How to cite this paper
Premkumar, M., Jangir, P., Sowmya, R & Abualigah, L. (2023). MaOMFO: Many-objective moth flame optimizer using reference-point based non-dominated sorting mechanism for global optimization problems.Decision Science Letters , 12(3), 571-590.
Refrences
Abbasi, M., Abbasi, E., & Mohammadi-Ivatloo, B. (2021). Single and multi-objective optimal power flow using a new differential-based harmony search algorithm. Journal of Ambient Intelligence and Humanized Computing, 12(1), 851–871.
Behmanesh, R., Rahimi, I., Amir, ·, & Gandomi, H. (2021). Evolutionary Many-Objective Algorithms for Combinatorial Optimization Problems: A Comparative Study. Archives of Computational Methods in Engineering, 28, 673–688.
Bi, X., & Wang, C. (2018). A niche-elimination operation based NSGA-III algorithm for many-objective optimization. Applied Intelligence, 48(1), 118–141.
Branke, J., Kaußler, T., & Schmeck, H. (2001). Guidance in evolutionary multi-objective optimization. Advances in Engineering Software, 32(6), 499–507.
Brest, J., Maučec, M. S., & Bošković, B. (2017). Single objective real-parameter optimization: Algorithm jSO. 2017 IEEE Congress on Evolutionary Computation, CEC 2017 - Proceedings, 1311–1318.
Castro, O. R., Santana, R., Lozano, J. A., & Pozo, A. (2017). Combining CMA-ES and MOEA/DD for many-objective optimization. 2017 IEEE Congress on Evolutionary Computation, CEC 2017 - Proceedings, 1451–1458.
Coello Coello, C. A., González Brambila, S., Figueroa Gamboa, J., Castillo Tapia, M. G., & Hernández Gómez, R. (2019). Evolutionary multiobjective optimization: open research areas and some challenges lying ahead. Complex & Intelligent Systems, 6(2), 221–236.
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 Transactions on Evolutionary Computation, 18(4), 577–601.
Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6(2), 182–197.
Deb, K., Thiele, L., Laumanns, M., & Zitzler, E. (2002). Scalable multi-objective optimization test problems. Proceedings of the 2002 Congress on Evolutionary Computation. CEC’02 (Cat. No.02TH8600), 1, 825–830.
Deb, K., Thiele, L., Laumanns, M., & Zitzler, E. (2005). Scalable Test Problems for Evolutionary Multiobjective Optimization. In: Evolutionary Multiobjective Optimization, 105–145.
Dorigo, M., Maniezzo, V., & Colorni, A. (1996). Ant system: optimization by a colony of cooperating agents. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 26(1), 29–41.
Duan, H., Zhao, W., Wang, G., & Feng, X. (2012). Test-sheet composition using analytic hierarchy process and hybrid metaheuristic algorithm TS/BBO. Mathematical Problems in Engineering, 2012, 712752.
Elsayed, S., & Sarker, R. (2016). Differential evolution framework for big data optimization. Memetic Computing, 8(1), 17–33.
Erickson, M., Mayer, A., & Horn, J. (2001). The niched pareto genetic algorithm 2 applied to the design of groundwater remediation systems. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 1993, 681–695.
Fan, Z., Wang, T., Cheng, Z., Li, G., & Gu, F. (2017). An Improved Multiobjective Particle Swarm Optimization Algorithm Using Minimum Distance of Point to Line. Shock and Vibration, 2017, 25-45.
Fonseca, C. M., & Fleming, P. J. (1993). Genetic Algorithms for Multiobjective Optimization: FormulationDiscussion and Generalization. Proceedings of the 5th International Conference on Genetic Algorithms, 416–423.
Guo, X., & Wang, X. (2019). A Novel Objective Grouping Evolutionary Algorithm for Many-Objective Optimization Problems. International Journal of Pattern Recognition and Artificial Intelligence, 34(6), 2059018.
He, Z., & Yen, G. G. (2014). Many-Objective Evolutionary Algorithms and Hybrid Performance Metrics. In Frontiers of Intelligent Control and Information Processing, 335–364.
Horn, J., Nafpliotis, N., & Goldberg, D. E. (1994). A niched Pareto genetic algorithm for multiobjective optimization. Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence, 82–87 vol.1.
Ibrahim, A., Rahnamayan, S., Martin, M. V., & Deb, K. (2016). EliteNSGA-III: An improved evolutionary many-objective optimization algorithm. 2016 IEEE Congress on Evolutionary Computation, CEC 2016, 973–982.
