How to cite this paper
Mane, S & Narsingrao, M. (2021). A chaotic-based improved many-objective Jaya algorithm for many-objective optimization problems.International Journal of Industrial Engineering Computations , 12(1), 49-62.
Refrences
Aslan, M., Gunduz, M., & Kiran, M. S. (2019). JayaX: Jaya algorithm with xor operator for binary optimization. Applied Soft Computing, 82, 105576.
Bewoor, L. A., Chandra Prakash, V., & Sapkal, S. U. (2017). Evolutionary hybrid particle swarm optimization algorithm for solving NP-hard no-wait flow shop scheduling problems. Algorithms, 10(4), 121.
Blank, J., Deb, K., Dhebar, Y., Bandaru, S., & Seada, H. (2020). Generating well-spaced points on a unit simplex for evolutionary many-objective optimization. IEEE Transactions on Evolutionary Computation.
Champasak, P., Panagant, N., Pholdee, N., Bureerat, S., & Yildiz, A. R. (2020). Self-adaptive many-objective meta-heuristic based on decomposition for many-objective conceptual design of a fixed wing unmanned aerial vehicle. Aerospace Science and Technology, 100, 105783.
Chen, F., Ding, Z., Lu, Z., & Zeng, X. (2018). Parameters identification for chaotic systems based on a modified Jaya algorithm. Nonlinear Dynamics, 94(4), 2307-2326.
Chen, H., Cheng, R., Wen, J., Li, H., & Weng, J. (2020). Solving large-scale many-objective optimization problems by covariance matrix adaptation evolution strategy with scalable small subpopulations. Information Sciences, 509, 457-469.
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.
Chi, Y., Xu, Y., & Zhang, R. (2020). Many-objective Robust Optimization for Dynamic VAR Planning to Enhance Voltage Stability of a Wind-Energy Power System. IEEE Transactions on Power Delivery.
Črepinšek, M., Liu, S. H., & Mernik, M. (2013). Exploration and exploitation in evolutionary algorithms: A survey. ACM computing surveys (CSUR), 45(3), 1-33.
Cui, Z., Zhang, J., Wang, Y., Cao, Y., Cai, X., Zhang, W., & Chen, J. (2019). A pigeon-inspired optimization algorithm for many-objective optimization problems. Sci. China Information Science, 62(7), 70212-1.
Cui, Z., Zhang, J., Wu, D., Cai, X., Wang, H., Zhang, W., & Chen, J. (2020). Hybrid many-objective particle swarm optimization algorithm for green coal production problem. Information Sciences, 518, 256-271.
Deb, K., Thiele, L., Laumanns, M., & Zitzler, E. (2005). Scalable test problems for evolutionary multiobjective optimization. In Evolutionary multiobjective optimization (pp. 105-145). Springer, London.
Dhiman, G., Soni, M., Pandey, H. M., Slowik, A., & Kaur, H. (2020). A novel hybrid hypervolume indicator and reference vector adaptation strategies based evolutionary algorithm for many-objective optimization. Engineering with Computers, 1-19.
Di Wu, S. G., Cai, X., Zhang, G., & Xue, F. (2020). A many-objective optimization WSN energy balance model.
Farah, A., & Belazi, A. (2018). A novel chaotic Jaya algorithm for unconstrained numerical optimization. Nonlinear Dynamics, 93(3), 1451-1480.
Fritsche, G., & Pozo, A. (2020). The Analysis of a Cooperative Hyper-Heuristic on a Constrained Real-world Many-objective Continuous Problem, IEEE Congress on Evolutionary Computation (CEC), Glasgow, United Kingdom, pp. 1-8.
Gómez, R. H., Coello, C. A. C., & Alba, E. (2020). A Parallel Island Model for Hypervolume-Based Many-Objective Optimization. In High-Performance Simulation-Based Optimization (pp. 247-273). Springer, Cham.
Gong, C. (2017). An enhanced Jaya algorithm with a two group Adaption. International Journal of Computational Intelligence Systems, 10(1), 1102-1115.
