Abstract
Recently hybrid optimization algorithms enjoy growing attention in the optimization community. However, over the last two decades, many new hybrid meta-heuristics optimization techniques are developed and are still developing. On the hybrid optimization algorithm, the most common criticism is that they are not well balanced in respect of the local search and global search of the algorithm. Viewing this, in the present work a modified adaptive based teaching factor is suggested for the basic TLBO algorithm. Also, a novel hybrid approach is proposed that combines the Teaching Learning Base Optimization (TLBO) Algorithm and Quadratic approximation (QA). The QA is applied to improve the global as well as local search capability of the method that also represents the characters of “Teacher Refresh”. For the performance investigation, the suggested algorithm is involved to solve twenty classical optimization functions and one real life optimization problem and the performances are differentiated with different state-of-the-arts methods in terms of numerical results of the solution.
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References
Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceeding of the IEEE International Conference on Neural Networks, 1995, 1942–1948
Shi, Y., Eberhart, R.: A modified particle swarm optimizer. In: IEEE International Conference on Computational Intelligence, pp. 69–73 (1998)
Holland, J.: Adaptation in Natural and Artificial Systems. University of Michigan Press (1975)
Dorigo, M.: Optimization, Learning and Natural Algorithms. Thesis (Ph.D.), Politecnico di Milano (1992)
Storn, R., Price, K.: Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces. J. Glob. Optim. 11(4), 341–359 (1997)
Rashedi, E., Nezamabadi-pour, H., Saryazdi, S.: GSA: a gravitational search algorithm. Inf. Sci. 179, 2232–2248 (2009)
Akay, B., Karaboga, D.: Artificial bee colony algorithm for large-scale problems and engineering design optimization. J. Intell. Manuf. (2010). https://doi.org/10.1007/s10845-010-0393-4
Akay, B., Karaboga, D.: A modified artificial bee colony (ABC) algorithm for constrained optimization problems. Appl. Soft Comput. (2010). https://doi.org/10.1016/j.asoc.2010.12.001
Rao, R.V., Savsani, V.J., Vakharia, D.P.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Comput. Aided Des. 43, 303–315 (2011)
Clerc, M., Kennedy, J.: The particle swarm-explosion, stability, and convergence in a multidimensional complex space. IEEE Trans. Evol. Comput. 6(1), 58–73 (2002)
Kennedy, J., Mendes, R.: Population structure and particle swarm performance. In: Proceedings of IEEE Congress Evolutionary Computation, Honolulu, HI, 2002, pp. 1671–1676
Mendes, R., Kennedy, J., Neves, J.: The fully informed particle swarm: simpler, maybe better. IEEE Trans. Evol. Comput. 8, 204–210 (2004)
Parsopoulos, K.E., Vrahatis, M.N.: UPSO—a unified particle swarm optimization scheme. Lect. Ser. Comput. Sci. 1, 868–873 (2004)
Peram, T., Veeramachaneni, K., Mohan, C.K.: Fitness-distance-ratio based particle swarm optimization. In: Proceedings of Swarm Intelligence Symposium, pp. 174–181 (2003)
van den Bergh, F., Engelbrecht, A.P.: A cooperative approach to particle swarm optimization. IEEE Trans. Evol. Comput. 8, 225–239 (2004)
Liang, J.J., Qin, A.K., Suganthan, P.N., Baskar, S.: Comprehensive learning particle swarm optimizer for global optimization of multimodal functions. IEEE Trans. Evol. Comput. 10(3) (2006)
Qin, A.K., Huang, V.L., Suganthan, P.N.: Differential evolution algorithm with strategy adaptation for global numerical optimization. IEEE Trans. Evol. Comput. 13(April), 398–417 (2009)
Iorio, A., Li, X.: Solving rotated multi-objective optimization problems using differential evolution. In: Australian Conference on Artificial Intelligence, Cairns, Australia, 2004, pp. 861–872