Jain, H., & Deb, K. (2014). An evolutionary many-objective optimization algorithm using reference-point based nondominated sorting approach, Part II: Handling constraints and extending to an adaptive approach. IEEE Transactions on Evolutionary Computation, 18(4), 602–622.
Jangir, P., Manoharan, P., Pandya, S & Sowmya, R. (2023). MaOTLBO: Many-objective teaching-learning-based optimizer for control and monitoring the optimal power flow of modern power systems. International Journal of Industrial Engineering Computations, 14(2), 293-308.
Johari, N. F., Zain, A. M., Noorfa, M. H., & Udin, A. (2013). Firefly Algorithm for Optimization Problem. Applied Mechanics and Materials, 421, 512–517.
Ke, L., Zhang, Q., & Battiti, R. (2013). MOEA/D-ACO: A multiobjective evolutionary algorithm using decomposition and AntColony. IEEE Transactions on Cybernetics, 43(6), 1845–1859.
Kirkpatrick, S., Gelatt, C. D., & Vecchi, M. P. (1983). Optimization by Simulated Annealing. Science, 220(4598), 671–680.
Kumar, B. V., Oliva, D., & Suganthan, P. N. (Eds.). (2022). Differential Evolution: From Theory to Practice. 1009.
Kumar, S., Jangir, P., Tejani, G. G., & Premkumar, M. (2022). A Decomposition based Multi-Objective Heat Transfer Search algorithm for structure optimization. Knowledge-Based Systems, 253, 109591.
Kumar, S., Jangir, P., Tejani, G. G., Premkumar, M., & Alhelou, H. H. (2021). MOPGO: A New Physics-Based Multi-Objective Plasma Generation Optimizer for Solving Structural Optimization Problems. IEEE Access, 9, 84982–85016.
Li, B., Li, J., Tang, K., & Yao, X. (2015). Many-Objective Evolutionary Algorithms. ACM Computing Surveys (CSUR), 48(1), 2792984.
Li, H., & Zhang, Q. (2009). Multiobjective Optimization Problems With Complicated Pareto Sets, MOEA/D and NSGA-II. IEEE Transactions on Evolutionary Computation, 13(2), 284–302.
Li, M., Yang, S., Liu, X., & Shen, R. (2013). A comparative study on evolutionary algorithms for many-objective optimization. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7811 LNCS, 261–275.
Lin, Q., Liu, S., Zhu, Q., Tang, C., Song, R., Chen, J., Coello, C. A. C., Wong, K. C., & Zhang, J. (2018). Particle Swarm Optimization with a Balanceable Fitness Estimation for Many-Objective Optimization Problems. IEEE Transactions on Evolutionary Computation, 22(1), 32–46.
Liu, Y., Gong, D., Sun, X., & Zhang, Y. (2017). Many-objective evolutionary optimization based on reference points. Applied Soft Computing Journal, 50, 344–355.
Luo, W., Shi, L., Lin, X., & Coello Coello, C. A. (2019). The g-dominance Relation for Preference-Based Evolutionary Multi-Objective Optimization. 2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings, 2418–2425.
Mirjalili, S. (2015). Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm. Knowledge-Based Systems, 89, 228–249.
Mirjalili, S. (2016). Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems. Neural Computing and Applications, 27(4), 1053–1073.
Mirjalili, S., Mirjalili, S. M., & Lewis, A. (2014). Grey Wolf Optimizer. Advances in Engineering Software, 69, 46–61.
Mousa, A. A., El-Shorbagy, M. A., & Abd-El-Wahed, W. F. (2012). Local search based hybrid particle swarm optimization algorithm for multiobjective optimization. Swarm and Evolutionary Computation, 3, 1–14.
Premkumar, M., Jangir, P., Kumar, B. S., Alqudah, M. A., & Nisar, K. S. (2022). Multi-objective grey Wolf optimization algorithm for solving real-world bldc motor design problem. Computers, Materials and Continua, 70(2), 2435–2452.
Premkumar, M., Jangir, P., Santhosh Kumar, B., Sowmya, R., Haes Alhelou, H., Abualigah, L., Riza Yildiz, A., Mirjalili, S., & Premkumar, M. (2021). A New Arithmetic Optimization Algorithm for Solving Real-World Multiobjective CEC-2021 Constrained Optimization Problems: Diversity Analysis and Validations. IEEE Access, 9, 84263–84295.
