Abstract
A variant of teaching–learning-based optimization algorithm (TLBO) with multi-classes cooperation and simulated annealing operator (SAMCCTLBO) is proposed in paper. To take full advantage of microteaching, the population is divided into several sub-classes, the mean of all learners in teacher phase of original TLBO is replaced by the mean solutions of different sub-classes, the modification might make the mean solutions improved quickly for the effect of microteaching is often better than teaching in big classes. With considering the limitation of learning ability of learner, the learners in different sub-classes only learn new knowledge from others in their sub-classes in learner phase of SAMCCTLBO, and all learners are regrouped randomly after some generations to improve the diversity of the sub-classes. The diversity of the whole class is improved by simulated annealing operator. The effectiveness of the proposed algorithm is tested on several benchmark functions, the results demonstrate that SAMCCTLBO has some good performances when compared with some other EAs.
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References
Adil B, Alper H, Simge YK (2014) Testing the performance of teaching–learning based optimization (TLBO) algorithm on combinatorial problems: flow shop and job shop scheduling cases. Inf Sci 276:204–218
Arnold DV, Hansen N (2012) A (1\(+\)1)-CMA-ES for constrained optimisation. GECCO, Philadelphia
Bergh FVD, Engelbrecht AP (2004) A cooperative approach to particle swarm optimization. IEEE Trans Evolut comput 8(3):225–239
Brest J, Greiner S, Boskovic B, Mernik M, Zumer V (2006) Self-adapting control parameters in differential evolution: a comparative study on numerical benchmark problems. IEEE Trans Evolut Comput 10(6):646–657
Clerc M, Kennedy J (2002) The particle swarm-explosion, stability, and convergence in a multidimensional complex space. IEEE Trans Evol Comput 6(1):58–73
Dekkers A, Aarts E (1991) Global optimization and simulated annealing. Math Program 5:367–393
Dor AE, Clere M, Siarry P (2012) A multi-swarm PSO using charged particles in a partitioned search space for continuous optimization. Comput Optim Appl 53(1):271–295
Floudas CA, Gounaris CE (2009) A review of recent advances in global optimization. J Global Optim 45:3–38
Hossein H, Taher N, Seyed IT (2011) A Modified TLBO algorithm for placement of AVRs considering DGs, 26th international power system conference, 2011, pp 1–8
Kennedy J, Mendes R (2002) Population structure and particle swarm performance. In: Proceedings of IEEE congress Ecol. Comput. Honolulu, HI, pp 1671–1676
Li CH, Yang SX, Nguyen TT (2012) A self-learning particle swarm optimizer for global optimization problems. IEEE Trans Syst Man Cybernet Part B 42(3):627–646
Liang JJ, Qin AK, Suganthan PN, Baskar S (2006) Comprehensive learning particle swarm optimizer for global optimization of multimodal functions. IEEE Trans Evolut Comput 10(3):281–294
Mendes R, Kenedy J, Neves J (2004) The fully informed particle swarm: simpler, maybe better. IEEE Trans Evolut Comput 8(3):868–873
Naik A, Satapathy SC, Parvathi K (2012) Improvement of initial cluster center of c-means using teaching–learning based optimization. 2nd international conference on communication, computing and security. Proc Technol 6:428–435
Niknam T, Golestaneh F, Sadeghi MS (2012) \(\theta \)-Multi-objective teaching–learning-based optimization for dynamic economic emission dispatch. IEEE Syst J 6(2):341–352
Niu B, Zhu YL, Hw XX, Henry W (2007) MCPSO: a multi-swarm cooperative particle swarm optimizer. Appl Math Comput 185:1050–1062
Parsopoulos KE, Vrahatis MN (2004) UPSO—a unified particle swarm optimization scheme, in lecture series on computational sciences, pp 868–873
Peram T, Veeramachaneni K, Mohan CK (2003) Fitness-distance-ratio based particle swarm optimization. In: Proceedings of swarm intelligence symposium, 2003, pp 174–181
Qin AK, Huang VL, Suganthan PN (2009) Differential evolution algorithm with strategy adaptation for global numerical optimization. IEEE Trans Evolut Comput 13(2):398–417
Rao RV, Patel V (2011) Multi-objective optimization of combined Brayton and inverse Brayton cycles using advanced optimization algorithms. Eng Optim 44(8):965–983
