How to cite this paper
Nama, S., Saha, A & Ghosh, S. (2016). Improved symbiotic organisms search algorithm for solving unconstrained function optimization.Decision Science Letters , 5(3), 361-380.
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.
Abido, M.A. (2009) Multiobjective particle swarm optimization for environmental/economic dispatch problem. Electric Power Systems Research, 79, 1105–1113
Aickelin, U., & Dowsland, K. A. (2014). An indirect genetic algorithm for a nurse scheduling problem. Computers & Operations Research, 31(5), 761-778.
Aulady, M. (2013). A hybrid symbiotic organisms search-quantum neural network for predicting high performance concrete compressive strength. Master & apos; s Thesis, http://pc01.lib.ntust.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0705114-091239.
Barati, M.A., Mohammadi, M., & Naderi, B. (2016). Multi-period fuzzy mean-semi variance portfolio selection problem with transaction cost and minimum transaction lots using genetic algorithm. International Journal of Industrial Engineering Computations, 7(2), 217–22
Baykaso?lu, A., Hamzadayi, A., & K?se, 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, 276, 204-218.
Bhunia, A., Pal, P., & Chattopadhyay, S. (2015). A hybrid of genetic algorithm and Fletcher-Reeves for bound constrained optimization problems.Decision Science Letters, 4(2), 125-136.
Blum, C. (2005). Ant colony optimization: Introduction and recent trends. Physics of Life Reviews, 2 (4), 353-373.
Bola?os, R., Echeverry, M., & Escobar, J. (2015). A multiobjective non-dominated sorting genetic algorithm (NSGA-II) for the Multiple Traveling Salesman Problem. Decision Science Letters, 4(4), 559-568.
Canelas, A., Neves, R., & Horta, N. (2013). A SAX-GA approach to evolve investment strategies on financial markets based on pattern discovery techniques. Expert Systems with Applications, 40(5), 1579-1590.
Chander, A., Chatterjee, A., & Siarry, P. (2011). A new social and momentum component adaptive PSO algorithm for image segmentation.Expert Systems with Applications, 38(5), 4998-5004.
Cheng, M. Y., & Prayogo, D. (2014). Symbiotic Organisms Search: A new metaheuristic optimization algorithm. Computers & Structures, 139, 98-112.
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, DOI: 10.1061/(ASCE)CP.1943-5487.0000512
Dorigo, M., Maniezzo, V., & Colorni, A. (1991).Positive feedback as a search strategy. Technical Report 91-016, Dipartimento di Elettronica, Politecnico di Milano, IT.
Eki, R., Vincent, F. Y., Budi, S., & Redi, A. P. (2015). Symbiotic Organism Search (SOS) for Solving the Capacitated Vehicle Routing Problem. World Academy of Science, Engineering and Technology, International Journal of Mechanical, Aerospace, Industrial, Mechatronic and Manufacturing Engineering, 9(5), 850-854.
Eshraghi, A. (2016). A new approach for solving resource constrained project scheduling problems using differential evolution algorithm. International Journal of Industrial Engineering Computations, 7(2), 205-216.
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.
Geem, Z. W., Kim, J. H., & Loganathan, G. V. (2001). A new heuristic optimization algorithm: harmony search. Simulation, 76(2), 60-68.
Gen, M., Tsujimura, Y., & Kubota, E. (1994, October). Solving job-shop scheduling problems by genetic algorithm. In Systems, Man, and Cybernetics, 1994. Humans, Information and Technology., 1994 IEEE International Conference on (Vol. 2, pp. 1577-1582). IEEE.
Ghasemi, M., Ghavidel, S., Ghanbarian, M. M., Massrur, H. R., & Gharibzadeh, M. (2014). Application of imperialist competitive algorithm with its modified techniques for multi-objective optimal power flow problem: a comparative study. Information Sciences, 281, 225-247.
Hecker, F. T., Hussein, W. B., Paquet-Durand, O., Hussein, M. A., & Becker, T. (2013). A case study on using evolutionary algorithms to optimize bakery production planning. Expert Systems with Applications, 40(17), 6837-6847.
Hecker, F. T., Stanke, M., Becker, T., & Hitzmann, B. (2014). Application of a modified GA, ACO and a random search procedure to solve the production scheduling of a case study bakery. Expert Systems with Applications,41(13), 5882-5891.
Holland, J. H. (1975). Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence. U Michigan Press.
Hosseini, M., Sadeghzade, M., & Nourmandi-Pour, R. (2014). An efficient approach based on differential evolution algorithm for data clustering.Decision Science Letters, 3(3), 319-324.
Kavousi-Fard, A., Rostami, M. A., & Niknam, T. (2015). Reliability-oriented reconfiguration of vehicle-to-grid networks. Industrial Informatics, IEEE Transactions on, 11(3), 682-691.
Kennedy, J., Eberhart, R., (1995). Particle swam optimization, in: Proceeding of the IEEE International Conference on Neural Network, Piscataway, 4, 1942–1948.
Li, X., Yin, M., & Ma, Z. (2011). Hybrid differential evolution and gravitation search algorithm for unconstrained optimization. Int. J. Phys. Sci, 6(25), 5961-5981.