Premkumar, M., Jangir, P., & Sowmya, R. (2021). MOGBO: A new Multiobjective Gradient-Based Optimizer for real-world structural optimization problems. Knowledge-Based Systems, 218, 106856.
Premkumar, M., Jangir, P., Sowmya, R., & Elavarasan, R. M. (2021). Many-Objective Gradient-Based Optimizer to Solve Optimal Power Flow Problems: Analysis and Validations. Engineering Applications of Artificial Intelligence, 106, 104479.
Premkumar, M., Kumar, C., Sowmya, R., & Pradeep, J. (2021). A novel salp swarm assisted hybrid maximum power point tracking algorithm for the solar photovoltaic power generation systems. Automatika, 62(1), 14-25.
Premkumar, M., Sowmya, R., Jangir, P., Haes Alhelou, H., Heidari, A. A., & Huling Chen. (2021). MOSMA: Multi-Objective Slime Mould Algorithm Based on Elitist Non-Dominated Sorting. IEEE Access, 9, 3229–3248.
Premkumar, M., Sowmya, R., Jangir, P., & Siva Kumar, J. S. V. (2020). A New and Reliable Objective Functions for Extracting the Unknown Parameters of Solar Photovoltaic Cell Using Political Optimizer Algorithm. 2020 International Conference on Data Analytics for Business and Industry: Way Towards a Sustainable Economy, ICDABI 2020, 1-8.
Premkumar, M., & Sumithira, R. (2018). Humpback whale assisted hybrid maximum power point tracking algorithm for partially shaded solar photovoltaic systems. Journal of Power Electronics, 18(6), 1805–1818.
Rao, R. V. (2020). Rao algorithms: Three metaphor-less simple algorithms for solving optimization problems. International Journal of Industrial Engineering Computations, 11(1), 107–130.
Schaffer, J. D. (1984). Some Experiments in Machine Learning Using Vector Evaluated Genetic Algorithms (Artificial Intelligence, Optimization, Adaptation, Pattern Recognition).
Sierra, M., & Coello, C. (2005). Improving PSO-Based Multi-objective Optimization Using Crowding, Mutation and ∈-Dominance. Lecture Notes in Computer Science, 3410.
Stanovov, V., Akhmedova, S., & Semenkin, E. (2020). Differential Evolution with Linear Bias Reduction in Parameter Adaptation. Algorithms, 13(11), 283.
Tian, Y., Cheng, R., Zhang, X., & Jin, Y. (2017). PlatEMO: A matlab platform for evolutionary multi-objective optimization. IEEE Computational Intelligence Magazine, 12(4), 73–87.
Vachhani, V. L., Dabhi, V. K., & Prajapati, H. B. (2016). Improving NSGA-II for solving multi objective function optimization problems. 2016 International Conference on Computer Communication and Informatics, ICCCI 2016, 1-8.
Venkata 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.
Vesikar, Y., Deb, K., & Blank, J. (2019). Reference Point Based NSGA-III for Preferred Solutions. Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence, SSCI 2018, 1587–1594.
Wang, S., Zhou, Y., & Zhang, J. (2018). An Improved NSGA-III Approach to Many-Objective Optimal Power Flow Problems. 2018 Chinese Automation Congress (CAC), 2664–2669.
Wolpert, D. H., & Macready, W. G. (1997). No Free Lunch Theorems for Optimization. IEEE Transactions On Evolutionary Computation, 1(1), 67–82.
Yang, X.-S. (2021). Genetic Algorithms. Nature-Inspired Optimization Algorithms, 91–100.
Zhang, J., Wang, S., Tang, Q., Zhou, Y., & Zeng, T. (2019). An improved NSGA-III integrating adaptive elimination strategy to solution of many-objective optimal power flow problems. Energy, 172, 945–957.
Zhang, Q., & Li, H. (2007). MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition. IEEE Transactions on Evolutionary Computation, 11(6), 712–731.
Zheng, W., Tan, Y., Meng, L., & Zhang, H. (2018). An improved MOEA/D design for many-objective optimization problems. Applied Intelligence, 48(10), 3839–3861.
Zhou, A., Qu, B. Y., Li, H., Zhao, S. Z., Suganthan, P. N., & Zhangd, Q. (2011). Multiobjective evolutionary algorithms: A survey of the state of the art. Swarm and Evolutionary Computation, 1(1), 32–49.
Zhou, A., Zhang, Q., & Zhang, G. (2012). A multiobjective evolutionary algorithm based on decomposition and probability model. 2012 IEEE Congress on Evolutionary Computation, CEC 2012, 1-8.
