An improved teaching-learning-based optimization algorithm for numerical and engineering optimization problems (original) (raw)
References
Akay, B., & Karaboga, D. (2012). Artificial bee colony algorithm for large-scale problems and engineering design optimization. Journal of Intelligent Manufacturing, 23(4), 1001–1014. Article Google Scholar
Alatas, B. (2010). Chaotic bee colony algorithms for global numerical optimization. Expert Systems with Applications, 37(8), 5682–5687. Article Google Scholar
Amiri, B. (2012). Application of teaching-learning-based optimization algorithm on cluster analysis. Journal of Basic and Applied Scientific Research., 2(11), 11795–11802. Google Scholar
Baykasoğlu, A., Hamzadayi, A., & Köse, S. Y. (2014). Testing the performance of teaching-learning based optimization (TLBO) algorithm on combinatorial problems: Flow shop and job shop scheduling cases. Information Sciences. doi:10.1016/j.ins.2014.02.056.
Brajevic, I., & Tuba, M. (2013). An upgrade artificial bee colony algorithm for constrained optimization problems. Journal of Intelligent Manufacturing, 24(4), 729–740. Article Google Scholar
Coelho, L. D. S., Bora, T. C., & Lebensztajn, L. (2012). A chaotic approach of differential evolution optimization applied to loudspeaker design problem. IEEE Transactions on Magnetics, 48(2), 751–754. Article Google Scholar
Črepinšek, M., Liu, S. H., & Mernik, L. (2012). A note on teaching-learning-based optimization algorithm. Information Sciences, 212, 79–93.
Črepinšek, M., Liu, S. H., & Mernik, M. (2014). Replication and comparison of computational experiments in applied evolutionary computing: Common pitfalls and guidelines to avoid them. Applied Soft Computing, 19, 161–170. Article Google Scholar
Deb, K. (2000). An efficient constraint handling method for genetic algorithms. Computer Methods in Applied Mechanics and Engineering, 186(2–4), 311–338. Article Google Scholar
Dolgui, A., & Ofitserov, D. (1997). A stochastic method for discrete and continuous optimization in manufacturing systems. Journal of Intelligent Manufacturing, 8(5), 405–413. Article Google Scholar
Dorigo, M., Maniezzo, V., & Colorni, A. (1991). Positive feedback as a search strategy. Technical Report 91–016, Italy: Politecnico di Milano.
Geem, Z. W., Kim, J. H., & Loganathan, G. V. (2001). A new heuristic optimization algorithm: harmony search. Simulation, 76(2), 60–70. Article Google Scholar
He, Q., & Wang, L. (2007a). A hybrid particle swarm optimization with a feasibility-based rule for constrained optimization. Applied Mathematics & Computation, 186(2), 1407–1422. Article Google Scholar
He, Q., & Wang, L. (2007b). An effective co-evolutionary particle swarm optimization for constrained engineering design problems. Engineering Application of Artificial Intelligence, 20(1), 89–99. Article Google Scholar
He, S., Wu, Q. H., & Saunders, J. R. (2009). Group search optimizer: An optimization algorithm inspired by animal searching behavior. IEEE Transactions on Evolutionary Computation, 13(5), 973–990. Article Google Scholar
Holland, J. H. (1975). Adaptation in Natural and Artificial Systems. Ann Arbor: University of Michigan Press. Google Scholar
Huang, F. Z., Wang, L., & He, Q. (2007). An effective co-evolutionary differential evolution for constrained optimization. Applied Mathematics & Computation, 186(1), 340–356. Article Google Scholar
Karaboga, D. (2005). An Idea Based on Honey Bee Swarm for Numerical Optimization. Technical report-tr06, Erciyes University, Engineering Faculty, Computer Engineering Department, Voi 200
Karaboga, D., & Akay, B. (2009). Artificial bee colony (ABC), harmony search and bees algorithms on numerical optimization, Proceeding of IPROMS-2009 on Innovative Production Machines and Systems. UK: Cardiff. Google Scholar
Kennedy, J., & Eberhart, R. C. (1995). Particle swarm optimization. In Proceedings of 1995 IEEE International Conference on Neural Networks (pp. 1942-1948). Piscataway, NJ: IEEE Service Center.