Storn, R.: On the usage of differential evolution for function optimization. In: Biennial Conference of the North American Fuzzy Information Processing Society (NAFIPS), pp. 519–523. IEEE, Berkeley (1996)
Mallipeddi, R., Suganthan, P.N., Pan, Q.K., Tasgetiren, M.F.: Differential evolution algorithm with ensemble of parameters and mutation strategies. Appl. Soft Comput. 11, 1679–1696 (2011)
Pant, M., Thangaraj, R.: DE-PSO: a new hybrid meta-heuristic for solving global optimization problems. New Math. Nat. Comput. 7(3), 363–381 (2011)
Deep, K., Das, K.N.: Quadratic approximation based hybrid genetic algorithm for function optimization. Appl. Math. Comput. 203, 86–98 (2008)
Abd-El-Wahed, W.F., Mousa, A.A., El-Shorbagy, M.A.: Integrating particle swarm optimization with genetic algorithms for solving nonlinear optimization problems. J. Comput. Appl. Math. 235, 1446–1453 (2011)
Zhang, L., Li, H., Jiao, Y.-C., Zhang, F.-S.: Hybrid differential evolution and the simplified quadratic interpolation for global optimization. Copyright is held by the author/owner(s). GEC’09, 12–14 June 2009, Shanghai, China. ACM 978-1-60558-326-6/09/06
Mirjalili, S., Mohd Hashim, S.Z.: A new hybrid PSOGSA algorithm for function optimization. In: International Conference on Computer and Information Application, ICCIA 2010
Deep, K., Bansal, J.C.: Hybridization of particle swarm optimization with quadratic approximation. OPSEARCH 46(1), 3–24
Pant, M., Thangaraj, R., Abraham, A.: A new PSO algorithm with crossover operator for global optimization problems. Innov. Hybrid Intell. Syst., ASC 44, 215–222 (2007)
Nama, S., Saha, A.K., Ghosh, S.: A new ensemble algorithm of differential evolution and backtracking search optimization algorithm with adaptive control parameter for function optimization. Int. J. Ind. Eng. Comput. 7, 323–338 (2016)
Nama, S., Saha, A.K., Ghosh, S.: A hybrid symbiosis organisms search algorithm and its application to real world problems. Memetic Comput. (2016). https://doi.org/10.1007/s12293-016-0194-1
Satapathy, S.C., Naik, A.: A modified teaching-learning-based optimization (mTLBO) for global search. Recent Pat. Comput. Sci. 6, 60–72 (2013)
Satapathy, S.C., Naik, A., Parvathi, K.: A teaching learning based optimization based on orthogonal design for solving global optimization problems
Rao, R.V., Patel, V.: Comparative performance of an elitist teaching-learning-based optimization algorithm for solving unconstrained optimization problems. Int. J. Ind. Eng. Comput. 4, 29–50 (2013)
Satapathy, S.C., Naik, A., Parvathi, K.: Weighted teaching-learning-based optimization for global function optimization. Appl. Math. 4, 429–439 (2013)
Nayak, M.R., Nayak, C.K., Rout, P.K.: Application of multi-objective teaching learning based optimization algorithm to optimal power flow problem. In: 2nd International Conference on Communication, Computing & Security [ICCCS-2012], Procedia Technology, vol. 6, pp. 255–264 (2012)
Xia, K., et al.: Disassembly sequence planning using a simplified teaching–learning-based optimization algorithm. Adv. Eng. Inform. (2014). http://dx.doi.org/10.1016/j.aei.2014.07.00
Roy, P.K., Paul, C., Sultana, S.: Oppositional teaching learning based optimization approach for combined heat and power dispatch. Electr. Power Energy Syst. 57, 392–403 (2014)
Roy, P.K., Sur, A., Pradhan, D.K.: Optimal short-term hydro-thermal scheduling using quasi-oppositional teaching learning based optimization. Eng. Appl. Artif. Intell. 26, 2516–2524 (2013)
Venkata Rao, R.: Teaching Learning Based Optimization Algorithm: And Its Engineering Applications, 1st edn. Springer Publishing Company, Incorporated (2015)