Zhou, J., Wang, C., Li, Y., Wang, P., Li, C., Lu, P., & Mo, L. (2017). A multi-objective multi-population ant colony optimization for economic emission dispatch considering power system security. Applied Mathematical Modelling, 45, 684–704.
Behmanesh, R., Rahimi, I., Amir, ·, & Gandomi, H. (2021). Evolutionary Many-Objective Algorithms for Combinatorial Optimization Problems: A Comparative Study. Archives of Computational Methods in Engineering, 28, 673–688.
Bi, X., & Wang, C. (2018). A niche-elimination operation based NSGA-III algorithm for many-objective optimization. Applied Intelligence, 48(1), 118–141.
Branke, J., Kaußler, T., & Schmeck, H. (2001). Guidance in evolutionary multi-objective optimization. Advances in Engineering Software, 32(6), 499–507.
Brest, J., Maučec, M. S., & Bošković, B. (2017). Single objective real-parameter optimization: Algorithm jSO. 2017 IEEE Congress on Evolutionary Computation, CEC 2017 - Proceedings, 1311–1318.
Castro, O. R., Santana, R., Lozano, J. A., & Pozo, A. (2017). Combining CMA-ES and MOEA/DD for many-objective optimization. 2017 IEEE Congress on Evolutionary Computation, CEC 2017 - Proceedings, 1451–1458.
Coello Coello, C. A., González Brambila, S., Figueroa Gamboa, J., Castillo Tapia, M. G., & Hernández Gómez, R. (2019). Evolutionary multiobjective optimization: open research areas and some challenges lying ahead. Complex & Intelligent Systems, 6(2), 221–236.
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 Transactions on Evolutionary Computation, 18(4), 577–601.
Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6(2), 182–197.
Deb, K., Thiele, L., Laumanns, M., & Zitzler, E. (2002). Scalable multi-objective optimization test problems. Proceedings of the 2002 Congress on Evolutionary Computation. CEC’02 (Cat. No.02TH8600), 1, 825–830.
Deb, K., Thiele, L., Laumanns, M., & Zitzler, E. (2005). Scalable Test Problems for Evolutionary Multiobjective Optimization. In: Evolutionary Multiobjective Optimization, 105–145.
Dorigo, M., Maniezzo, V., & Colorni, A. (1996). Ant system: optimization by a colony of cooperating agents. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 26(1), 29–41.
Duan, H., Zhao, W., Wang, G., & Feng, X. (2012). Test-sheet composition using analytic hierarchy process and hybrid metaheuristic algorithm TS/BBO. Mathematical Problems in Engineering, 2012, 712752.
Elsayed, S., & Sarker, R. (2016). Differential evolution framework for big data optimization. Memetic Computing, 8(1), 17–33.
Erickson, M., Mayer, A., & Horn, J. (2001). The niched pareto genetic algorithm 2 applied to the design of groundwater remediation systems. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 1993, 681–695.
Fan, Z., Wang, T., Cheng, Z., Li, G., & Gu, F. (2017). An Improved Multiobjective Particle Swarm Optimization Algorithm Using Minimum Distance of Point to Line. Shock and Vibration, 2017, 25-45.
Fonseca, C. M., & Fleming, P. J. (1993). Genetic Algorithms for Multiobjective Optimization: FormulationDiscussion and Generalization. Proceedings of the 5th International Conference on Genetic Algorithms, 416–423.
Guo, X., & Wang, X. (2019). A Novel Objective Grouping Evolutionary Algorithm for Many-Objective Optimization Problems. International Journal of Pattern Recognition and Artificial Intelligence, 34(6), 2059018.
He, Z., & Yen, G. G. (2014). Many-Objective Evolutionary Algorithms and Hybrid Performance Metrics. In Frontiers of Intelligent Control and Information Processing, 335–364.
Horn, J., Nafpliotis, N., & Goldberg, D. E. (1994). A niched Pareto genetic algorithm for multiobjective optimization. Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence, 82–87 vol.1.
Ibrahim, A., Rahnamayan, S., Martin, M. V., & Deb, K. (2016). EliteNSGA-III: An improved evolutionary many-objective optimization algorithm. 2016 IEEE Congress on Evolutionary Computation, CEC 2016, 973–982.
Jain, H., & Deb, K. (2014). An evolutionary many-objective optimization algorithm using reference-point based nondominated sorting approach, Part II: Handling constraints and extending to an adaptive approach. IEEE Transactions on Evolutionary Computation, 18(4), 602–622.