Li, G. Q., Niu, P. F., & Xiao, X. J. (2012). Development and investigation of efficient artificial bee colony algorithm for numerical function optimization. Applied Soft Computing, 12(1), 320–332. Article Google Scholar
Liu, H., Cai, Z. X., & Wang, Y. (2010). Hybridizing particle swarm optimization with differential evolution for constrained numerical and engineering optimization. Applied Soft Computing, 10(2), 629–640. Article Google Scholar
Meeran, S., & Morshed, M. S. (2012). A hybrid genetic Tabu search algorithm for solving job shop scheduling problems: A case study. Journal of Intelligent Manufacturing, 23(4), 1063–1078. Article Google Scholar
Mohamed, A. W., & Sabry, H. Z. (2012). Constrained optimization based on modified differential evolution algorithm. Information Sciences, 194, 171–208. Article Google Scholar
Niknam, T., Azizipanah-Abarghooee, R., & Narimani, M. R. (2012a). A new multi objective optimization approach based on TLBO for location of automatic voltage regulators in distribution systems. Engineering Applications of Artificial Intelligence, 25(8), 1577– 1588. Article Google Scholar
Niknam, T., Golestaneh, F., & Sadeghi, M. S. (2012b). \(\theta \)-multi-objective teaching-learning-based optimization for dynamic economic emission dispatch. IEEE Systems Journal, 6(2), 341–352. Article Google Scholar
Perez, E., Posada, M., & Herrera, F. (2012). Analysis of new niching genetic algorithms for finding multiple solutions in the job shop scheduling. Journal of Intelligent Manufacturing, 23(3), 341–356. Article Google Scholar
Rao, R. V., & Patel, V. (2011). Thermodynamic optimization of plate-fin heat exchanger using teaching-learning-based optimization (TLBO) algorithm. The International Journal of Advanced Manufacturing Technology., 2, 91–96. Google Scholar
Rao, R. V., Savsani, V. J., & Vakharia, D. P. (2011). Teaching-learning-based optimization: A novel method for constrained mechanical design optimization problems. Computer-Aided Design, 43(3), 303–315. Article Google Scholar
Rao, R. V., & Patel, V. (2012). An elitist teaching-learning-based optimization algorithm for solving complex constrained optimization problem. International Journal of Industrial Engineering Computations, 3(4), 535–560. Article Google Scholar
Rao, R. V., & Kalyankar, V. D. (2012). Parameter optimization of modern machining processes using teaching-learning-based optimization algorithm. Engineering Applications of Artificial, 26(1), 524–531. Google Scholar
Rao, R. V., Savsani, V. J., & Vakharia, D. P. (2012). Teaching-learning-based optimization: An optimization method for continuous non-linear large scale problems. Information Sciences, 183, 1–15. Article Google Scholar
Rao, R. V., & Patel, V. (2013). An improved teaching-learning-based optimization algorithm for solving unconstrained optimization problems. Scientia Iranica, 20(3), 710–720. Google Scholar
Rao, R. V., & Kalyankar, V. D. (2013a). Parameter optimization of modern machining processes using teaching-learning-based optimization algorithm. Engineering Applications of Artificial Intelligence, 26(1), 524–531. Article Google Scholar
Rao, R. V., & Patel, V. (2013a). Multi-objective optimization of heat exchangers using a modified teaching-learning-based optimization algorithm. Applied Mathematical Modelling, 37(3), 1147–1162. Article Google Scholar
Rao, R. V., & Patel, V. (2013b). Multi-objective optimization of two stage thermoelectric coolers using a modified teaching-learning-based optimization algorithm. Engineering Applications of Artificial Intelligence, 26(1), 430–445. Article Google Scholar
Rao, R. V., & Kalyankar, V. D. (2013b). Multi-pass turning process parameter optimization using teaching-learning-based optimization algorithm. Scientia Iranica, 20(3), 967–974. Google Scholar
Ray, T., & Liew, K. M. (2003). Society and civilization: an optimization algorithm based on the simulation of social behavior. IEEE Transactions on Evolutionary Computation, 7(4), 386–396. Article Google Scholar
Roy, P. K., Sur, A., & Pradhan, D. K. (2013). Optimal short-term hydro-thermal scheduling using quasi-oppositional teaching learning based optimization. Engineering Applications of Artificial Intelligence, 26(10), 2516–2524. Article Google Scholar
Satapathy, S. C., & Naik, A. (2011). Data clustering based on teaching-learning-based optimization. Swarm, Evolutionary, and Memetic Computing Lecture Notes in Computer Science, 7077, 148–156. Article Google Scholar
Satapathy, S. C., & Naik, A. (2014). Modified Teaching-Learning-Based Optimization algorithm for global numerical optimization–A comparative study. Swarm and Evolutionary Computation, 16, 28–37. Article Google Scholar
Sauvey, C., & Sauer, N. (2012). A genetic algorithm with genes-association recognition for flowshop scheduling problems. Journal of Intelligent Manufacturing, 23(4), 1167–1177. Article Google Scholar
Storn, R., & Price, K. (1997). Differential evolution - a simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization, 11(4), 341–359. Article Google Scholar
Togan, V. (2012). Design of planar steel frames using teaching-learning based optimization. Engineering Structures, 34, 225–232. Article Google Scholar
Veček, N., Mernik, M., & Črepinšek, M. (2014). A chess rating system for evolutionary algorithms: A new method for the comparison and ranking of evolutionary algorithms. Information Sciences,. doi:10.1016/j.ins.2014.02.154. Google Scholar
Waghmare, G. (2013). Comments on “A note on teaching-learning-based optimization algorithm”. Information Sciences, 229(20), 159–169.
