How to cite this paper
Nama, S & Saha, A. (2019). A novel hybrid backtracking search optimization algorithm for continuous function optimization.Decision Science Letters , 8(2), 163-174.
Refrences
Back, T. (1996). Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms. Oxford university press.
Beyer, H. G., & Arnold, D. V. (2001). Theory of evolution strategies—A tutorial. In Theoretical aspects of evolutionary computing (pp. 109-133). Springer, Berlin, Heidelberg.
Civicioglu, P. (2013). Backtracking search optimization algorithm for numerical optimization problems. Applied Mathematics and Computation, 219(15), 8121-8144.
Deep, K., & Das, K. N. (2008). Quadratic approximation based hybrid genetic algorithm for function optimization. Applied Mathematics and Computation, 203(1), 86-98.
Derrac, J., García, S., Molina, D., & Herrera, F. (2011). A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm and Evolutionary Computation, 1(1), 3-18.
Erol, O. K., & Eksin, I. (2006). A new optimization method: big bang–big crunch. Advances in Engineering Software, 37(2), 106-111.
Eskandar, H., Sadollah, A., Bahreininejad, A., & Hamdi, M. (2012). Water cycle algorithm–A novel metaheuristic optimization method for solving constrained engineering optimization problems. Computers & Structures, 110, 151-166.
Gandomi, A. H., Yang, X. S., & Alavi, A. H. (2011). Mixed variable structural optimization using firefly algorithm. Computers & Structures, 89(23-24), 2325-2336.
Gandomi, A. H., Talatahari, S., Yang, X. S., & Deb, S. (2013). Design optimization of truss structures using cuckoo search algorithm. The Structural Design of Tall and Special Buildings, 22(17), 1330-1349.
Gong, W., Cai, Z., & Ling, C. X. (2010). DE/BBO: a hybrid differential evolution with biogeography-based optimization for global numerical optimization. Soft Computing, 15(4), 645-665.
Holland, J. H. (1992). Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence. MIT press.
Kaveh, A., & Talatahari, S. (2010). A novel heuristic optimization method: charged system search. Acta Mechanica, 213(3-4), 267-289.
Kao, Y. T., & Zahara, E. (2008). A hybrid genetic algorithm and particle swarm optimization for multimodal functions. Applied Soft Computing, 8(2), 849-857.
Kennedy, J., & Eberhart, R. C. 1995. Particle Swarm Optimization. In IEEE International Conference on Neural Networks (Perth, Australia), IEEE Service Center, Piscataway, NJ (pp. 1942-1948).
Kundra, H., & Sood, M. (2010). Cross-country path finding using hybrid approach of PSO and BBO. International Journal of Computer Applications, 7(6), 15-19.
Li, X., Zhang, J., & Yin, M. (2014). Animal migration optimization: an optimization algorithm inspired by animal migration behavior. Neural Computing and Applications, 24(7-8), 1867-1877.
Liang, J. J., Qin, A. K., Suganthan, P. N., & Baskar, S. (2006). Comprehensive learning particle swarm optimizer for global optimization of multimodal functions. IEEE transactions on evolutionary computation, 10(3), 281-295.
Mahdavi, M., Fesanghary, M., & Damangir, E. (2007). An improved harmony search algorithm for solving optimization problems. Applied mathematics and computation, 188(2), 1567-1579.
Mendes, R., Kennedy, J., & Neves, J. (2004). The fully informed particle swarm: simpler, maybe better. IEEE transactions on evolutionary computation, 8(3), 204-210.
Mohan, C., & Shanker, K. (1994). A controlled random search technique for global optimization using quadratic approximation. Asia-Pacific Journal of Operational Research, 11(1), 93-101.
Nama, S., Saha, A., & Ghosh, S. (2016a). A new ensemble algorithm of differential evolution and backtracking search optimization algorithm with adaptive control parameter for function optimization. International Journal of Industrial Engineering Computations, 7(2), 323-338.
Nama, S., Saha, A., & Ghosh, S. (2016b). Improved symbiotic organisms search algorithm for solving unconstrained function optimization. Decision Science Letters, 5(3), 361-380.
Nama, S., Saha, A. K., & Ghosh, S. (2017c). A hybrid symbiosis organisms search algorithm and its application to real world problems. Memetic Computing, 9(3), 261-280.
Nama, S., Saha, A. K., & Ghosh, S. (2017d). Improved backtracking search algorithm for pseudo dynamic active earth pressure on retaining wall supporting c-Ф backfill. Applied Soft Computing, 52, 885-897.
