How to cite this paper
Mane, S., Narsingrao, M & Patil, V. (2018). A many-objective Jaya algorithm for many-objective optimization problems.Decision Science Letters , 7(4), 567-582.
Refrences
Abhishek, K., Kumar, V. R., Datta, S., & Mahapatra, S. S. (2016). Application of JAYA algorithm for the optimization of machining performance characteristics during the turning of CFRP (epoxy) composites: comparison with TLBO, GA, and ICA. Engineering with Computers, 1-19.
Asafuddoula, M., Ray, T., & Sarker, R. (2015). A decomposition-based evolutionary algorithm for many objective optimization. IEEE Transactions on Evolutionary Computation, 19(3), 445-460.
Azizipanah-Abarghooee, R., Dehghanian, P., & Terzija, V. (2016). Practical multi-area bi-objective environmental economic dispatch equipped with a hybrid gradient search method and improved Jaya algorithm. IET Generation, Transmission & Distribution, 10(14), 3580-3596.
Bhoye, M., Pandya, M. H., Valvi, S., Trivedi, I. N., Jangir, P., & Parmar, S. A. (2016, April). An emission constraint economic load dispatch problem solution with microgrid using JAYA algorithm. In Energy Efficient Technologies for Sustainability (ICEETS), 2016 International Conference on (pp. 497-502). IEEE.
Chand, S., & Wagner, M. (2015). Evolutionary many-objective optimization: a quick-start guide. Surveys in Operations Research and Management Science, 20(2), 35-42.
Cheng, R., Jin, Y., Olhofer, M., & Sendhoff, B. (2016). A reference vector guided evolutionary algorithm for many-objective optimization. IEEE Transactions on Evolutionary Computation, 20(5), 773-791.
Cheung, Y. M., Gu, F., & Liu, H. L. (2016). Objective extraction for many-objective optimization problems: Algorithm and test problems. IEEE Transactions on Evolutionary Computation, 20(5), 755-772.
Deb, K., & Jain, H. (2014). An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach, part I: Solving problems with box constraints. IEEE Trans. Evolutionary Computation, 18(4), 577-601.
Figueiredo, E. M., Araújo, D. R., Bastos Filho, C. J., & Ludermir, T. B. (2016, October). Physical Topology Design of Optical Networks Aided by Many-Objective Optimization Algorithms. In Intelligent Systems (BRACIS), 2016 5th Brazilian Conference on (pp. 409-414). IEEE.
Gong, D. W., Sun, J., & Miao, Z. (2016). A Set-based Genetic Algorithm for Interval Many-objective Optimization Problems. IEEE Transactions on Evolutionary Computation.
Ibrahim, A., Rahnamayan, S., Martin, M. V., & Deb, K. (2016, July). EliteNSGA-III: An improved evolutionary many-objective optimization algorithm. In Evolutionary Computation (CEC), 2016 IEEE Congress on (pp. 973-982). IEEE.
Khosravi, S., & Akbarzadeh-T, M. R. (2015, November). A tree-of-heaven inspired approach for solving many-objective optimization problems. In Technology, Communication and Knowledge (ICTCK), 2015 International Congress on (pp. 104-111). IEEE.
Li, B., Li, J., Tang, K., & Yao, X. (2015). Many-objective evolutionary algorithms: A survey. AC1M Computing Surveys (CSUR), 48(1), 13.
Lwin, K., Qu, R., & Kendall, G. (2014). A learning-guided multi-objective evolutionary algorithm for constrained portfolio optimization. Applied Soft Computing, 24, 757-772.
Mishra, S., & Ray, P. K. (2016). Power quality improvement using photovoltaic fed DSTATCOM based on JAYA optimization. IEEE Transactions on Sustainable Energy, 7(4), 1672-1680.
Murata, T., Ishibuchi, H., & Gen, M. (2001, March). Specification of genetic search directions in cellular multi-objective genetic algorithms. In International Conference on Evolutionary Multi-Criterion Optimization (pp. 82-95). Springer Berlin Heidelberg.
