How to cite this paper
Nama, S & Saha, A. (2018). An ensemble symbiosis organisms search algorithm and its application to real world problems.Decision Science Letters , 7(2), 103-118.
Refrences
Abdullahi, M., & Ngadi, M. A. (2016). Symbiotic Organism Search optimization based task scheduling in cloud computing environment. Future Generation Computer Systems, 56, 640-650.
Beighter, C.S., & Phillips, D.T. (1976). Applied Geometric Programming. John Wiley and Sons.
Cheng, M. Y., Prayogo, D., & Tran, D. H. (2015). Optimizing multiple-resources leveling in multiple projects using discrete symbiotic organisms search. Journal of Computing in Civil Engineering, 30(3), 04015036.
Cheng, M. Y., & Prayogo, D. (2014). Symbiotic organisms search: a new metaheuristic optimization algorithm. Computers & Structures, 139, 98-112.
Črepinšek, M., Liu, S. H., & Mernik, M. (2013). Exploration and exploitation in evolutionary algorithms: A survey. ACM Computing Surveys (CSUR), 45(3), 35.
Das, K. N., & Singh, T. K. (2014). Drosophila food-search optimization. Applied mathematics and Computation, 231, 566-580.
Dorigo, M., & Stützle, T. (2004). Ant colony optimization. Bradford Company.
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.
Gao, W. F., Liu, S. Y., & Huang, L. L. (2012). Particle swarm optimization with chaotic opposition-based population initialization and stochastic search technique. Communications in Nonlinear Science and Numerical Simulation, 17(11), 4316-4327.
Geem, Z. W., Kim, J. H., & Loganathan, G. V. (2001). A new heuristic optimization algorithm: harmony search. Simulation, 76(2), 60-68.
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. (1975). Adaptation in natural and artificial systems. University of Michigan Press, USA.
Karaboga, D., & Basturk, B. (2007). A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. Journal of global optimization, 39(3), 459-471.
Kennedy, J., & Eberhart, R. (1995). Particle swarm optimization. In: Proceedings of the IEEE international conference on neural networks. Perth, Australia;. p. 1942– 1948.
Kennedy, J., & Mendes, R. (2002), Population structure and particle swarm performance, In: IEEE international conference evolutionary computation, Honolulu, HI, p. 1671–1676.
Kennedy, J., & Mendes, R., (2006), Neighborhood topologies in fully informed and best-of neighborhood particle swarms. IEEE Transactions Systems, Man, and Cybernetics, Part C, 36(4), 515–519.
Lee, K. S., & Geem, Z. W. (2004). A new structural optimization method based on the harmony search algorithm. Computers & structures, 82(9), 781-798.
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 Evolutionary Computation, 10(3), 281–295.
Liang, J.J., & Suganthan, P.N. (2005). Dynamic multi-swarm particle swarm optimizer, In: IEEE swarm intelligence symposium, p. 124–129.
Lee, K. S., & Geem, Z. W. (2004). A new structural optimization method based on the harmony search algorithm. Computers & structures, 82(9), 781-798.
Mendes, R., Kennedy, J., Neves, J., (2004), The fully informed particle swarm: simpler maybe better. IEEE Transactions Evolutionary Computation, 8(3), 204–210.
Nama, S., Saha, A. K., & Ghosh, S. (2015a). Parameters optimization of geotechnical problem using different optimization algorithm. Geotechnical and Geological Engineering, 33(5), 1235-1253.
Nama, S., Saha, A.K., & Ghosh, S. (2016b). 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. (2016c). Improved symbiotic organisms search algorithm for solving unconstrained function optimization. Decision Science Letters, 5(3), 361-380.
Nama, S., Saha, A.K., and Ghosh, S. (2016d). A hybrid symbiosis organisms search algorithm and its application to real world problems. Memetic Computing (DOI: 10.1007/s12293-016-0194-1)
Nama, S., Saha, A. K., & Ghosh, S. (2017). Improved backtracking search algorithm for pseudo dynamic active earth pressure on retaining wall supporting c-Ф backfill. Applied Soft Computing, 52, 885-897.
Osman, I.H., & Laporte, G. (1996). Metaheuristics: a bibliography. Annals of Operations Research, 63,511-623
Pham, D.T., Ghanbarzadeh, A., Koc, E., Otri, S., Rahim, S., & Zaidi, M. (2006), The Bees algorithm, a novel tool for complex optimization problems. In: Proceedings of the 2nd international virtual conference on intelligent production machines and systems (IPROMS 2006). Elsevier: Oxford; p. 454–9.
Prasad, D., & Mukherjee, V. (2015). A novel symbiotic organisms search algorithm for optimal power flow of power system with FACTS devices. Engineering Science and Technology, an International Journal, 19(1), 79-89.