Jangir, P., Manoharan, P., Pandya, S & Sowmya, R. (2023). MaOTLBO: Many-objective teaching-learning-based optimizer for control and monitoring the optimal power flow of modern power systems. International Journal of Industrial Engineering Computations, 14(2), 293-308.
Johari, N. F., Zain, A. M., Noorfa, M. H., & Udin, A. (2013). Firefly Algorithm for Optimization Problem. Applied Mechanics and Materials, 421, 512–517.
Ke, L., Zhang, Q., & Battiti, R. (2013). MOEA/D-ACO: A multiobjective evolutionary algorithm using decomposition and AntColony. IEEE Transactions on Cybernetics, 43(6), 1845–1859.
Kirkpatrick, S., Gelatt, C. D., & Vecchi, M. P. (1983). Optimization by Simulated Annealing. Science, 220(4598), 671–680.
Kumar, B. V., Oliva, D., & Suganthan, P. N. (Eds.). (2022). Differential Evolution: From Theory to Practice. 1009.
Kumar, S., Jangir, P., Tejani, G. G., & Premkumar, M. (2022). A Decomposition based Multi-Objective Heat Transfer Search algorithm for structure optimization. Knowledge-Based Systems, 253, 109591.
Kumar, S., Jangir, P., Tejani, G. G., Premkumar, M., & Alhelou, H. H. (2021). MOPGO: A New Physics-Based Multi-Objective Plasma Generation Optimizer for Solving Structural Optimization Problems. IEEE Access, 9, 84982–85016.
Li, B., Li, J., Tang, K., & Yao, X. (2015). Many-Objective Evolutionary Algorithms. ACM Computing Surveys (CSUR), 48(1), 2792984.
Li, H., & Zhang, Q. (2009). Multiobjective Optimization Problems With Complicated Pareto Sets, MOEA/D and NSGA-II. IEEE Transactions on Evolutionary Computation, 13(2), 284–302.
Li, M., Yang, S., Liu, X., & Shen, R. (2013). A comparative study on evolutionary algorithms for many-objective optimization. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7811 LNCS, 261–275.
Lin, Q., Liu, S., Zhu, Q., Tang, C., Song, R., Chen, J., Coello, C. A. C., Wong, K. C., & Zhang, J. (2018). Particle Swarm Optimization with a Balanceable Fitness Estimation for Many-Objective Optimization Problems. IEEE Transactions on Evolutionary Computation, 22(1), 32–46.
Liu, Y., Gong, D., Sun, X., & Zhang, Y. (2017). Many-objective evolutionary optimization based on reference points. Applied Soft Computing Journal, 50, 344–355.
Luo, W., Shi, L., Lin, X., & Coello Coello, C. A. (2019). The g-dominance Relation for Preference-Based Evolutionary Multi-Objective Optimization. 2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings, 2418–2425.
Mirjalili, S. (2015). Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm. Knowledge-Based Systems, 89, 228–249.
Mirjalili, S. (2016). Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems. Neural Computing and Applications, 27(4), 1053–1073.
Mirjalili, S., Mirjalili, S. M., & Lewis, A. (2014). Grey Wolf Optimizer. Advances in Engineering Software, 69, 46–61.
Mousa, A. A., El-Shorbagy, M. A., & Abd-El-Wahed, W. F. (2012). Local search based hybrid particle swarm optimization algorithm for multiobjective optimization. Swarm and Evolutionary Computation, 3, 1–14.
Premkumar, M., Jangir, P., Kumar, B. S., Alqudah, M. A., & Nisar, K. S. (2022). Multi-objective grey Wolf optimization algorithm for solving real-world bldc motor design problem. Computers, Materials and Continua, 70(2), 2435–2452.
Premkumar, M., Jangir, P., Santhosh Kumar, B., Sowmya, R., Haes Alhelou, H., Abualigah, L., Riza Yildiz, A., Mirjalili, S., & Premkumar, M. (2021). A New Arithmetic Optimization Algorithm for Solving Real-World Multiobjective CEC-2021 Constrained Optimization Problems: Diversity Analysis and Validations. IEEE Access, 9, 84263–84295.
Premkumar, M., Jangir, P., & Sowmya, R. (2021). MOGBO: A new Multiobjective Gradient-Based Optimizer for real-world structural optimization problems. Knowledge-Based Systems, 218, 106856.