Jiang, X., Zhou, J.: Hybrid DE-TLBO algorithm for solving short term hydro-thermal optimal scheduling with incommensurable objectives. In: Proceedings of the 32nd Chinese Control Conference, 26–28 July 2013, Xian, China
Xie, Z., Zhang, C., Shao, X., Lin, W., Zhu, H.: An effective hybrid teaching–learning-based optimization algorithm for permutation flow shop scheduling problem. Adv. Eng. Softw. 77, 35–47 (2014)
Azad-Farsani, E., Zare, M., Azizipanah-Abarghooee, R., Askarian-Abyaneh, H.: A new hybrid CPSO-TLBO optimization algorithm for distribution network reconfiguration. J. Intell. Fuzzy Syst. 26(5), 2175–2184 (2014). https://doi.org/10.3233/IFS-130892
Dokeroglu, T.: Hybrid teaching–learning-based optimization algorithms for the quadratic assignment problem. Comput. Ind. Eng. 85, 86–101 (2015)
Gnanambal, K., Jeyavelumani, K.R., Juriya Banu, H.: Optimal, power flow using hybrid teaching learning based optimization algorithm. GRD Journals. Global Research and Development Journal for Engineering. International Conference on Innovations in Engineering and Technology, (ICIET)—2016, July 2016. e-ISSN: 2455-5703
Khare, R., Kumar, Y.: A novel hybrid MOL–TLBO optimized techno-economic-socio analysis of renewable energy mix in island mode. Appl. Soft Comput. 43, 187–198 (2016)
Sahu, B.K., Pati, T.K., Nayak, J.R., Panda, S., Kar, S.K.: A novel hybrid LUS–TLBO optimized fuzzy-PID controller for load frequency control of multi-source power system. Int. J. Electr. Power Energy Syst. 74, 58–69 (2016)
Babazadeh, R., Tavakkoli-Moghaddam, R.: A hybrid GA-TLBO algorithm for optimizing a capacitated three-stage supply chain network. Int. J. Ind. Eng. Prod. Res. 28, 151–161 (2017)
Deb, S., Kalita, K., Gao, X., Tammi, K., Mahanta, P.: Optimal placement of charging stations using CSO-TLBO algorithm. In: 2017 Third International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN), Kolkata, pp. 84–89 (2017)
Patsariya, A., et al.: Implementation of noble TLBO-MPPT technique for SPV in hybrid DC-DC boost converter. In: 2017 International Conference on Energy, Communication, Data Analytics and Soft Computing (ICECDS), pp. 1622–1627 (2017)
Shahbeig, S., Helfroush, M.S., Rahideh, A.: A fuzzy multi-objective hybrid TLBO–PSO approach to select the associated genes with breast cancer. Signal Process. 131, 58–65 (2017)
Tuo, S., Yong, L., Deng, F., Li, Y., Lin, Y., Lu, Q.: HSTLBO: a hybrid algorithm based on harmony search and teaching-learning-based optimization for complex high-dimensional optimization problems. PLoS ONE 12(4), e0175114 (2017). https://doi.org/10.1371/journal.pone.0175114
Ding, Y., et al.: A novel hybrid teaching learning based optimization algorithm for function optimization. In: 2017 Chinese Automation Congress (CAC), pp. 4383–4388 (2017)
Singh, R., Chaudhary, H., Singh, A.K.: A new hybrid teaching–learning particle swarm optimization algorithm for synthesis of linkages to generate path. Sadhana 42(11), 1851–1870 (2017)
Chen, X., Xu, B., Yu, K., Du, W.: Teaching-learning-based optimization with learning enthusiasm mechanism and its application in chemical engineering. J. Appl. Math. (2018). https://doi.org/10.1155/2018/1806947
Nenavath, H., Jatoth, R.K.: Hybrid SCA–TLBO: a novel optimization algorithm for global optimization and visual tracking. Neural Comput. Appl. (2018). https://doi.org/10.1007/s00521-018-3376-6
Zhang, M., Pan, Y., Zhu, J., Chen, G.: BC-TLBO: a hybrid algorithm based on artificial bee colony and teaching-learning-based optimization, pp. 2410–2417 (2018). https://doi.org/10.23919/chicc.2018.8483829
Sevinç, E., Dökeroğlu, T.: A novel hybrid teaching-learning-based optimization algorithm for the classification of data by using extreme learning machines. Turk. J. Electr. Eng. Comput. Sci. 27, 1523–1533 (2019). https://doi.org/10.3906/elk-1802-40