Gong, D., Liu, Y., & Yen, G. G. (2020). A meta-objective approach for many-objective evolutionary optimization. Evolutionary computation, 28(1), 1-25.
Gu, Z. M., & Wang, G. G. (2020). Improving NSGA-III algorithms with information feedback models for large-scale many-objective optimization. Future Generation Computer Systems, 107, 49-69.
Helbig, M., & Engelbrecht, A. (2020, March). Partial Dominance for Many-Objective Optimization. In Proceedings of the 2020 4th International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence (pp. 81-86).
Hu, Z., Wei, Z., Ma, X., Sun, H., & Yang, J. (2020). Multi-parameter deep-perception and many-objective autonomous-control of rolling schedule on high speed cold tandem mill. ISA transactions.
Ingle, K. K., & Jatoth, R. K. (2020). An efficient JAYA algorithm with lévy flight for non-linear channel equalization. Expert Systems with Applications, 145, 112970.
Kaur, A., Sharma, S., & Mishra, A. (2019). A Novel Jaya-BAT Algorithm Based Power Consumption Minimization in Cognitive Radio Network. Wireless Personal Communications, 108(4), 2059-2075.
Kumar, A. S., & Venkatesan, M. (2019). Multi-Objective Task Scheduling Using Hybrid Genetic-Ant Colony Optimization Algorithm in Cloud Environment. Wireless Personal Communications, 107(4), 1835-1848.
Li, X., & Zhang, H. (2020). A multi-agent complex network algorithm for multi-objective optimization. Applied Intelligence, 1-28.
Liu, J., & Lansey, K. E. (2020). Multiphase DMA design methodology based on graph theory and many-objective optimization. Journal of Water Resources Planning and Management, 146(8), 04020068.
Liu, R., Liu, J., Zhou, R., Lian, C., & Bian, R. (2020). A region division based decomposition approach for evolutionary many-objective optimization. Knowledge-Based Systems, 105518.
Liu, Y., Zhu, N., & Li, M. (2020). Solving Many-Objective Optimization Problems by a Pareto-Based Evolutionary Algorithm With Preprocessing and a Penalty Mechanism. IEEE Transactions on Cybernetics.
Liu, Y., Zhu, N., Li, K., Li, M., Zheng, J., & Li, K. (2020). An angle dominance criterion for evolutionary many-objective optimization. Information Sciences, 509, 376-399.
Liu, Z. Z., Wang, Y., & Huang, P. Q. (2020). AnD: A many-objective evolutionary algorithm with angle-based selection and shift-based density estimation. Information Sciences, 509, 400-419.
Ma, L., Wang, R., Chen, S., Cheng, S., Wang, X., Lin, Z., ... & Huang, M. (2020). A novel many-objective evolutionary algorithm based on transfer matrix with Kriging model. Information Sciences, 509, 437-456.
Mane, S. U., & Rao, M. N. (2019). Large-Scale Compute-Intensive Constrained Optimization Problems: GPGPU-Based Approach. In Soft Computing: Theories and Applications (pp. 579-589). Springer, Singapore.
Mane, S., & Rao, M. N. (2017). Many-objective optimization: Problems and evolutionary algorithms–a short review. International Journal of Applied Engineering Research, 12(20), 9774-9793.
Mane, S., Narsingrao, M., & Patil, V. (2018). A many-objective Jaya algorithm for many-objective optimization problems. Decision Science Letters, 7(4), 567-582.
Meneghini, I. R., Alves, M. A., Gaspar-Cunha, A., & Guimarães, F. G. (2020). Scalable and customizable benchmark problems for many-objective optimization. Applied Soft Computing, 90, 106139.
Mohammed, R. T., Yaakob, R., Zaidan, A. A., Sharef, N. M., Abdullah, R. H., Zaidan, B. B., & Dawood, K. A. (2020). Review of the research landscape of multi-criteria evaluation and benchmarking processes for many-objective optimisation methods: coherent taxonomy, challenges and recommended solution. International Journal of Information Technology & Decision Making.