Premkumar, M., Jangir, P., Sowmya, R., & Elavarasan, R. M. (2021). Many-Objective Gradient-Based Optimizer to Solve Optimal Power Flow Problems: Analysis and Validations. Engineering Applications of Artificial Intelligence, 106, 104479.
Premkumar, M., Kumar, C., Sowmya, R., & Pradeep, J. (2021). A novel salp swarm assisted hybrid maximum power point tracking algorithm for the solar photovoltaic power generation systems. Automatika, 62(1), 14-25.
Premkumar, M., Sowmya, R., Jangir, P., Haes Alhelou, H., Heidari, A. A., & Huling Chen. (2021). MOSMA: Multi-Objective Slime Mould Algorithm Based on Elitist Non-Dominated Sorting. IEEE Access, 9, 3229–3248.
Premkumar, M., Sowmya, R., Jangir, P., & Siva Kumar, J. S. V. (2020). A New and Reliable Objective Functions for Extracting the Unknown Parameters of Solar Photovoltaic Cell Using Political Optimizer Algorithm. 2020 International Conference on Data Analytics for Business and Industry: Way Towards a Sustainable Economy, ICDABI 2020, 1-8.
Premkumar, M., & Sumithira, R. (2018). Humpback whale assisted hybrid maximum power point tracking algorithm for partially shaded solar photovoltaic systems. Journal of Power Electronics, 18(6), 1805–1818.
Rao, R. V. (2020). Rao algorithms: Three metaphor-less simple algorithms for solving optimization problems. International Journal of Industrial Engineering Computations, 11(1), 107–130.
Schaffer, J. D. (1984). Some Experiments in Machine Learning Using Vector Evaluated Genetic Algorithms (Artificial Intelligence, Optimization, Adaptation, Pattern Recognition).
Sierra, M., & Coello, C. (2005). Improving PSO-Based Multi-objective Optimization Using Crowding, Mutation and ∈-Dominance. Lecture Notes in Computer Science, 3410.
Stanovov, V., Akhmedova, S., & Semenkin, E. (2020). Differential Evolution with Linear Bias Reduction in Parameter Adaptation. Algorithms, 13(11), 283.
Tian, Y., Cheng, R., Zhang, X., & Jin, Y. (2017). PlatEMO: A matlab platform for evolutionary multi-objective optimization. IEEE Computational Intelligence Magazine, 12(4), 73–87.
Vachhani, V. L., Dabhi, V. K., & Prajapati, H. B. (2016). Improving NSGA-II for solving multi objective function optimization problems. 2016 International Conference on Computer Communication and Informatics, ICCCI 2016, 1-8.
Venkata 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.
Vesikar, Y., Deb, K., & Blank, J. (2019). Reference Point Based NSGA-III for Preferred Solutions. Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence, SSCI 2018, 1587–1594.
Wang, S., Zhou, Y., & Zhang, J. (2018). An Improved NSGA-III Approach to Many-Objective Optimal Power Flow Problems. 2018 Chinese Automation Congress (CAC), 2664–2669.
Wolpert, D. H., & Macready, W. G. (1997). No Free Lunch Theorems for Optimization. IEEE Transactions On Evolutionary Computation, 1(1), 67–82.
Yang, X.-S. (2021). Genetic Algorithms. Nature-Inspired Optimization Algorithms, 91–100.
Zhang, J., Wang, S., Tang, Q., Zhou, Y., & Zeng, T. (2019). An improved NSGA-III integrating adaptive elimination strategy to solution of many-objective optimal power flow problems. Energy, 172, 945–957.
Zhang, Q., & Li, H. (2007). MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition. IEEE Transactions on Evolutionary Computation, 11(6), 712–731.
Zheng, W., Tan, Y., Meng, L., & Zhang, H. (2018). An improved MOEA/D design for many-objective optimization problems. Applied Intelligence, 48(10), 3839–3861.
Zhou, A., Qu, B. Y., Li, H., Zhao, S. Z., Suganthan, P. N., & Zhangd, Q. (2011). Multiobjective evolutionary algorithms: A survey of the state of the art. Swarm and Evolutionary Computation, 1(1), 32–49.
Zhou, A., Zhang, Q., & Zhang, G. (2012). A multiobjective evolutionary algorithm based on decomposition and probability model. 2012 IEEE Congress on Evolutionary Computation, CEC 2012, 1-8.
Zhou, J., Wang, C., Li, Y., Wang, P., Li, C., Lu, P., & Mo, L. (2017). A multi-objective multi-population ant colony optimization for economic emission dispatch considering power system security. Applied Mathematical Modelling, 45, 684–704.