Li, X.L., & He, X.D. (2014) A hybrid particle swarm optimization method for structure learning of probabilistic relational models. Information Sciences, 283, 258–266
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.
Mir, M.S.S., & Rezaeian, J., (2016), A robust hybrid approach based on particle swarm optimization and genetic algorithm to minimize the total machine load on unrelated parallel machines, Applied Soft Computing, 41, 488-504
Mohammadi, S., Rahmani, M., & Azadi, M. (2015). Optimization of continuous ranked probability score using PSO. Decision Science Letters,4(3), 373-378.
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.
Nama, S., Saha, A. K., & Ghosh, S. (2015). Parameters Optimization of Geotechnical Problem Using Different Optimization Algorithm. Geotechnical and Geological Engineering, 33(5), 1235-1253.
Orouji, M. (2016). Theory of constraints: A state-of-art review. Accounting,2(1), 45-52.
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.
Rao, R & Patel, V. (2014). A multi-objective improved teaching-learning based optimization algorithm for unconstrained and constrained optimization problems. International Journal of Industrial Engineering Computations, 5(1), 1-22.
Rao, R. (2016). Review of applications of TLBO algorithm and a tutorial for beginners to solve the unconstrained and constrained optimization problems.Decision Science Letters, 5(1), 1-30.
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.
Rout, N. K., Das, D. P., & Panda, G. (2016) Particle swarm optimization based nonlinear active noise control under saturation nonlinearity. Applied Soft Computing, 41, 275-289
Satapathy, S., & Naik, A. (2013). Improved teaching learning based optimization for global function optimization. Decision Science Letters, 2(1), 23-34.
Shi, Y., & Eberhart, R. (1998). A modified particle swarm optimizer. In: Evolutionary computation proceedings. IEEE World Congress on Computational Intelligence. Pp. 69 – 73, doi:10.1109/ICEC.1998.699146.
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, 2005.
Verma, S., Saha, S., & Mukherjee, V. (2015). A novel symbiotic organisms search algorithm for congestion management in deregulated environment. Journal of Experimental & Theoretical Artificial Intelligence, DOI: 10.1080/0952813X.2015.1116141
Wang, Y., Cai, Z., & Zhang, Q. (2011). Differential evolution with composite trial vector generation strategies and control parameters. IEEE Transactions on Evolutionary Computation, 15(1), 55–66.
Wang, Y., Zhou, J., Zhou, C., Wang, Y., Qin, H., & Lu, Y. (2012). An improved self-adaptive PSO technique for short-term hydrothermal scheduling. Expert Systems with Applications, 39(3), 2288-2295.
Abido, M.A. (2009) Multiobjective particle swarm optimization for environmental/economic dispatch problem. Electric Power Systems Research, 79, 1105–1113
Aickelin, U., & Dowsland, K. A. (2014). An indirect genetic algorithm for a nurse scheduling problem. Computers & Operations Research, 31(5), 761-778.
Aulady, M. (2013). A hybrid symbiotic organisms search-quantum neural network for predicting high performance concrete compressive strength. Master & apos; s Thesis, http://pc01.lib.ntust.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0705114-091239.
Barati, M.A., Mohammadi, M., & Naderi, B. (2016). Multi-period fuzzy mean-semi variance portfolio selection problem with transaction cost and minimum transaction lots using genetic algorithm. International Journal of Industrial Engineering Computations, 7(2), 217–22
Baykaso?lu, A., Hamzadayi, A., & K?se, 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, 276, 204-218.
Bhunia, A., Pal, P., & Chattopadhyay, S. (2015). A hybrid of genetic algorithm and Fletcher-Reeves for bound constrained optimization problems.Decision Science Letters, 4(2), 125-136.
Blum, C. (2005). Ant colony optimization: Introduction and recent trends. Physics of Life Reviews, 2 (4), 353-373.
Bola?os, R., Echeverry, M., & Escobar, J. (2015). A multiobjective non-dominated sorting genetic algorithm (NSGA-II) for the Multiple Traveling Salesman Problem. Decision Science Letters, 4(4), 559-568.
Canelas, A., Neves, R., & Horta, N. (2013). A SAX-GA approach to evolve investment strategies on financial markets based on pattern discovery techniques. Expert Systems with Applications, 40(5), 1579-1590.
Chander, A., Chatterjee, A., & Siarry, P. (2011). A new social and momentum component adaptive PSO algorithm for image segmentation.Expert Systems with Applications, 38(5), 4998-5004.
Cheng, M. Y., & Prayogo, D. (2014). Symbiotic Organisms Search: A new metaheuristic optimization algorithm. Computers & Structures, 139, 98-112.
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, DOI: 10.1061/(ASCE)CP.1943-5487.0000512
Dorigo, M., Maniezzo, V., & Colorni, A. (1991).Positive feedback as a search strategy. Technical Report 91-016, Dipartimento di Elettronica, Politecnico di Milano, IT.