Ocłoń, P., Cisek, P., Rerak, M., Taler, D., Rao, R. V., Vallati, A., & Pilarczyk, M. (2018). Thermal performance optimization of the underground power cable system by using a modified Jaya algorithm. International Journal of Thermal Sciences, 123, 162-180.
Pawar, S. S., & Prasanth, Y. (2017). Multi-Objective Optimization Model for QoS-Enabled Web Service Selection in Service-Based Systems. New Review of Information Networking, 22(1), 34-53.
Qiu, H., & Duan, H. (2020). A multi-objective pigeon-inspired optimization approach to UAV distributed flocking among obstacles. Information Sciences, 509, 515-529.
Rajakumar, R., Amudhavel, J., Dhavachelvan, P., & Vengattaraman, T. (2017). GWO-LPWSN: Grey wolf optimization algorithm for node localization problem in wireless sensor networks. Journal of Computer Networks and Communications.
Rajeswari, M., Amudhavel, J., Pothula, S., & Dhavachelvan, P. (2017). Directed bee colony optimization algorithm to solve the nurse rostering problem. Computational intelligence and neuroscience, 2017.
Ramgouda, P., & Chandraprakash, V. (2019). Constraints handling in combinatorial interaction testing using multi-objective crow search and fruitfly optimization. Soft Computing, 23(8), 2713-2726.
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. (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. (2020). Rao algorithms: Three metaphor-less simple algorithms for solving optimization problems. International Journal of Industrial Engineering Computations, 11(1), 107-130.
Rao, R. V. (2019). Jaya: an advanced optimization algorithm and its engineering applications. Cham: Springer International Publishing.
Rao, R. V., & Keesari, H. S. (2018). Multi-team perturbation guiding Jaya algorithm for optimization of wind farm layout. Applied Soft Computing, 71, 800-815.
Rao, R. V., Keesari, H. S., Oclon, P., & Taler, J. (2019). Improved multi-objective Jaya optimization algorithm for a solar dish Stirling engine. Journal of Renewable and Sustainable Energy, 11(2), 025903.
Rao, R. V., Savsani, V. J., & Balic, J. (2012). Teaching–learning-based optimization algorithm for unconstrained and constrained real-parameter optimization problems. Engineering Optimization, 44(12), 1447-1462.
Rao, S. S. (2019). Engineering optimization: theory and practice. John Wiley & Sons.
Raut, U., & Mishra, S. (2019). An improved elitist–jaya algorithm for simultaneous network reconfiguration and dg allocation in power distribution systems. Renewable Energy Focus, 30, 92-106.
Raveendra, K., & Vinothkanna, R. (2019). Hybrid ant colony optimization model for image retrieval using scale-invariant feature transform local descriptor. Computers & Electrical Engineering, 74, 281-291.
Reddy, M. S., Ratnam, C., Rajyalakshmi, G., & Manupati, V. K. (2018). An effective hybrid multi objective evolutionary algorithm for solving real time event in flexible job shop scheduling problem. Measurement, 114, 78-90.
Sarzaeim, P., Bozorg-Haddad, O., & Chu, X. (2018). Teaching-learning-based optimization (TLBO) algorithm. In Advanced Optimization by Nature-Inspired Algorithms (pp. 51-58). Springer, Singapore.
Schütze, O., Cuate, O., Martín, A., Peitz, S., & Dellnitz, M. (2020). Pareto Explorer: a global/local exploration tool for many-objective optimization problems. Engineering Optimization, 52(5), 832-855.
Taha, K. (2020). Methods That Optimize Multi-Objective Problems: A Survey and Experimental Evaluation. IEEE Access, 8, 80855-80878.
Tanabe, R., & Ishibuchi, H. (2020). An easy-to-use real-world multi-objective optimization problem suite. Applied Soft Computing, 89, 106078.