Xu, Q., Wang, L., He, B. M., & Wang, N. (2011). Modified opposition-based differential evolution for function optimization. Journal of Computational Information Systems, 7(5), 1582-1591.
Roy, P.K., & Mandal, D. (2011). Quasi-oppositional biogeography-based optimization for multi-objective optimal power flow. Electric Power Components and Systems, 40(2), 236–56.
Roy, P.K., & Mandal, D. (2014). Oppositional biogeography-based optimization for optimal power flow. International Journal of Power Energy Conversion, 5(1), 47–69.
Sadollah, A., Bahreininejad, A., Eskandar, H., Hamdi, M., (2012), Mine blast algorithm for optimization of truss structures with discrete variables. Computers and Structures, 102,49–63
Sapp, J. (1994). Evolution by association: a history of symbiosis: a history of symbiosis. New York: Oxford University Press, USA.
Shi, Y., & Eberhart, R. (1998). A modified particle swarm optimizer, In: IEEE international conference on computational intelligence, p. 69–73.
Storn, R., Price , K., (1997), Differential evolution – a simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization, 11, 341–59.
Sultana, S., & Roy, P.K. (2014), Multi-objective quasi-oppositional teaching learning based optimization for optimal location of distributed generator in radial distribution systems, Electrical Power and Energy Systems, 63, 534–545.
Tizhoosh, H.R. (2005). Opposition-based learning: a new scheme for machine intelligence. In: International conference on computational intelligence for modelling control and automation; Austria: p. 695–01.
Verma, S., Saha, S., & Mukherjee, V. (2017). A novel symbiotic organisms search algorithm for congestion management in deregulated environment. Journal of Experimental & Theoretical Artificial Intelligence, 29(1), 59-79.
Wang, H., Wu, Z., Rahnamayan, S., Liu, Y., & Ventresca, M. (2011). Enhancing particle swarm optimization using generalized opposition-based learning. Information Sciences, 181(20), 4699-4714.
Yang, X-S. (2009). Firefly algorithms for multimodal optimization. In: Proceedings of the 5th international conference on stochastic algorithms: foundations and applications. Sapporo, Japan: Springer-Verlag, 169–78.
Yang, X-S., & Deb, S. (2009), Cuckoo search via levy flights. In: Proceedings of the world congress on nature & biologically inspired computing (NaBIC-2009). Coimbatore, India. p. 210–214.
Zhan, Z.H., Zhang, J., Li, Y., & Chung, H.H. (2009), Adaptive particle swarm optimization. IEEE Transactions B, 39(6), 1362–1381.
Beighter, C.S., & Phillips, D.T. (1976). Applied Geometric Programming. John Wiley and Sons.
Cheng, M. Y., Prayogo, D., & Tran, D. H. (2015). Optimizing multiple-resources leveling in multiple projects using discrete symbiotic organisms search. Journal of Computing in Civil Engineering, 30(3), 04015036.
Cheng, M. Y., & Prayogo, D. (2014). Symbiotic organisms search: a new metaheuristic optimization algorithm. Computers & Structures, 139, 98-112.
Črepinšek, M., Liu, S. H., & Mernik, M. (2013). Exploration and exploitation in evolutionary algorithms: A survey. ACM Computing Surveys (CSUR), 45(3), 35.
Das, K. N., & Singh, T. K. (2014). Drosophila food-search optimization. Applied mathematics and Computation, 231, 566-580.
Dorigo, M., & Stützle, T. (2004). Ant colony optimization. Bradford Company.
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.
Gao, W. F., Liu, S. Y., & Huang, L. L. (2012). Particle swarm optimization with chaotic opposition-based population initialization and stochastic search technique. Communications in Nonlinear Science and Numerical Simulation, 17(11), 4316-4327.
Geem, Z. W., Kim, J. H., & Loganathan, G. V. (2001). A new heuristic optimization algorithm: harmony search. Simulation, 76(2), 60-68.
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. (1975). Adaptation in natural and artificial systems. University of Michigan Press, USA.
Karaboga, D., & Basturk, B. (2007). A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. Journal of global optimization, 39(3), 459-471.
Kennedy, J., & Eberhart, R. (1995). Particle swarm optimization. In: Proceedings of the IEEE international conference on neural networks. Perth, Australia;. p. 1942– 1948.
Kennedy, J., & Mendes, R. (2002), Population structure and particle swarm performance, In: IEEE international conference evolutionary computation, Honolulu, HI, p. 1671–1676.
Kennedy, J., & Mendes, R., (2006), Neighborhood topologies in fully informed and best-of neighborhood particle swarms. IEEE Transactions Systems, Man, and Cybernetics, Part C, 36(4), 515–519.