Wang, Y., Cai, Z. X., Zhou, Y. R., & Fan, Z. (2009). Constrained optimization based on hybrid evolutionary algorithm and adaptive constraint-handling technique. Structural Multidisciplinary Optimization, 37(4), 395–413. Article Google Scholar
Yildiz, A. R. (2009a). A novel particle swarm optimization approach for product design and manufacturing. International Journal of Advanced Manufacturing Technology, 40(5–6), 617–628. Article Google Scholar
Yildiz, A. R. (2009b). A novel hybrid immune algorithm for global optimization in design and manufacturing. Robotics and Computer-Integrated Manufacturing, 25(2), 261–270.
Yildiz, A. R. (2009c). Hybrid immune-simulated annealing algorithm for optimal design and manufacturing. International Journal of Materials and Product Technology, 34(3), 217–226.
Yildiz, A. R. (2009d). An effective hybrid immune-hill climbing optimization approach for solving design and manufacturing optimization problems in industry. Journal of Materials Processing Technology, 50(4), 224–228. Google Scholar
Yildiz, A. R., & Saitou, K. (2011). Topology synthesis of multi-component structural assemblies in continuum domains. ASME Journal of Mechanical Design, 133(1), 0110081–0110089. Article Google Scholar
Yildiz, A. R., & Solanki, K. N. (2012). Multi-objective optimization of vehicle crashworthiness using a new particle swarm based approach. International Journal of Advanced Manufacturing Technology, 59(1–4), 367–376. Article Google Scholar
Yildiz, A. R. (2012a). A comparative study of population-based optimization algorithms for turning operations. Information Sciences, 210, 81–88. Article Google Scholar
Yildiz, A. R. (2012b). A new hybrid particle swarm optimization approach for structural design optimization in automotive industry. Journal of Automobile Engineering, 226(10), 1340–1351. Article Google Scholar
Yildiz, A. R. (2013a). Comparison of evolutionary based optimization algorithms for structural design optimization. Engineering Applications of Artificial Intelligence, 26(1), 327–333. Article Google Scholar
Yildiz, A. R. (2013b). Optimization of cutting parameters in multi-pass turning using artificial bee colony-based approach. Information Sciences, 220, 399–407. Article Google Scholar
Yildiz, A. R. (2013c). A new hybrid differential evolution algorithm for the selection of optimal machining parameters in milling operations. Applied Soft Computing, 13(3), 1561–1566. Article Google Scholar
Yildiz, A. R. (2013d). Optimization of multi-pass turning operations using hybrid teaching learning-based approach. International Journal of Advanced Manufacturing Technology, 66(9–12), 1319–1326. Article Google Scholar
Zhang, M., Luo, W. J., & Wang, X. F. (2008). Differential evolution with dynamic stochastic selection for constrained optimization. Information Sciences, 178(15), 3043–3074. Article Google Scholar
Zou, F., Wang, L., Hei, X. L., Chen, D. B., & Yang, D. D., (2014). Teaching-learning-based optimization with dynamic group strategy for global optimization. Information Sciences,. doi:10.1016/j.ins.2014.03.038.