Wang, R., Zhou, Z., Ishibuchi, H., Liao, T., & Zhang, T. (2016). Localized weighted sum method for many-objective optimization. IEEE Transactions on Evolutionary Computation, 22(1), 3-18.
Wu, C., & He, Y. (2020). Solving the set-union knapsack problem by a novel hybrid Jaya algorithm. Soft Computing, 24(3), 1883-1902.
Xue, Y., Li, M., & Liu, X. (2020, April). Angle-Based Crowding Degree Estimation for Many-Objective Optimization. In International Symposium on Intelligent Data Analysis (pp. 574-586). Springer, Cham.
Yang, W., Chen, L., Wang, Y., & Zhang, M. (2020). A reference points and intuitionistic fuzzy dominance based particle swarm algorithm for multi/many-objective optimization. Applied Intelligence, 50(4), 1133-1154.
Yu, K., Liang, J. J., Qu, B. Y., Chen, X., & Wang, H. (2017). Parameters identification of photovoltaic models using an improved JAYA optimization algorithm. Energy Conversion and Management, 150, 742-753.
Yu, K., Qu, B., Yue, C., Ge, S., Chen, X., & Liang, J. (2019). A performance-guided JAYA algorithm for parameters identification of photovoltaic cell and module. Applied Energy, 237, 241-257.
Yu, K., Wang, X., & Wang, Z. (2016). An improved teaching-learning-based optimization algorithm for numerical and engineering optimization problems. Journal of Intelligent Manufacturing, 27(4), 831-843.
Zamli, K. Z., Alsewari, A., & Ahmed, B. S. (2018). Multi-Start Jaya Algorithm for Software Module Clustering Problem. Azerbaijan Journal of High Performance Computing, 1, 87-112.
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.
Zhang, Y., Wang, G. G., Li, K., Yeh, W. C., Jian, M., & Dong, J. (2020). Enhancing MOEA/D with information feedback models for large-scale many-objective optimization. Information Sciences.
Zulvia, F. E., Kuo, R. J., & Nugroho, D. Y. (2020). A many-objective gradient evolution algorithm for solving a green vehicle routing problem with time windows and time dependency for perishable products. Journal of Cleaner Production, 242, 118428.
Bewoor, L. A., Chandra Prakash, V., & Sapkal, S. U. (2017). Evolutionary hybrid particle swarm optimization algorithm for solving NP-hard no-wait flow shop scheduling problems. Algorithms, 10(4), 121.
Blank, J., Deb, K., Dhebar, Y., Bandaru, S., & Seada, H. (2020). Generating well-spaced points on a unit simplex for evolutionary many-objective optimization. IEEE Transactions on Evolutionary Computation.
Champasak, P., Panagant, N., Pholdee, N., Bureerat, S., & Yildiz, A. R. (2020). Self-adaptive many-objective meta-heuristic based on decomposition for many-objective conceptual design of a fixed wing unmanned aerial vehicle. Aerospace Science and Technology, 100, 105783.
Chen, F., Ding, Z., Lu, Z., & Zeng, X. (2018). Parameters identification for chaotic systems based on a modified Jaya algorithm. Nonlinear Dynamics, 94(4), 2307-2326.
Chen, H., Cheng, R., Wen, J., Li, H., & Weng, J. (2020). Solving large-scale many-objective optimization problems by covariance matrix adaptation evolution strategy with scalable small subpopulations. Information Sciences, 509, 457-469.
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.
Chi, Y., Xu, Y., & Zhang, R. (2020). Many-objective Robust Optimization for Dynamic VAR Planning to Enhance Voltage Stability of a Wind-Energy Power System. IEEE Transactions on Power Delivery.
Črepinšek, M., Liu, S. H., & Mernik, M. (2013). Exploration and exploitation in evolutionary algorithms: A survey. ACM computing surveys (CSUR), 45(3), 1-33.