Lee, K. S., & Geem, Z. W. (2004). A new structural optimization method based on the harmony search algorithm. Computers & structures, 82(9), 781-798.
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 Evolutionary Computation, 10(3), 281–295.
Liang, J.J., & Suganthan, P.N. (2005). Dynamic multi-swarm particle swarm optimizer, In: IEEE swarm intelligence symposium, p. 124–129.
Lee, K. S., & Geem, Z. W. (2004). A new structural optimization method based on the harmony search algorithm. Computers & structures, 82(9), 781-798.
Mendes, R., Kennedy, J., Neves, J., (2004), The fully informed particle swarm: simpler maybe better. IEEE Transactions Evolutionary Computation, 8(3), 204–210.
Nama, S., Saha, A. K., & Ghosh, S. (2015a). Parameters optimization of geotechnical problem using different optimization algorithm. Geotechnical and Geological Engineering, 33(5), 1235-1253.
Nama, S., Saha, A.K., & Ghosh, S. (2016b). 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. (2016c). Improved symbiotic organisms search algorithm for solving unconstrained function optimization. Decision Science Letters, 5(3), 361-380.
Nama, S., Saha, A.K., and Ghosh, S. (2016d). A hybrid symbiosis organisms search algorithm and its application to real world problems. Memetic Computing (DOI: 10.1007/s12293-016-0194-1)
Nama, S., Saha, A. K., & Ghosh, S. (2017). Improved backtracking search algorithm for pseudo dynamic active earth pressure on retaining wall supporting c-Ф backfill. Applied Soft Computing, 52, 885-897.
Osman, I.H., & Laporte, G. (1996). Metaheuristics: a bibliography. Annals of Operations Research, 63,511-623
Pham, D.T., Ghanbarzadeh, A., Koc, E., Otri, S., Rahim, S., & Zaidi, M. (2006), The Bees algorithm, a novel tool for complex optimization problems. In: Proceedings of the 2nd international virtual conference on intelligent production machines and systems (IPROMS 2006). Elsevier: Oxford; p. 454–9.
Prasad, D., & Mukherjee, V. (2015). A novel symbiotic organisms search algorithm for optimal power flow of power system with FACTS devices. Engineering Science and Technology, an International Journal, 19(1), 79-89.
Xu, Q., Wang, L., He, B. M., & Wang, N. (2011). Modified opposition-based differential evolution for function optimization. Journal of Computational Information Systems, 7(5), 1582-1591.
Roy, P.K., & Mandal, D. (2011). Quasi-oppositional biogeography-based optimization for multi-objective optimal power flow. Electric Power Components and Systems, 40(2), 236–56.
Roy, P.K., & Mandal, D. (2014). Oppositional biogeography-based optimization for optimal power flow. International Journal of Power Energy Conversion, 5(1), 47–69.
Sadollah, A., Bahreininejad, A., Eskandar, H., Hamdi, M., (2012), Mine blast algorithm for optimization of truss structures with discrete variables. Computers and Structures, 102,49–63
Sapp, J. (1994). Evolution by association: a history of symbiosis: a history of symbiosis. New York: Oxford University Press, USA.
Shi, Y., & Eberhart, R. (1998). A modified particle swarm optimizer, In: IEEE international conference on computational intelligence, p. 69–73.
Storn, R., Price , K., (1997), Differential evolution – a simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization, 11, 341–59.
Sultana, S., & Roy, P.K. (2014), Multi-objective quasi-oppositional teaching learning based optimization for optimal location of distributed generator in radial distribution systems, Electrical Power and Energy Systems, 63, 534–545.
Tizhoosh, H.R. (2005). Opposition-based learning: a new scheme for machine intelligence. In: International conference on computational intelligence for modelling control and automation; Austria: p. 695–01.
Verma, S., Saha, S., & Mukherjee, V. (2017). A novel symbiotic organisms search algorithm for congestion management in deregulated environment. Journal of Experimental & Theoretical Artificial Intelligence, 29(1), 59-79.
Wang, H., Wu, Z., Rahnamayan, S., Liu, Y., & Ventresca, M. (2011). Enhancing particle swarm optimization using generalized opposition-based learning. Information Sciences, 181(20), 4699-4714.
Yang, X-S. (2009). Firefly algorithms for multimodal optimization. In: Proceedings of the 5th international conference on stochastic algorithms: foundations and applications. Sapporo, Japan: Springer-Verlag, 169–78.
Yang, X-S., & Deb, S. (2009), Cuckoo search via levy flights. In: Proceedings of the world congress on nature & biologically inspired computing (NaBIC-2009). Coimbatore, India. p. 210–214.
Zhan, Z.H., Zhang, J., Li, Y., & Chung, H.H. (2009), Adaptive particle swarm optimization. IEEE Transactions B, 39(6), 1362–1381.