Nama, S., & Saha, A. (2018a). An ensemble symbiosis organisms search algorithm and its application to real world problems. Decision Science Letters, 7(2), 103-118.
Nama, S., Saha, A.K., (2018b). A new hybrid differential evolution algorithm with self-adaptation for function optimization. Applied Intelligence, 48, 1657–1671.
Parsopoulos, K. E. (2004). UPSO: A unified particle swarm optimization scheme. Lecture Series on Computer and Computational Science, 1, 868-873.
Peram, T., Veeramachaneni, K., & Mohan, C. K. (2003, April). Fitness-distance-ratio based particle swarm optimization. In Swarm Intelligence Symposium, 2003. SIS'03. Proceedings of the 2003 IEEE (pp. 174-181). IEEE.
Sadollah, A., Bahreininejad, A., Eskandar, H., & Hamdi, M. (2013). Mine blast algorithm: A new population based algorithm for solving constrained engineering optimization problems. Applied Soft Computing, 13(5), 2592-2612.
Nemati, S., Basiri, M. E., Ghasem-Aghaee, N., & Aghdam, M. H. (2009). A novel ACO–GA hybrid algorithm for feature selection in protein function prediction. Expert systems with applications, 36(10), 12086-12094.
Simon, D. (2008). Biogeography-based optimization. IEEE transactions on evolutionary computation, 12(6), 702-713.
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.
Van den Bergh, F., & Engelbrecht, A. P. (2004). A cooperative approach to particle swarm optimization. IEEE transactions on evolutionary computation, 8(3), 225-239.
Wolpert, D. H., & Macready, W. G. (1997). No free lunch theorems for optimization. IEEE transactions on evolutionary computation, 1(1), 67-82.
Yang, X. S., & Press, L. (2010). Nature-Inspired Metaheuristic Algorithms Second Edition.
Yildiz, A. R. (2013). Hybrid Taguchi-differential evolution algorithm for optimization of multi-pass turning operations. Applied Soft Computing, 13(3), 1433-1439.
Zhang, X., & Tang, L. (2009). A new hybrid ant colony optimization algorithm for the vehicle routing problem. Pattern Recognition Letters, 30(9), 848-855.
Zhang, G., Shao, X., Li, P., & Gao, L. (2009). An effective hybrid particle swarm optimization algorithm for multi-objective flexible job-shop scheduling problem. Computers & Industrial Engineering, 56(4), 1309-1318.
Beyer, H. G., & Arnold, D. V. (2001). Theory of evolution strategies—A tutorial. In Theoretical aspects of evolutionary computing (pp. 109-133). Springer, Berlin, Heidelberg.
Civicioglu, P. (2013). Backtracking search optimization algorithm for numerical optimization problems. Applied Mathematics and Computation, 219(15), 8121-8144.
Deep, K., & Das, K. N. (2008). Quadratic approximation based hybrid genetic algorithm for function optimization. Applied Mathematics and Computation, 203(1), 86-98.
Derrac, J., García, S., Molina, D., & Herrera, F. (2011). A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm and Evolutionary Computation, 1(1), 3-18.
Erol, O. K., & Eksin, I. (2006). A new optimization method: big bang–big crunch. Advances in Engineering Software, 37(2), 106-111.
Eskandar, H., Sadollah, A., Bahreininejad, A., & Hamdi, M. (2012). Water cycle algorithm–A novel metaheuristic optimization method for solving constrained engineering optimization problems. Computers & Structures, 110, 151-166.
Gandomi, A. H., Yang, X. S., & Alavi, A. H. (2011). Mixed variable structural optimization using firefly algorithm. Computers & Structures, 89(23-24), 2325-2336.
Gandomi, A. H., Talatahari, S., Yang, X. S., & Deb, S. (2013). Design optimization of truss structures using cuckoo search algorithm. The Structural Design of Tall and Special Buildings, 22(17), 1330-1349.
Gong, W., Cai, Z., & Ling, C. X. (2010). DE/BBO: a hybrid differential evolution with biogeography-based optimization for global numerical optimization. Soft Computing, 15(4), 645-665.
Holland, J. H. (1992). Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence. MIT press.
Kaveh, A., & Talatahari, S. (2010). A novel heuristic optimization method: charged system search. Acta Mechanica, 213(3-4), 267-289.
Kao, Y. T., & Zahara, E. (2008). A hybrid genetic algorithm and particle swarm optimization for multimodal functions. Applied Soft Computing, 8(2), 849-857.