Cui, Z., Zhang, J., Wang, Y., Cao, Y., Cai, X., Zhang, W., & Chen, J. (2019). A pigeon-inspired optimization algorithm for many-objective optimization problems. Sci. China Information Science, 62(7), 70212-1.
Cui, Z., Zhang, J., Wu, D., Cai, X., Wang, H., Zhang, W., & Chen, J. (2020). Hybrid many-objective particle swarm optimization algorithm for green coal production problem. Information Sciences, 518, 256-271.
Deb, K., Thiele, L., Laumanns, M., & Zitzler, E. (2005). Scalable test problems for evolutionary multiobjective optimization. In Evolutionary multiobjective optimization (pp. 105-145). Springer, London.
Dhiman, G., Soni, M., Pandey, H. M., Slowik, A., & Kaur, H. (2020). A novel hybrid hypervolume indicator and reference vector adaptation strategies based evolutionary algorithm for many-objective optimization. Engineering with Computers, 1-19.
Di Wu, S. G., Cai, X., Zhang, G., & Xue, F. (2020). A many-objective optimization WSN energy balance model.
Farah, A., & Belazi, A. (2018). A novel chaotic Jaya algorithm for unconstrained numerical optimization. Nonlinear Dynamics, 93(3), 1451-1480.
Fritsche, G., & Pozo, A. (2020). The Analysis of a Cooperative Hyper-Heuristic on a Constrained Real-world Many-objective Continuous Problem, IEEE Congress on Evolutionary Computation (CEC), Glasgow, United Kingdom, pp. 1-8.
Gómez, R. H., Coello, C. A. C., & Alba, E. (2020). A Parallel Island Model for Hypervolume-Based Many-Objective Optimization. In High-Performance Simulation-Based Optimization (pp. 247-273). Springer, Cham.
Gong, C. (2017). An enhanced Jaya algorithm with a two group Adaption. International Journal of Computational Intelligence Systems, 10(1), 1102-1115.
Gong, D., Liu, Y., & Yen, G. G. (2020). A meta-objective approach for many-objective evolutionary optimization. Evolutionary computation, 28(1), 1-25.
Gu, Z. M., & Wang, G. G. (2020). Improving NSGA-III algorithms with information feedback models for large-scale many-objective optimization. Future Generation Computer Systems, 107, 49-69.
Helbig, M., & Engelbrecht, A. (2020, March). Partial Dominance for Many-Objective Optimization. In Proceedings of the 2020 4th International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence (pp. 81-86).
Hu, Z., Wei, Z., Ma, X., Sun, H., & Yang, J. (2020). Multi-parameter deep-perception and many-objective autonomous-control of rolling schedule on high speed cold tandem mill. ISA transactions.
Ingle, K. K., & Jatoth, R. K. (2020). An efficient JAYA algorithm with lévy flight for non-linear channel equalization. Expert Systems with Applications, 145, 112970.
Kaur, A., Sharma, S., & Mishra, A. (2019). A Novel Jaya-BAT Algorithm Based Power Consumption Minimization in Cognitive Radio Network. Wireless Personal Communications, 108(4), 2059-2075.
Kumar, A. S., & Venkatesan, M. (2019). Multi-Objective Task Scheduling Using Hybrid Genetic-Ant Colony Optimization Algorithm in Cloud Environment. Wireless Personal Communications, 107(4), 1835-1848.
Li, X., & Zhang, H. (2020). A multi-agent complex network algorithm for multi-objective optimization. Applied Intelligence, 1-28.
Liu, J., & Lansey, K. E. (2020). Multiphase DMA design methodology based on graph theory and many-objective optimization. Journal of Water Resources Planning and Management, 146(8), 04020068.
Liu, R., Liu, J., Zhou, R., Lian, C., & Bian, R. (2020). A region division based decomposition approach for evolutionary many-objective optimization. Knowledge-Based Systems, 105518.
Liu, Y., Zhu, N., & Li, M. (2020). Solving Many-Objective Optimization Problems by a Pareto-Based Evolutionary Algorithm With Preprocessing and a Penalty Mechanism. IEEE Transactions on Cybernetics.
