How to cite this paper
Nama, S., Saha, A & Ghosh, S. (2016). 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.
Refrences
Behnamian, J., Zandieh, M., & Ghomi, S. F. (2009). Parallel-machine scheduling problems with sequence-dependent setup times using an ACO, SA and VNS hybrid algorithm. Expert Systems with Applications, 36(6), 9637-9644.
Blum, C. (2005). Ant colony optimization: Introduction and recent trends.Physics of Life reviews, 2(4), 353-373.
Civicioglu, P. (2013). Backtracking search optimization algorithm for numerical optimization problems. Applied Mathematics and Computation, 219(15), 8121-8144.
Das, S. K. (2005). Slope stability analysis using genetic algorithm. The Electronic Journal of Geotechnical Engineering, 10, 429-439.
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.
Fan, S. K. S., & Zahara, E. (2007). A hybrid simplex search and particle swarm optimization for unconstrained optimization. European Journal of Operational Research, 181(2), 527-548.
G?mperle, R., Müller, S. D., & Koumoutsakos, P. (2002). A parameter study for differential evolution. Advances in Intelligent Systems, Fuzzy Systems, Evolutionary Computation, 10, 293-298.
Glover, F., & Laguna, M. (1997). Tabu Search, Kluwer Academic Publishers, Norwell, MA.
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.
Guo, W., Li, W., Zhang, Q., Wang, L., Wu, Q., & Ren, H. (2014). Biogeography-based particle swarm optimization with fuzzy elitism and its applications to constrained engineering problems. Engineering Optimization, 46(11), 1465-1484.
Zhang, J. R., Zhang, J., Lok, T. M., & Lyu, M. R. (2007). A hybrid particle swarm optimization–back-propagation algorithm for feedforward neural network training. Applied Mathematics and Computation, 185(2), 1026-1037.
Kennedy, J., & Eberhart, R.C. (1995). Particle swarm optimization, Proceedings of the 1995 IEEE International Conference on Neural Networks (Perth, Australia, IEEE Service Center, Piscataway, NJ, 1995), vol. 4, pp. 1942-1948.
Lampinen, J., & Zelinka, I. (2000). On stagnation of the differential evolution algorithm, in: Proceedings of MENDEL 2000, 6th International Mendel Conference on Soft Computing, pp. 76–83.
Lee, K. S., & Geem, Z. W. (2005). A new meta-heuristic algorithm for continuous engineering optimization: harmony search theory and practice.Computer Methods in Applied Mechanics and Engineering, 194(36), 3902-3933.
Liang, J. J., Qin, A. K., Suganthan, P. N., & Baskar, S. (2006). Comprehensive learning particle swarm optimizer for global optimization of multimodal functions. Evolutionary Computation, IEEE Transactions on, 10(3), 281-295.
Lin, W. Y. (2010). A GA–DE hybrid evolutionary algorithm for path synthesis of four-bar linkage. Mechanism and Machine Theory, 45(8), 1096-1107.
Liu, H., Cai, Z., & Wang, Y. (2010). Hybridizing particle swarm optimization with differential evolution for constrained numerical and engineering optimization.Applied Soft Computing, 10(2), 629-640.
Kao, Y. T., & Zahara, E. (2008). A hybrid genetic algorithm and particle swarm optimization for multimodal functions. Applied Soft Computing, 8(2), 849-857.
Mallipeddi, R., Suganthan, P. N., Pan, Q. K., & Tasgetiren, M. F. (2011). Differential evolution algorithm with ensemble of parameters and mutation strategies. Applied Soft Computing, 11(2), 1679-1696.
Nama, S., Saha, A. K., & Ghosh, S. (2015). Parameters Optimization of Geotechnical Problem Using Different Optimization Algorithm, vol-33, Geotechnical and Geological Engineering, DOI 10.1007/s10706-015-9898-0
Nourelfath, M., Nahas, N., & Montreuil, B. (2007). Coupling ant colony optimization and the extended great deluge algorithm for the discrete facility layout problem. Engineering Optimization, 39(8), 953-968.
Parouha, R. P., Das, K. N., (2015), An efficient hybrid technique for numerical optimization and applications, Computers & Industrial Engineering, 83, 193–216.
Parsopoulos, K. E., & Vrahatis, M. N. (2004). UPSO: A unified particle swarm optimization scheme. Lecture Series on Computer and Computational Sciences,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 & apos; 03. Proceedings of the 2003 IEEE (pp. 174-181). IEEE.
Rahnamayan, S., Tizhoosh, H. R., & Salama, M. (2008). Opposition-based differential evolution. Evolutionary Computation, IEEE Transactions on, 12(1), 64-79.
Rao, R.V., & Savsani, V.J. (2012). Mechanical Design Optimization using Advanced Optimization Technique, Springer Series in Advanced Manufacturing, Springer, London, Heidelberg.