Guo, C., Lu, J., Tian, Z., Guo, W., Darvishan, A.: Optimization of critical parameters of PEM fuel cell using TLBO-DE based on Elman neural network. Energy Convers. Manag. 183, 149–158 (2019)
Zhang, Q., Yu, G., Song, H.: A hybrid bird mating optimizer algorithm with teaching-learning-based optimization for global numerical optimization. Stat. Optim. Inf. Comput. 3 (2015). https://doi.org/10.19139/soic.v3i1.86
Tang, Q., Li, Z., Zhang, L.P., Zhang, C.: Balancing stochastic two-sided assembly line with multiple constraints using hybrid teaching-learning-based optimization algorithm. Comput. Oper. Res. 82, 102–113 (2017)
Shao, W., Pi, D., Shao, Z.: A hybrid discrete optimization algorithm based on teaching–probabilistic learning mechanism for no-wait flow shop scheduling. Knowl.-Based Syst. 107, 219–234 (2016)
Shao, W., Pi, D., Shao, Z.: A hybrid discrete teaching-learning based meta-heuristic for solving no-idle flow shop scheduling problem with total tardiness criterion. Comput. Oper. Res. 94, 89–105 (2018)
Das, S.P., Padhy, S.: A novel hybrid model using teaching–learning-based optimization and a support vector machine for commodity futures index forecasting. Int. J. Mach. Learn. Cyber. 9, 97 (2018). https://doi.org/10.1007/s13042-015-0359-0
González-Álvarez, D.L., Vega-Rodríguez, M.A., Rubio-Largo, Á.: Finding patterns in protein sequences by using a hybrid multiobjective teaching learning based optimization algorithm. IEEE/ACM Trans. Comput. Biol. Bioinform. 12(3), 656–666 (2015)
Chen, D., Zou, F., Wang, J., et al.: A multi-class cooperative teaching–learning-based optimization algorithm with simulated annealing. Soft Comput. 20, 1921 (2016). https://doi.org/10.1007/s00500-015-1613-9
Zou, F., Wang, L., Hei, X., Chen, D., Jiang, Q., Li, H.: Bare-bones teaching-learning-based optimization. Sci. World J. 2014, 17p (2014). Article ID 136920. https://doi.org/10.1155/2014/136920
Ghasemi, M., Taghizadeh, M., Ghavidel, S., Aghaei, J., Abbasian, A.: Solving optimal reactive power dispatch problem using a novel teaching–learning-based optimization algorithm. Eng. Appl. Artif. Intell. 39, 100–108 (2015)
Wang, L., Zou, F., Hei, X., et al.: A hybridization of teaching–learning-based optimization and differential evolution for chaotic time series prediction. Neural Comput. Appl. 25, 1407 (2014). https://doi.org/10.1007/s00521-014-1627-8
Ghasemi, M., Ghanbarian, M.M., Ghavidel, S., Rahmani, S., Moghaddam, E.M.: Modified teaching learning algorithm and double differential evolution algorithm for optimal reactive power dispatch problem: a comparative study. Inf. Sci. 278, 231–249 (2014)
Zou, F., Wang, L., Chen, D., Hei, X.: An improved teaching-learning-based optimization with differential learning and its application. Math. Probl. Eng. 2015, 19p (2015). Article ID 754562. http://dx.doi.org/10.1155/2015/754562
Dib, F., Boumhidi, I.: Hybrid algorithm DE–TLBO for optimal H∞ and PID control for multi-machine power system. Int. J. Syst. Assur. Eng. Manag. (2017). https://doi.org/10.1007/s13198-016-0550-z
Turgut, O.E., Coban, M.T.: Optimal proton exchange membrane fuel cell modelling based on hybrid teaching learning based optimization–differential evolution algorithm. Ain Shams Eng. J. 7(1), 347–360 (2016)
Lim, W.H., Isa, N.A.M.: Teaching and peer-learning particle swarm optimization. Appl. Soft Comput. 18, 39–58 (2014)
Lim, W.H., Isa, N.A.M.: Bidirectional teaching and peer-learning particle swarm optimization. Inf. Sci. 280, 111–134 (2014)
Cheng, T., Chen, M., Fleming, P.J., et al.: A novel hybrid teaching learning based multi-objective particle swarm optimization. Neuro Comput. 222, 11–25 (2017)