Liu, Y., Zhu, N., Li, K., Li, M., Zheng, J., & Li, K. (2020). An angle dominance criterion for evolutionary many-objective optimization. Information Sciences, 509, 376-399.
Liu, Z. Z., Wang, Y., & Huang, P. Q. (2020). AnD: A many-objective evolutionary algorithm with angle-based selection and shift-based density estimation. Information Sciences, 509, 400-419.
Ma, L., Wang, R., Chen, S., Cheng, S., Wang, X., Lin, Z., ... & Huang, M. (2020). A novel many-objective evolutionary algorithm based on transfer matrix with Kriging model. Information Sciences, 509, 437-456.
Mane, S. U., & Rao, M. N. (2019). Large-Scale Compute-Intensive Constrained Optimization Problems: GPGPU-Based Approach. In Soft Computing: Theories and Applications (pp. 579-589). Springer, Singapore.
Mane, S., & Rao, M. N. (2017). Many-objective optimization: Problems and evolutionary algorithms–a short review. International Journal of Applied Engineering Research, 12(20), 9774-9793.
Mane, S., Narsingrao, M., & Patil, V. (2018). A many-objective Jaya algorithm for many-objective optimization problems. Decision Science Letters, 7(4), 567-582.
Meneghini, I. R., Alves, M. A., Gaspar-Cunha, A., & Guimarães, F. G. (2020). Scalable and customizable benchmark problems for many-objective optimization. Applied Soft Computing, 90, 106139.
Mohammed, R. T., Yaakob, R., Zaidan, A. A., Sharef, N. M., Abdullah, R. H., Zaidan, B. B., & Dawood, K. A. (2020). Review of the research landscape of multi-criteria evaluation and benchmarking processes for many-objective optimisation methods: coherent taxonomy, challenges and recommended solution. International Journal of Information Technology & Decision Making.
Ocłoń, P., Cisek, P., Rerak, M., Taler, D., Rao, R. V., Vallati, A., & Pilarczyk, M. (2018). Thermal performance optimization of the underground power cable system by using a modified Jaya algorithm. International Journal of Thermal Sciences, 123, 162-180.
Pawar, S. S., & Prasanth, Y. (2017). Multi-Objective Optimization Model for QoS-Enabled Web Service Selection in Service-Based Systems. New Review of Information Networking, 22(1), 34-53.
Qiu, H., & Duan, H. (2020). A multi-objective pigeon-inspired optimization approach to UAV distributed flocking among obstacles. Information Sciences, 509, 515-529.
Rajakumar, R., Amudhavel, J., Dhavachelvan, P., & Vengattaraman, T. (2017). GWO-LPWSN: Grey wolf optimization algorithm for node localization problem in wireless sensor networks. Journal of Computer Networks and Communications.
Rajeswari, M., Amudhavel, J., Pothula, S., & Dhavachelvan, P. (2017). Directed bee colony optimization algorithm to solve the nurse rostering problem. Computational intelligence and neuroscience, 2017.
Ramgouda, P., & Chandraprakash, V. (2019). Constraints handling in combinatorial interaction testing using multi-objective crow search and fruitfly optimization. Soft Computing, 23(8), 2713-2726.
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. (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. (2020). Rao algorithms: Three metaphor-less simple algorithms for solving optimization problems. International Journal of Industrial Engineering Computations, 11(1), 107-130.
Rao, R. V. (2019). Jaya: an advanced optimization algorithm and its engineering applications. Cham: Springer International Publishing.
Rao, R. V., & Keesari, H. S. (2018). Multi-team perturbation guiding Jaya algorithm for optimization of wind farm layout. Applied Soft Computing, 71, 800-815.
Rao, R. V., Keesari, H. S., Oclon, P., & Taler, J. (2019). Improved multi-objective Jaya optimization algorithm for a solar dish Stirling engine. Journal of Renewable and Sustainable Energy, 11(2), 025903.