Ronkkonen, J., Kukkonen, S., & Price, K. V. (2005, September). Real-parameter optimization with differential evolution. In Proc. IEEE CEC (Vol. 1, pp. 506-513).
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.
Sengupta, A., & Upadhyay, A. (2009). Locating the critical failure surface in a slope stability analysis by genetic algorithm. Applied Soft Computing, 9(1), 387-392.
Shojaeefard, M. H., Khalkhali, A., Akbari, M., & Tahani, M. (2013). Application of Taguchi optimization technique in determining aluminum to brass friction stir welding parameters. Materials & Design, 52, 587-592.
?muc, T. (2002). Improving convergence properties of the differential evolution algorithm. Matou?ek and O?mera [529], 80-86.
Storn, R.(1996). On the usage of differential evolution for function optimization, in: Biennial Conference of the North American Fuzzy Information Processing Society (NAFIPS), IEEE, Berkeley, pp. 519–523.
Storn, R., & Price, K. (1995). Differential evolution-a simple and efficient adaptive scheme for global optimization over continuous spaces (Vol. 3). Berkeley: ICSI.
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.
Suganthan, P. N., Hansen, N., Liang, J. J., Deb, K., Chen, Y. P., Auger, A., & Tiwari, S. (2005). Problem definitions and evaluation criteria for the CEC 2005 special session on real-parameter optimization. KanGAL report, 2005005.
Tizhoosh, H.R. (2005). Opposition-based learning: a new scheme for machine intelligence. International conference on computational intelligence for modelling control and automation; Austria. p. 695–701.
Van den Bergh, F., & Engelbrecht, A. P. (2004). A cooperative approach to particle swarm optimization. Evolutionary Computation, IEEE Transactions on,8(3), 225-239.
Zaharie, D. (2009). Influence of crossover on the behavior of differential evolution algorithms. Applied Soft Computing, 9(3), 1126-1138.
Zolfaghari, A. R., Heath, A. C., & McCombie, P. F. (2005). Simple genetic algorithm search for critical non-circular failure surface in slope stability analysis. Computers and Geotechnics, 32(3), 139-152.
Rao, R., & Patel, V. (2013). Comparative performance of an elitist teaching-learning-based optimization algorithm for solving unconstrained optimization problems. International Journal of Industrial Engineering Computations, 4(1), 29-50.
Rao, R., & Patel, V. (2012). An elitist teaching-learning-based optimization algorithm for solving complex constrained optimization problems. International Journal of Industrial Engineering Computations, 3(4), 535-560.
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.
Blum, C. (2005). Ant colony optimization: Introduction and recent trends.Physics of Life reviews, 2(4), 353-373.
Civicioglu, P. (2013). Backtracking search optimization algorithm for numerical optimization problems. Applied Mathematics and Computation, 219(15), 8121-8144.
Das, S. K. (2005). Slope stability analysis using genetic algorithm. The Electronic Journal of Geotechnical Engineering, 10, 429-439.
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.
Fan, S. K. S., & Zahara, E. (2007). A hybrid simplex search and particle swarm optimization for unconstrained optimization. European Journal of Operational Research, 181(2), 527-548.
G?mperle, R., Müller, S. D., & Koumoutsakos, P. (2002). A parameter study for differential evolution. Advances in Intelligent Systems, Fuzzy Systems, Evolutionary Computation, 10, 293-298.
Glover, F., & Laguna, M. (1997). Tabu Search, Kluwer Academic Publishers, Norwell, MA.
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.
Guo, W., Li, W., Zhang, Q., Wang, L., Wu, Q., & Ren, H. (2014). Biogeography-based particle swarm optimization with fuzzy elitism and its applications to constrained engineering problems. Engineering Optimization, 46(11), 1465-1484.
Zhang, J. R., Zhang, J., Lok, T. M., & Lyu, M. R. (2007). A hybrid particle swarm optimization–back-propagation algorithm for feedforward neural network training. Applied Mathematics and Computation, 185(2), 1026-1037.
Kennedy, J., & Eberhart, R.C. (1995). Particle swarm optimization, Proceedings of the 1995 IEEE International Conference on Neural Networks (Perth, Australia, IEEE Service Center, Piscataway, NJ, 1995), vol. 4, pp. 1942-1948.
Lampinen, J., & Zelinka, I. (2000). On stagnation of the differential evolution algorithm, in: Proceedings of MENDEL 2000, 6th International Mendel Conference on Soft Computing, pp. 76–83.
Lee, K. S., & Geem, Z. W. (2005). A new meta-heuristic algorithm for continuous engineering optimization: harmony search theory and practice.Computer Methods in Applied Mechanics and Engineering, 194(36), 3902-3933.