Eki, R., Vincent, F. Y., Budi, S., & Redi, A. P. (2015). Symbiotic Organism Search (SOS) for Solving the Capacitated Vehicle Routing Problem. World Academy of Science, Engineering and Technology, International Journal of Mechanical, Aerospace, Industrial, Mechatronic and Manufacturing Engineering, 9(5), 850-854.
Eshraghi, A. (2016). A new approach for solving resource constrained project scheduling problems using differential evolution algorithm. International Journal of Industrial Engineering Computations, 7(2), 205-216.
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.
Geem, Z. W., Kim, J. H., & Loganathan, G. V. (2001). A new heuristic optimization algorithm: harmony search. Simulation, 76(2), 60-68.
Gen, M., Tsujimura, Y., & Kubota, E. (1994, October). Solving job-shop scheduling problems by genetic algorithm. In Systems, Man, and Cybernetics, 1994. Humans, Information and Technology., 1994 IEEE International Conference on (Vol. 2, pp. 1577-1582). IEEE.
Ghasemi, M., Ghavidel, S., Ghanbarian, M. M., Massrur, H. R., & Gharibzadeh, M. (2014). Application of imperialist competitive algorithm with its modified techniques for multi-objective optimal power flow problem: a comparative study. Information Sciences, 281, 225-247.
Hecker, F. T., Hussein, W. B., Paquet-Durand, O., Hussein, M. A., & Becker, T. (2013). A case study on using evolutionary algorithms to optimize bakery production planning. Expert Systems with Applications, 40(17), 6837-6847.
Hecker, F. T., Stanke, M., Becker, T., & Hitzmann, B. (2014). Application of a modified GA, ACO and a random search procedure to solve the production scheduling of a case study bakery. Expert Systems with Applications,41(13), 5882-5891.
Holland, J. H. (1975). Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence. U Michigan Press.
Hosseini, M., Sadeghzade, M., & Nourmandi-Pour, R. (2014). An efficient approach based on differential evolution algorithm for data clustering.Decision Science Letters, 3(3), 319-324.
Kavousi-Fard, A., Rostami, M. A., & Niknam, T. (2015). Reliability-oriented reconfiguration of vehicle-to-grid networks. Industrial Informatics, IEEE Transactions on, 11(3), 682-691.
Kennedy, J., Eberhart, R., (1995). Particle swam optimization, in: Proceeding of the IEEE International Conference on Neural Network, Piscataway, 4, 1942–1948.
Li, X., Yin, M., & Ma, Z. (2011). Hybrid differential evolution and gravitation search algorithm for unconstrained optimization. Int. J. Phys. Sci, 6(25), 5961-5981.
Li, X.L., & He, X.D. (2014) A hybrid particle swarm optimization method for structure learning of probabilistic relational models. Information Sciences, 283, 258–266
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.
Mir, M.S.S., & Rezaeian, J., (2016), A robust hybrid approach based on particle swarm optimization and genetic algorithm to minimize the total machine load on unrelated parallel machines, Applied Soft Computing, 41, 488-504
Mohammadi, S., Rahmani, M., & Azadi, M. (2015). Optimization of continuous ranked probability score using PSO. Decision Science Letters,4(3), 373-378.
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.
Nama, S., Saha, A. K., & Ghosh, S. (2015). Parameters Optimization of Geotechnical Problem Using Different Optimization Algorithm. Geotechnical and Geological Engineering, 33(5), 1235-1253.
Orouji, M. (2016). Theory of constraints: A state-of-art review. Accounting,2(1), 45-52.
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.
Rao, R & Patel, V. (2014). A multi-objective improved teaching-learning based optimization algorithm for unconstrained and constrained optimization problems. International Journal of Industrial Engineering Computations, 5(1), 1-22.
Rao, R. (2016). Review of applications of TLBO algorithm and a tutorial for beginners to solve the unconstrained and constrained optimization problems.Decision Science Letters, 5(1), 1-30.
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.
Rout, N. K., Das, D. P., & Panda, G. (2016) Particle swarm optimization based nonlinear active noise control under saturation nonlinearity. Applied Soft Computing, 41, 275-289
Satapathy, S., & Naik, A. (2013). Improved teaching learning based optimization for global function optimization. Decision Science Letters, 2(1), 23-34.
Shi, Y., & Eberhart, R. (1998). A modified particle swarm optimizer. In: Evolutionary computation proceedings. IEEE World Congress on Computational Intelligence. Pp. 69 – 73, doi:10.1109/ICEC.1998.699146.
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, 2005.
Verma, S., Saha, S., & Mukherjee, V. (2015). A novel symbiotic organisms search algorithm for congestion management in deregulated environment. Journal of Experimental & Theoretical Artificial Intelligence, DOI: 10.1080/0952813X.2015.1116141
Wang, Y., Cai, Z., & Zhang, Q. (2011). Differential evolution with composite trial vector generation strategies and control parameters. IEEE Transactions on Evolutionary Computation, 15(1), 55–66.
Wang, Y., Zhou, J., Zhou, C., Wang, Y., Qin, H., & Lu, Y. (2012). An improved self-adaptive PSO technique for short-term hydrothermal scheduling. Expert Systems with Applications, 39(3), 2288-2295.