3
    • 33847199831 scopus 로고    scopus 로고
    • Self-adapting control parameters in differential evolution: A comparative study on numerical benchmark problems
    • J. Brest, S. Greiner, B. Boskovic, M. Mernik, and V. Zumer Self-adapting control parameters in differential evolution: a comparative study on numerical benchmark problems IEEE Trans. Evol. Comput. 10 6 2006 646 657
    • (2006) IEEE Trans. Evol. Comput. , vol.10 , Issue.6 , pp. 646-657
    • Brest, J.1    Greiner, S.2    Boskovic, B.3    Mernik, M.4    Zumer, V.5
  • 4
    • 84870056206 scopus 로고    scopus 로고
    • Simulated annealing based artificial bee colony algorithm for global numerical optimization
    • S.M. Chen, A. Sarosh, and Y.F. Dong Simulated annealing based artificial bee colony algorithm for global numerical optimization Appl. Math. Comput. 219 2012 3575 3589
    • (2012) Appl. Math. Comput. , vol.219 , pp. 3575-3589
    • Chen, S.M.1    Sarosh, A.2    Dong, Y.F.3
  • 5
    • 0036464756 scopus 로고    scopus 로고
    • The particle swarm-explosion, stability, and convergence in a multidimensional complex space
    • M. Clerc, and J. Kennedy The particle swarm-explosion, stability, and convergence in a multidimensional complex space IEEE Trans. Evol. Comput. 6 1 2002 58 73
    • (2002) IEEE Trans. Evol. Comput. , vol.6 , Issue.1 , pp. 58-73
    • Clerc, M.1    Kennedy, J.2
  • 7
    • 70349873857 scopus 로고    scopus 로고
    • Group search optimizer: An optimization algorithm inspired by animal searching behavior
    • S. He, Q.H. Wu, and J.R. Saunders Group search optimizer: an optimization algorithm inspired by animal searching behavior IEEE Trans. Evol. Comput. 13 5 2009 973 990
    • (2009) IEEE Trans. Evol. Comput. , vol.13 , Issue.5 , pp. 973-990
    • He, S.1    Wu, Q.H.2    Saunders, J.R.3
  • 8
    • 84856300028 scopus 로고    scopus 로고
    • Hybrid ant colony-genetic algorithm (GAAPI) for global continuous optimization
    • C. Irina, and K. Elias Hybrid ant colony-genetic algorithm (GAAPI) for global continuous optimization IEEE Trans. Syst., Man, Cybern. Part B: Cybern. 42 1 2012 234 245
    • (2012) IEEE Trans. Syst., Man, Cybern. Part B: Cybern. , vol.42 , Issue.1 , pp. 234-245
    • Irina, C.1    Elias, K.2
  • 9
    • 84859002368 scopus 로고    scopus 로고
    • An adaptive differential evolution algorithm with novel mutation and crossover strategies for global numerical optimization
    • S.M. Islam, S. Das, S. Ghosh, S. Roy, and P.N. Suganthan An adaptive differential evolution algorithm with novel mutation and crossover strategies for global numerical optimization IEEE Trans. Syst., Man, Cybern. Part B: Cybern. 42 2 2012 482 500
    • (2012) IEEE Trans. Syst., Man, Cybern. Part B: Cybern. , vol.42 , Issue.2 , pp. 482-500
    • Islam, S.M.1    Das, S.2    Ghosh, S.3    Roy, S.4    Suganthan, P.N.5
  • 10
    • 34548479029 scopus 로고    scopus 로고
    • On the performance of artificial bee colony (ABC) algorithm
    • D. Karaboga, and B. Basturk On the performance of artificial bee colony (ABC) algorithm Appl. Soft Comput. 8 1 2008 687 697
    • (2008) Appl. Soft Comput. , vol.8 , Issue.1 , pp. 687-697
    • Karaboga, D.1    Basturk, B.2
  • 13
    • 0035247566 scopus 로고    scopus 로고
    • An orthogonal genetic algorithm with quantization for global numerical optimization
    • Y.W. Leung, and Y. Wang An orthogonal genetic algorithm with quantization for global numerical optimization IEEE Trans. Evol. Comput. 5 1 2001 41 53
    • (2001) IEEE Trans. Evol. Comput. , vol.5 , Issue.1 , pp. 41-53
    • Leung, Y.W.1    Wang, Y.2
  • 14
    • 35048898017 scopus 로고    scopus 로고
    • Adaptively choosing neighborhood bests using species in a particle swarm optimizer for multimodal function optimization
    • X. Li, Adaptively choosing neighborhood bests using species in a particle swarm optimizer for multimodal function optimization, in: Proc. Genetic Evol. Comput. Conf., 2004, pp. 105-116.