Azizipanah-Abarghooee, R., Niknam, T., Bavafa, F., Zare, M.: Short-term scheduling of thermal power systems using hybrid gradient based modified teaching–learning optimizer with black hole algorithm. Electr. Power Syst. Res. 108, 16–34 (2014)
Güçyetmez, M., Çam, E.: A new hybrid algorithm with genetic-teaching learning optimization (G-TLBO) technique for optimizing of power flow in wind-thermal power systems. Electr. Eng. 98, 145 (2016). https://doi.org/10.1007/s00202-015-0357-y
Chen, X., Bin, X., Mei, C., Ding, Y., Li, K.: Teaching–learning–based artificial bee colony for solar photovoltaic parameter estimation. Appl. Energy 212, 1578–1588 (2018)
Tefek, M.F., Uğuz, H., Güçyetmez, M.: A new hybrid gravitational search–teaching–learning-based optimization method for energy demand estimation of Turkey. Neural Comput. Appl. (2017). https://doi.org/10.1007/s00521-017-3244-9
Huang, J., Gao, L., Li, X.: An effective teaching-learning-based cuckoo search algorithm for parameter optimization problems in structure designing and machining processes. Appl. Soft Comput. 36, 349–356 (2015)
Huang, J., Gao, L., Li, X.: A teaching–learning-based cuckoo search for constrained engineering design problems. Adv. Glob. Optim. (2015). https://doi.org/10.1007/978-3-319-08377-3_37
Tuo, S., Yong, L., Zhou, T.: An improved harmony search based on teaching-learning strategy for unconstrained optimization problems. Math. Probl. Eng. (2013). https://doi.org/10.1155/2013/413565
Mahdad, B., Srairi, K.: Optimal power flow improvement using a hybrid teaching-learning-based optimization and pattern search. Int. J. Mod. Educ. Comput. Sci. 10, 55–70 (2018). https://doi.org/10.5815/ijmecs.2018.03.07
Mohan, C., Shanker, K.: A random search technique for global optimization based on quadratic approximation. Asia Pac. J. Oper. Res. 11, 93–101 (1994)
Ali, M.M., Torn, A., Viitanen, S.: A numerical comparison of some modified controlled random search algorithms. J. Glob. Optim. 11, 377–385 (1997)
Venkata Rao, R., Patel, V.: Multi-objective optimization of heat exchangers using a modified teaching-learning-based optimization algorithm. Appl. Math. Model. 37, 1147–1162 (2013)
Venkata Rao, R., Patel, V.: Multi-objective optimization of two stage thermoelectric cooler using a modified teaching–learning-based optimization algorithm. Eng. Appl. Artif. Intell. 26, 430–445 (2013)
Crepinsek, M., Liu, S.-H., Mernik, M.: Exploration and exploitation in evolutionary algorithms: a survey. ACM Comput. Surv. (CSUR) 45(3), 35 (2013)
Civicioglu, P.: Backtracking search optimization algorithm for numerical optimization problems. Appl. Math. Comput. 219, 8121–8144 (2013)
Nasir, M., Das, S., Maity, D., Sengupta, S., Halder, U., Suganthan, P.N.: A dynamic neighborhood learning based particle swarm optimizer for global numerical optimization. Inf. Sci. 209, 16–36 (2012)
Das, S., Suganthan, P.N.: Problem definitions and evaluation criteria for CEC 2011 competition on testing evolutionary algorithms on real world optimization problems. Jadavpur University, Nanyang Technol. University, Kolkata, India, 2010
Acknowledgements
The authors would like to thank Dr. P. N. Suganthan, School of Electrical and Electronic Engineering, NTU, Singapore for shearing the source codes of PSO variants. Also thanks to the editors, anonymous referees for their valuable suggestion towards improving the book chapter.
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Nama, S., Saha, A.K., Sharma, S. (2020). A Hybrid TLBO Algorithm by Quadratic Approximation for Function Optimization and Its Application. In: Balas, V., Kumar, R., Srivastava, R. (eds) Recent Trends and Advances in Artificial Intelligence and Internet of Things. Intelligent Systems Reference Library, vol 172. Springer, Cham. https://doi.org/10.1007/978-3-030-32644-9_30
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