Rao, R. V., Savsani, V. J., & Balic, J. (2012). Teaching–learning-based optimization algorithm for unconstrained and constrained real-parameter optimization problems. Engineering Optimization, 44(12), 1447-1462.
Rao, S. S. (2019). Engineering optimization: theory and practice. John Wiley & Sons.
Raut, U., & Mishra, S. (2019). An improved elitist–jaya algorithm for simultaneous network reconfiguration and dg allocation in power distribution systems. Renewable Energy Focus, 30, 92-106.
Raveendra, K., & Vinothkanna, R. (2019). Hybrid ant colony optimization model for image retrieval using scale-invariant feature transform local descriptor. Computers & Electrical Engineering, 74, 281-291.
Reddy, M. S., Ratnam, C., Rajyalakshmi, G., & Manupati, V. K. (2018). An effective hybrid multi objective evolutionary algorithm for solving real time event in flexible job shop scheduling problem. Measurement, 114, 78-90.
Sarzaeim, P., Bozorg-Haddad, O., & Chu, X. (2018). Teaching-learning-based optimization (TLBO) algorithm. In Advanced Optimization by Nature-Inspired Algorithms (pp. 51-58). Springer, Singapore.
Schütze, O., Cuate, O., Martín, A., Peitz, S., & Dellnitz, M. (2020). Pareto Explorer: a global/local exploration tool for many-objective optimization problems. Engineering Optimization, 52(5), 832-855.
Taha, K. (2020). Methods That Optimize Multi-Objective Problems: A Survey and Experimental Evaluation. IEEE Access, 8, 80855-80878.
Tanabe, R., & Ishibuchi, H. (2020). An easy-to-use real-world multi-objective optimization problem suite. Applied Soft Computing, 89, 106078.
Wang, R., Zhou, Z., Ishibuchi, H., Liao, T., & Zhang, T. (2016). Localized weighted sum method for many-objective optimization. IEEE Transactions on Evolutionary Computation, 22(1), 3-18.
Wu, C., & He, Y. (2020). Solving the set-union knapsack problem by a novel hybrid Jaya algorithm. Soft Computing, 24(3), 1883-1902.
Xue, Y., Li, M., & Liu, X. (2020, April). Angle-Based Crowding Degree Estimation for Many-Objective Optimization. In International Symposium on Intelligent Data Analysis (pp. 574-586). Springer, Cham.
Yang, W., Chen, L., Wang, Y., & Zhang, M. (2020). A reference points and intuitionistic fuzzy dominance based particle swarm algorithm for multi/many-objective optimization. Applied Intelligence, 50(4), 1133-1154.
Yu, K., Liang, J. J., Qu, B. Y., Chen, X., & Wang, H. (2017). Parameters identification of photovoltaic models using an improved JAYA optimization algorithm. Energy Conversion and Management, 150, 742-753.
Yu, K., Qu, B., Yue, C., Ge, S., Chen, X., & Liang, J. (2019). A performance-guided JAYA algorithm for parameters identification of photovoltaic cell and module. Applied Energy, 237, 241-257.
Yu, K., Wang, X., & Wang, Z. (2016). An improved teaching-learning-based optimization algorithm for numerical and engineering optimization problems. Journal of Intelligent Manufacturing, 27(4), 831-843.
Zamli, K. Z., Alsewari, A., & Ahmed, B. S. (2018). Multi-Start Jaya Algorithm for Software Module Clustering Problem. Azerbaijan Journal of High Performance Computing, 1, 87-112.
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.
Zhang, Y., Wang, G. G., Li, K., Yeh, W. C., Jian, M., & Dong, J. (2020). Enhancing MOEA/D with information feedback models for large-scale many-objective optimization. Information Sciences.
Zulvia, F. E., Kuo, R. J., & Nugroho, D. Y. (2020). A many-objective gradient evolution algorithm for solving a green vehicle routing problem with time windows and time dependency for perishable products. Journal of Cleaner Production, 242, 118428.