    • (2004) Proc. Genetic Evol. Comput. Conf. , pp. 105-116
    • Li, X.1
  • 16
    • 33744730797 scopus 로고    scopus 로고
    • Comprehensive learning particle swarm optimizer for global optimization of multimodal functions
    • J.J. Liang, A.K. Qin, P.N. Suganthan, and S. Baskar Comprehensive learning particle swarm optimizer for global optimization of multimodal functions IEEE Trans. Evol. Comput. 10 3 2006 281 295
    • (2006) IEEE Trans. Evol. Comput. , vol.10 , Issue.3 , pp. 281-295
    • Liang, J.J.1    Qin, A.K.2    Suganthan, P.N.3    Baskar, S.4
  • 18
    • 3142781923 scopus 로고    scopus 로고
    • The fully informed particle swarm: Simpler, maybe better
    • R. Mendes, J. Kennedy, and J. Neves The fully informed particle swarm: simpler, maybe better IEEE Trans. Evol. Comput. 8 3 2004 204 210
    • (2004) IEEE Trans. Evol. Comput. , vol.8 , Issue.3 , pp. 204-210
    • Mendes, R.1    Kennedy, J.2    Neves, J.3
  • 19
    • 0033747331 scopus 로고    scopus 로고
    • On how Pachycondyla apicalis ants suggest a new search algorithm
    • N. Monmarche, G. Venturini, and M. Slimane On how Pachycondyla apicalis ants suggest a new search algorithm Future Gener. Comput. Syst. 16 8 2000 937 946
    • (2000) Future Gener. Comput. Syst. , vol.16 , Issue.8 , pp. 937-946
    • Monmarche, N.1    Venturini, G.2    Slimane, M.3
  • 20
    • 84867575848 scopus 로고    scopus 로고
    • A novel hybrid algorithm based on particle swarm and ant colony optimization for finding the global minimum
    • S.K. Mustafa, G. Mustafa, and K.B. Omer A novel hybrid algorithm based on particle swarm and ant colony optimization for finding the global minimum Appl. Math. Comput. 219 2012 1515 1521
    • (2012) Appl. Math. Comput. , vol.219 , pp. 1515-1521
    • Mustafa, S.K.1    Mustafa, G.2    Omer, K.B.3
  • 21
    • 84862688092 scopus 로고    scopus 로고
    • A dynamic neighborhood learning based particle swarm optimizer for global numerical optimization
    • M. Nasir, S. Das, D. Maity, S. Sengupta, U. Halder, and P.N. Suganthan A dynamic neighborhood learning based particle swarm optimizer for global numerical optimization Inf. Sci. 209 2012 16 36
    • (2012) Inf. Sci. , vol.209 , pp. 16-36
    • Nasir, M.1    Das, S.2    Maity, D.3    Sengupta, S.4    Halder, U.5    Suganthan, P.N.6
  • 24
    • 84898910167 scopus 로고    scopus 로고
    • Particle swarm optimizer with neighborhood operator
    • Washington, DC
    • P.N. Suganthan, Particle swarm optimizer with neighborhood operator, in: Proc. Congr. Evol. Comput., Washington, DC, 1999, pp. 1958-1962.