Pandey, H. M. (2016, January). Jaya a novel optimization algorithm: What, how and why?. In Cloud System and Big Data Engineering (Confluence), 2016 6th International Conference (pp. 728-730). IEEE.
Prakash, T., Singh, V. P., Singh, S. P., & Mohanty, S. R. Binary Jaya Algorithm Based Optimal Placement of Phasor Measurement Units for Power System Observability.
Rao, R. (2016). Jaya: A simple and new optimization algorithm for solving constrained and unconstrained optimization problems. International Journal of Industrial Engineering Computations, 7(1), 19-34.
Rao, R. V., & Saroj, A. (2016). Multi-objective design optimization of heat exchangers using elitist-Jaya algorithm. Energy Systems, 1-37.
Rao, R. V., & Saroj, A. (2017). A self-adaptive multi-population based Jaya algorithm for engineering optimization. Swarm and Evolutionary Computation.
Rao, R. V., & Waghmare, G. G. (2017). A new optimization algorithm for solving complex constrained design optimization problems. Engineering Optimization, 49(1), 60-83.
Rao, R. V., More, K. C., Taler, J., & Ocłoń, P. (2016). Dimensional optimization of a micro-channel heat sink using Jaya algorithm. Applied Thermal Engineering, 103, 572-582.
Rao, R. V., Rai, D. P., & Balic, J. (2017). A new optimization algorithm for parameter optimization of nano-finishing processes. Scientia Iranica. Transaction E, Industrial Engineering, 24(2), 868.
Rao, R. V., Rai, D. P., & Balic, J. (2017). A multi-objective algorithm for optimization of modern machining processes. Engineering Applications of Artificial Intelligence, 61, 103–125
Rao, R. V., Rai, D. P., Ramkumar, J., & Balic, J. (2016). A new multi-objective Jaya algorithm for optimization of modern machining processes. Advances in Production Engineering & Management, 11(4), 271.
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.
Seshadri, A. (2006). Multi-objective optimization using evolutionary algorithms (MOEA). Matlab Website: http://www. Mathworks. com/matlabcentral/fileexchange/10429, by, 19.
Singh, S. P., Prakash, T., Singh, V. P., & Babu, M. G. (2017). Analytic hierarchy process based automatic generation control of multi-area interconnected power system using Jaya algorithm. Engineering Applications of Artificial Intelligence, 60, 35-44.
Wang, R. (2016). An Improved Nondominated Sorting Genetic Algorithm for Multiobjective Problem. Mathematical Problems in Engineering, 2016.
Wang, R., Zhou, Z., Ishibuchi, H., Liao, T., & Zhang, T. (2016). Localized weighted sum method for many-objective optimization. IEEE Transactions on Evolutionary Computation.
Wang, W., Ying, S., Li, L., Wang, Z., & Li, W. (2017). An improved decomposition-based multiobjective evolutionary algorithm with a better balance of convergence and diversity. Applied Soft Computing, 57, 627-641.
Wang, Z. J., Zhan, Z. H., & Zhang, J. (2016, December). Parallel multi-strategy evolutionary algorithm using massage passing interface for many-objective optimization. In Computational Intelligence (SSCI), 2016 IEEE Symposium Series on (pp. 1-8). IEEE.
Warid, W., Hizam, H., Mariun, N., & Abdul-Wahab, N. I. (2016). Optimal Power Flow Using the Jaya Algorithm. Energies, 9(9), 678.
Zhang, X., Tian, Y., & Jin, Y. (2015). A knee point-driven evolutionary algorithm for many-objective optimization. IEEE Transactions on Evolutionary Computation, 19(6), 761-776.
Zhang, Y. H., Gong, Y. J., Zhang, J., & Ling, Y. B. (2016, July). A hybrid evolutionary algorithm with dual populations for many-objective optimization. In Evolutionary Computation (CEC), 2016 IEEE Congress on (pp. 1610-1617). IEEE.