Liang, J. J., Qin, A. K., Suganthan, P. N., & Baskar, S. (2006). Comprehensive learning particle swarm optimizer for global optimization of multimodal functions. Evolutionary Computation, IEEE Transactions on, 10(3), 281-295.
Lin, W. Y. (2010). A GA–DE hybrid evolutionary algorithm for path synthesis of four-bar linkage. Mechanism and Machine Theory, 45(8), 1096-1107.
Liu, H., Cai, Z., & Wang, Y. (2010). Hybridizing particle swarm optimization with differential evolution for constrained numerical and engineering optimization.Applied Soft Computing, 10(2), 629-640.
Kao, Y. T., & Zahara, E. (2008). A hybrid genetic algorithm and particle swarm optimization for multimodal functions. Applied Soft Computing, 8(2), 849-857.
Mallipeddi, R., Suganthan, P. N., Pan, Q. K., & Tasgetiren, M. F. (2011). Differential evolution algorithm with ensemble of parameters and mutation strategies. Applied Soft Computing, 11(2), 1679-1696.
Nama, S., Saha, A. K., & Ghosh, S. (2015). Parameters Optimization of Geotechnical Problem Using Different Optimization Algorithm, vol-33, Geotechnical and Geological Engineering, DOI 10.1007/s10706-015-9898-0
Nourelfath, M., Nahas, N., & Montreuil, B. (2007). Coupling ant colony optimization and the extended great deluge algorithm for the discrete facility layout problem. Engineering Optimization, 39(8), 953-968.
Parouha, R. P., Das, K. N., (2015), An efficient hybrid technique for numerical optimization and applications, Computers & Industrial Engineering, 83, 193–216.
Parsopoulos, K. E., & Vrahatis, M. N. (2004). UPSO: A unified particle swarm optimization scheme. Lecture Series on Computer and Computational Sciences,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 & apos; 03. Proceedings of the 2003 IEEE (pp. 174-181). IEEE.
Rahnamayan, S., Tizhoosh, H. R., & Salama, M. (2008). Opposition-based differential evolution. Evolutionary Computation, IEEE Transactions on, 12(1), 64-79.
Rao, R.V., & Savsani, V.J. (2012). Mechanical Design Optimization using Advanced Optimization Technique, Springer Series in Advanced Manufacturing, Springer, London, Heidelberg.
Ronkkonen, J., Kukkonen, S., & Price, K. V. (2005, September). Real-parameter optimization with differential evolution. In Proc. IEEE CEC (Vol. 1, pp. 506-513).
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.
Sengupta, A., & Upadhyay, A. (2009). Locating the critical failure surface in a slope stability analysis by genetic algorithm. Applied Soft Computing, 9(1), 387-392.
Shojaeefard, M. H., Khalkhali, A., Akbari, M., & Tahani, M. (2013). Application of Taguchi optimization technique in determining aluminum to brass friction stir welding parameters. Materials & Design, 52, 587-592.
?muc, T. (2002). Improving convergence properties of the differential evolution algorithm. Matou?ek and O?mera [529], 80-86.
Storn, R.(1996). On the usage of differential evolution for function optimization, in: Biennial Conference of the North American Fuzzy Information Processing Society (NAFIPS), IEEE, Berkeley, pp. 519–523.
Storn, R., & Price, K. (1995). Differential evolution-a simple and efficient adaptive scheme for global optimization over continuous spaces (Vol. 3). Berkeley: ICSI.
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.
Suganthan, P. N., Hansen, N., Liang, J. J., Deb, K., Chen, Y. P., Auger, A., & Tiwari, S. (2005). Problem definitions and evaluation criteria for the CEC 2005 special session on real-parameter optimization. KanGAL report, 2005005.
Tizhoosh, H.R. (2005). Opposition-based learning: a new scheme for machine intelligence. International conference on computational intelligence for modelling control and automation; Austria. p. 695–701.
Van den Bergh, F., & Engelbrecht, A. P. (2004). A cooperative approach to particle swarm optimization. Evolutionary Computation, IEEE Transactions on,8(3), 225-239.
Zaharie, D. (2009). Influence of crossover on the behavior of differential evolution algorithms. Applied Soft Computing, 9(3), 1126-1138.
Zolfaghari, A. R., Heath, A. C., & McCombie, P. F. (2005). Simple genetic algorithm search for critical non-circular failure surface in slope stability analysis. Computers and Geotechnics, 32(3), 139-152.
Rao, R., & Patel, V. (2013). Comparative performance of an elitist teaching-learning-based optimization algorithm for solving unconstrained optimization problems. International Journal of Industrial Engineering Computations, 4(1), 29-50.
Rao, R., & Patel, V. (2012). An elitist teaching-learning-based optimization algorithm for solving complex constrained optimization problems. International Journal of Industrial Engineering Computations, 3(4), 535-560.
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.