    • (1999) Proc. Congr. Evol. Comput. , pp. 1958-1962
    • Suganthan, P.N.1
  • 25
    • 84867222422 scopus 로고    scopus 로고
    • A hybrid co-evolutionary cultural algorithm based on particle swarm optimization for solving global optimization problems
    • Y. Sun, L.B. Zhang, and X.S. Gu A hybrid co-evolutionary cultural algorithm based on particle swarm optimization for solving global optimization problems Neurocomputing 98 2012 76 89
    • (2012) Neurocomputing , vol.98 , pp. 76-89
    • Sun, Y.1    Zhang, L.B.2    Gu, X.S.3
  • 27
    • 80155189617 scopus 로고    scopus 로고
    • Design of planar steel frames using teaching-learning based optimization
    • T. Vedat Design of planar steel frames using teaching-learning based optimization Eng. Struct. 34 2012 225 232
    • (2012) Eng. Struct. , vol.34 , pp. 225-232
    • Vedat, T.1
  • 29
    • 84870064537 scopus 로고    scopus 로고
    • Parameter optimization of modern machining processes using teaching-learning-based optimization algorithm
    • R. Venkata Rao, and V.D. Kalyankar Parameter optimization of modern machining processes using teaching-learning-based optimization algorithm Eng. Appl. Artif. Intell. 26 2013 524 531
    • (2013) Eng. Appl. Artif. Intell. , vol.26 , pp. 524-531
    • Venkata Rao, R.1    Kalyankar, V.D.2
  • 30
    • 84870236403 scopus 로고    scopus 로고
    • Multi-objective optimization of heat exchangers using a modified teaching-learning-based optimization algorithm
    • V. Venkata Rao, and V. Patel Multi-objective optimization of heat exchangers using a modified teaching-learning-based optimization algorithm Appl. Math. Model. 37 3 2012 1147 1162
    • (2012) Appl. Math. Model. , vol.37 , Issue.3 , pp. 1147-1162
    • Venkata Rao, V.1    Patel, V.2
  • 31
    • 84868299435 scopus 로고    scopus 로고
    • Teaching-learning-based optimization algorithm for unconstrained and constrained real-parameter optimization problems
    • R. Venkata Rao, V.J. savasni, and J. Bilac Teaching-learning-based optimization algorithm for unconstrained and constrained real-parameter optimization problems Eng. Optimiz. 44 12 2012 1447 1462
    • (2012) Eng. Optimiz. , vol.44 , Issue.12 , pp. 1447-1462
    • Venkata Rao, R.1    Savasni, V.J.2    Bilac, J.3
  • 32
    • 78951475022 scopus 로고    scopus 로고
    • Teaching-learning-based optimization: A novel method for constrained mechanical design optimization problems
    • R. Venkata Rao, V.J. savasni, and D.P. Vakharia Teaching-learning-based optimization: a novel method for constrained mechanical design optimization problems Comput.-Aided Des. 43 3 2011 303 315
    • (2011) Comput.-Aided Des. , vol.43 , Issue.3 , pp. 303-315
    • Venkata Rao, R.1    Savasni, V.J.2    Vakharia, D.P.3
  • 33
    • 80055062464 scopus 로고    scopus 로고
    • Teaching-learning-based optimization: An optimization method for continuous non-linear large scale problems
    • R. Venkata Rao, V.J. savasni, and D.P. Vakharia Teaching-learning-based optimization: an optimization method for continuous non-linear large scale problems Inf. Sci. 183 1 2012 1 15
    • (2012) Inf. Sci. , vol.183 , Issue.1 , pp. 1-15
    • Venkata Rao, R.1    Savasni, V.J.2    Vakharia, D.P.3
  • 34
    • 84870065071 scopus 로고    scopus 로고
    • An elitist teaching-learning-based optimization algorithm for solving complex constrained optimization problems
    • R. Venkata Rao, and P. vivek An elitist teaching-learning-based optimization algorithm for solving complex constrained optimization problems Int. J. Ind. Eng. Comput. 3 4 2012 535 560
    • (2012) Int. J. Ind. Eng. Comput. , vol.3 , Issue.4 , pp. 535-560
    • Venkata Rao, R.1    Vivek, P.2
  • 35
    • 34948900352 scopus 로고    scopus 로고
    • An evolutionary algorithm for global optimization based on level-set evolution and Latin squares
    • Y. Wang, and C. Dang An evolutionary algorithm for global optimization based on level-set evolution and Latin squares IEEE Trans. Evol. Comput. 11 5 2007 579 595
    • (2007) IEEE Trans. Evol. Comput. , vol.11 , Issue.5 , pp. 579-595
    • Wang, Y.1    Dang, C.2
  • 38
    • 3042859422 scopus 로고    scopus 로고
    • Fast evolution strategies
    • X. Yao, Y. Liu, and G. Lin Fast evolution strategies Control Cybern. 26 3 1997 467 496
    • (1997) Control Cybern. , vol.26 , Issue.3 , pp. 467-496
    • Yao, X.1    Liu, Y.2    Lin, G.3
  • 39
    • 0032685734 scopus 로고    scopus 로고
    • Evolutionary programming made faster
    • X. Yao, Y. Liu, and G.M. Lin Evolutionary programming made faster IEEE Trans. Evol. Comput. 3 2 1999 82 102
    • (1999) IEEE Trans. Evol. Comput. , vol.3 , Issue.2 , pp. 82-102
    • Yao, X.1    Liu, Y.2    Lin, G.M.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.