Zhuansun, X., Zhu, A., Wu, J., Han, T., & Chen, Y. (2016, October). Many-objective reactive power optimization using Particle Swarm Optimization algorithm based on Pareto entropy. In Power and Energy Engineering Conference (APPEEC), 2016 IEEE PES Asia-Pacific (pp. 923-928). IEEE.
Asafuddoula, M., Ray, T., & Sarker, R. (2015). A decomposition-based evolutionary algorithm for many objective optimization. IEEE Transactions on Evolutionary Computation, 19(3), 445-460.
Azizipanah-Abarghooee, R., Dehghanian, P., & Terzija, V. (2016). Practical multi-area bi-objective environmental economic dispatch equipped with a hybrid gradient search method and improved Jaya algorithm. IET Generation, Transmission & Distribution, 10(14), 3580-3596.
Bhoye, M., Pandya, M. H., Valvi, S., Trivedi, I. N., Jangir, P., & Parmar, S. A. (2016, April). An emission constraint economic load dispatch problem solution with microgrid using JAYA algorithm. In Energy Efficient Technologies for Sustainability (ICEETS), 2016 International Conference on (pp. 497-502). IEEE.
Chand, S., & Wagner, M. (2015). Evolutionary many-objective optimization: a quick-start guide. Surveys in Operations Research and Management Science, 20(2), 35-42.
Cheng, R., Jin, Y., Olhofer, M., & Sendhoff, B. (2016). A reference vector guided evolutionary algorithm for many-objective optimization. IEEE Transactions on Evolutionary Computation, 20(5), 773-791.
Cheung, Y. M., Gu, F., & Liu, H. L. (2016). Objective extraction for many-objective optimization problems: Algorithm and test problems. IEEE Transactions on Evolutionary Computation, 20(5), 755-772.
Deb, K., & Jain, H. (2014). An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach, part I: Solving problems with box constraints. IEEE Trans. Evolutionary Computation, 18(4), 577-601.
Figueiredo, E. M., Araújo, D. R., Bastos Filho, C. J., & Ludermir, T. B. (2016, October). Physical Topology Design of Optical Networks Aided by Many-Objective Optimization Algorithms. In Intelligent Systems (BRACIS), 2016 5th Brazilian Conference on (pp. 409-414). IEEE.
Gong, D. W., Sun, J., & Miao, Z. (2016). A Set-based Genetic Algorithm for Interval Many-objective Optimization Problems. IEEE Transactions on Evolutionary Computation.
Ibrahim, A., Rahnamayan, S., Martin, M. V., & Deb, K. (2016, July). EliteNSGA-III: An improved evolutionary many-objective optimization algorithm. In Evolutionary Computation (CEC), 2016 IEEE Congress on (pp. 973-982). IEEE.
Khosravi, S., & Akbarzadeh-T, M. R. (2015, November). A tree-of-heaven inspired approach for solving many-objective optimization problems. In Technology, Communication and Knowledge (ICTCK), 2015 International Congress on (pp. 104-111). IEEE.
Li, B., Li, J., Tang, K., & Yao, X. (2015). Many-objective evolutionary algorithms: A survey. AC1M Computing Surveys (CSUR), 48(1), 13.
Lwin, K., Qu, R., & Kendall, G. (2014). A learning-guided multi-objective evolutionary algorithm for constrained portfolio optimization. Applied Soft Computing, 24, 757-772.
Mishra, S., & Ray, P. K. (2016). Power quality improvement using photovoltaic fed DSTATCOM based on JAYA optimization. IEEE Transactions on Sustainable Energy, 7(4), 1672-1680.
Murata, T., Ishibuchi, H., & Gen, M. (2001, March). Specification of genetic search directions in cellular multi-objective genetic algorithms. In International Conference on Evolutionary Multi-Criterion Optimization (pp. 82-95). Springer Berlin Heidelberg.
Pandey, H. M. (2016, January). Jaya a novel optimization algorithm: What, how and why?. In Cloud System and Big Data Engineering (Confluence), 2016 6th International Conference (pp. 728-730). IEEE.