Rao RV, Patel V (2012) An elitist teaching–learning-based optimization algorithm for solving complex constrained optimization problems. Int J Ind Eng Comput 3:535–560
Rao RV, Patel V (2013) Multi-objective optimization of heat exchangers using a modified teaching–learning-based optimization algorithm. Appl Math Model 37(3):1147–1162
Rao RV, Savasni VJ, Vakharia DP (2012) Teaching–learning-based optimization: an optimization method for continuous non-linear large scale problems. Inf Sci 183(1):1–15
Rao RV, Savasni VJ, Vakharia DP (2011) Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Comput Aid Des 43(3):303–315
Rao RV, Savsani VJ, Balic J (2011) Teaching–learning-based optimization algorithm for unconstrained and constrained real-parameter optimization problems. Eng Optim 44(12):1447–1462
Rao RV, Waghmare GG (2014) A comparative study of a teaching–learning-based optimization algorithm on multi-objective unconstrained and constrained functions. J King Saud Univ Comput Inf Sci 26:332–346
Sabat SL, Ali L, Udgata SK (2011) Integrated learning particle swarm optimizer for global optimization. Appl Soft Comput 11:574–584
Suganthan PN (1999) Particle swarm optimizer with neighborhood operator. In: Proceedings of conference Evol. Comput, Washington, DC, pp 1958–1962
Suresh CS, Anima N (2011) Based data clustering, on teaching–learning-based optimization SEMCCO, 2011 Part II. LNCS 7077 (2011):148–156
Shi Y, Eberhart RC (1998) A modified particle swarm optimizer. In: Proceedings of IEEE Congr. Evol. computation, 1998:69–73
Tang K, Yao X, Suganthan PN et al (2007) Benchmark functions for the CEC’2008 special session and competition on large scale global optimization. Nature Inspired Computation and Applications Laboratory, USTC, China
Vedat T (2012) Design of planar steel frames using teaching–learning based optimization. Eng Struct 34:225–232
Waghmare G (2013) Comments on “a note on teaching–learning-based optimization algorithm”. Inf Sci 229:159–169
Wang Y, Cai Z, Zhang Q (2011) Differential evolution with composite trial vector generation strategies and control parameters. IEEE Trans Evolut Comput 14(1):55–66
Wolpert DH, Macready WG (1997) No free lunch theorems for optimization. IEEE Trans Evolut Comput 1(1):67–82
Xu Y, Wang L, Wang SY, Liu M (2015) An effective teaching–learning-based optimization algorithm for the flexible job-shop scheduling problem with fuzzy processing time. Neurocomputing 148:260–268
Yu K, Wang X, Wang Z (2014) An improved teaching–learning-based optimization algorithm for numerical and engineering optimization problems [J]. J Intel Manufact. doi:10.1007/s10845-014-0918-3
Yao X, Liu Y, Lin GM (1999) Evolutionary programming made faster. IEEE Trans Evolut Comput 3(2):82–102
Zhang JQ, Sanderson AC (2009) JADE: adaptive differential evolution with optional external archive. IEEE Trans Evolut comput 13(5):945–958
Zhang JZ, Ding XM (2011) A multi-swarm self-adaptive and cooperative particle swarm optimization. Eng Appl Artif Intel 24:958–967
Zou F, Wang L, Lei XH, Chen DB et al (2013) Multi-objective optimization using teaching–learning-based optimization algorithm. Eng Appl Artif Intel 26:1291–1300
Acknowledgments
This work is partially supported by Natural Science Foundation of Anhui Province, China, (Grant No. 1308085MF82), National Natural Science Foundation of China (Grants No. 41475017, No. 61403304, No. 61304082), Sci-tech talents cultivation Fund projects of HuaiBei city (Grant No. 20110304). The authors would like to thank Prof. Suganthan who provided some code of PSOs and the code provided by Y. Wang, Z. Cai and Q. Zhang (http://dces.essex.ac.uk/staff/qzhang/mypublication.htm).
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Communicated by V. Loia.
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Chen, D., Zou, F., Wang, J. et al. SAMCCTLBO: a multi-class cooperative teaching–learning-based optimization algorithm with simulated annealing. Soft Comput 20, 1921–1943 (2016). https://doi.org/10.1007/s00500-015-1613-9
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DOI: https://doi.org/10.1007/s00500-015-1613-9