Kennedy, J., & Eberhart, R. C. 1995. Particle Swarm Optimization. In IEEE International Conference on Neural Networks (Perth, Australia), IEEE Service Center, Piscataway, NJ (pp. 1942-1948).
Kundra, H., & Sood, M. (2010). Cross-country path finding using hybrid approach of PSO and BBO. International Journal of Computer Applications, 7(6), 15-19.
Li, X., Zhang, J., & Yin, M. (2014). Animal migration optimization: an optimization algorithm inspired by animal migration behavior. Neural Computing and Applications, 24(7-8), 1867-1877.
Liang, J. J., Qin, A. K., Suganthan, P. N., & Baskar, S. (2006). Comprehensive learning particle swarm optimizer for global optimization of multimodal functions. IEEE transactions on evolutionary computation, 10(3), 281-295.
Mahdavi, M., Fesanghary, M., & Damangir, E. (2007). An improved harmony search algorithm for solving optimization problems. Applied mathematics and computation, 188(2), 1567-1579.
Mendes, R., Kennedy, J., & Neves, J. (2004). The fully informed particle swarm: simpler, maybe better. IEEE transactions on evolutionary computation, 8(3), 204-210.
Mohan, C., & Shanker, K. (1994). A controlled random search technique for global optimization using quadratic approximation. Asia-Pacific Journal of Operational Research, 11(1), 93-101.
Nama, S., Saha, A., & Ghosh, S. (2016a). A new ensemble algorithm of differential evolution and backtracking search optimization algorithm with adaptive control parameter for function optimization. International Journal of Industrial Engineering Computations, 7(2), 323-338.
Nama, S., Saha, A., & Ghosh, S. (2016b). Improved symbiotic organisms search algorithm for solving unconstrained function optimization. Decision Science Letters, 5(3), 361-380.
Nama, S., Saha, A. K., & Ghosh, S. (2017c). A hybrid symbiosis organisms search algorithm and its application to real world problems. Memetic Computing, 9(3), 261-280.
Nama, S., Saha, A. K., & Ghosh, S. (2017d). Improved backtracking search algorithm for pseudo dynamic active earth pressure on retaining wall supporting c-Ф backfill. Applied Soft Computing, 52, 885-897.
Nama, S., & Saha, A. (2018a). An ensemble symbiosis organisms search algorithm and its application to real world problems. Decision Science Letters, 7(2), 103-118.
Nama, S., Saha, A.K., (2018b). A new hybrid differential evolution algorithm with self-adaptation for function optimization. Applied Intelligence, 48, 1657–1671.
Parsopoulos, K. E. (2004). UPSO: A unified particle swarm optimization scheme. Lecture Series on Computer and Computational Science, 1, 868-873.
Peram, T., Veeramachaneni, K., & Mohan, C. K. (2003, April). Fitness-distance-ratio based particle swarm optimization. In Swarm Intelligence Symposium, 2003. SIS'03. Proceedings of the 2003 IEEE (pp. 174-181). IEEE.
Sadollah, A., Bahreininejad, A., Eskandar, H., & Hamdi, M. (2013). Mine blast algorithm: A new population based algorithm for solving constrained engineering optimization problems. Applied Soft Computing, 13(5), 2592-2612.
Nemati, S., Basiri, M. E., Ghasem-Aghaee, N., & Aghdam, M. H. (2009). A novel ACO–GA hybrid algorithm for feature selection in protein function prediction. Expert systems with applications, 36(10), 12086-12094.
Simon, D. (2008). Biogeography-based optimization. IEEE transactions on evolutionary computation, 12(6), 702-713.
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.
Van den Bergh, F., & Engelbrecht, A. P. (2004). A cooperative approach to particle swarm optimization. IEEE transactions on evolutionary computation, 8(3), 225-239.
Wolpert, D. H., & Macready, W. G. (1997). No free lunch theorems for optimization. IEEE transactions on evolutionary computation, 1(1), 67-82.
Yang, X. S., & Press, L. (2010). Nature-Inspired Metaheuristic Algorithms Second Edition.
Yildiz, A. R. (2013). Hybrid Taguchi-differential evolution algorithm for optimization of multi-pass turning operations. Applied Soft Computing, 13(3), 1433-1439.
Zhang, X., & Tang, L. (2009). A new hybrid ant colony optimization algorithm for the vehicle routing problem. Pattern Recognition Letters, 30(9), 848-855.
Zhang, G., Shao, X., Li, P., & Gao, L. (2009). An effective hybrid particle swarm optimization algorithm for multi-objective flexible job-shop scheduling problem. Computers & Industrial Engineering, 56(4), 1309-1318.