Prakash, T., Singh, V. P., Singh, S. P., & Mohanty, S. R. Binary Jaya Algorithm Based Optimal Placement of Phasor Measurement Units for Power System Observability.
Rao, R. (2016). Jaya: A simple and new optimization algorithm for solving constrained and unconstrained optimization problems. International Journal of Industrial Engineering Computations, 7(1), 19-34.
Rao, R. V., & Saroj, A. (2016). Multi-objective design optimization of heat exchangers using elitist-Jaya algorithm. Energy Systems, 1-37.
Rao, R. V., & Saroj, A. (2017). A self-adaptive multi-population based Jaya algorithm for engineering optimization. Swarm and Evolutionary Computation.
Rao, R. V., & Waghmare, G. G. (2017). A new optimization algorithm for solving complex constrained design optimization problems. Engineering Optimization, 49(1), 60-83.
Rao, R. V., More, K. C., Taler, J., & Ocłoń, P. (2016). Dimensional optimization of a micro-channel heat sink using Jaya algorithm. Applied Thermal Engineering, 103, 572-582.
Rao, R. V., Rai, D. P., & Balic, J. (2017). A new optimization algorithm for parameter optimization of nano-finishing processes. Scientia Iranica. Transaction E, Industrial Engineering, 24(2), 868.
Rao, R. V., Rai, D. P., & Balic, J. (2017). A multi-objective algorithm for optimization of modern machining processes. Engineering Applications of Artificial Intelligence, 61, 103–125
Rao, R. V., Rai, D. P., Ramkumar, J., & Balic, J. (2016). A new multi-objective Jaya algorithm for optimization of modern machining processes. Advances in Production Engineering & Management, 11(4), 271.
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.
Seshadri, A. (2006). Multi-objective optimization using evolutionary algorithms (MOEA). Matlab Website: http://www. Mathworks. com/matlabcentral/fileexchange/10429, by, 19.
Singh, S. P., Prakash, T., Singh, V. P., & Babu, M. G. (2017). Analytic hierarchy process based automatic generation control of multi-area interconnected power system using Jaya algorithm. Engineering Applications of Artificial Intelligence, 60, 35-44.
Wang, R. (2016). An Improved Nondominated Sorting Genetic Algorithm for Multiobjective Problem. Mathematical Problems in Engineering, 2016.
Wang, R., Zhou, Z., Ishibuchi, H., Liao, T., & Zhang, T. (2016). Localized weighted sum method for many-objective optimization. IEEE Transactions on Evolutionary Computation.
Wang, W., Ying, S., Li, L., Wang, Z., & Li, W. (2017). An improved decomposition-based multiobjective evolutionary algorithm with a better balance of convergence and diversity. Applied Soft Computing, 57, 627-641.
Wang, Z. J., Zhan, Z. H., & Zhang, J. (2016, December). Parallel multi-strategy evolutionary algorithm using massage passing interface for many-objective optimization. In Computational Intelligence (SSCI), 2016 IEEE Symposium Series on (pp. 1-8). IEEE.
Warid, W., Hizam, H., Mariun, N., & Abdul-Wahab, N. I. (2016). Optimal Power Flow Using the Jaya Algorithm. Energies, 9(9), 678.
Zhang, X., Tian, Y., & Jin, Y. (2015). A knee point-driven evolutionary algorithm for many-objective optimization. IEEE Transactions on Evolutionary Computation, 19(6), 761-776.
Zhang, Y. H., Gong, Y. J., Zhang, J., & Ling, Y. B. (2016, July). A hybrid evolutionary algorithm with dual populations for many-objective optimization. In Evolutionary Computation (CEC), 2016 IEEE Congress on (pp. 1610-1617). IEEE.
Zhuansun, X., Zhu, A., Wu, J., Han, T., & Chen, Y. (2016, October). Many-objective reactive power optimization using Particle Swarm Optimization algorithm based on Pareto entropy. In Power and Energy Engineering Conference (APPEEC), 2016 IEEE PES Asia-Pacific (pp. 923-928). IEEE.