How to cite this paper
Yılmaz, ., Altun, A & Köklü, M. (2022). A new hybrid algorithm based on MVO and SA for function optimization.International Journal of Industrial Engineering Computations , 13(2), 237-254.
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Abasi, A. K., Khader, A. T., Al-Betar, M. A., Naim, S., Alyasseri, Z. A. A., & Makhadmeh, S. N. (2020). A novel hybrid multi-verse optimizer with K-means for text documents clustering. Neural Comput. Appl., 32(23), 17703-17729.
Abdel-Basset, M., Ding, W., & El-Shahat, D. (2021). A hybrid Harris Hawks optimization algorithm with simulated annealing for feature selection. Artificial Intelligence Review, 54(1), 593-637.
Alizada, B. (2019). Hybridization of swarm-based ant lion and whale optimization algorithms with physics-based algorithms. (Master's thesis, Erciyes University). Erciyes University Research Information System. https://avesis.erciyes.edu.tr/file?id=8a77bd62-5fea-44cb-a5f3-d61118c8a9ab
Alkhateeb, F., & Abed-Alguni, B. H. (2019). A hybrid cuckoo search and simulated annealing algorithm. Journal of Intelligent Systems, 28(4), 683-698.
Blum, C., & Roli, A. (2003). Metaheuristics in combinatorial optimization: Overview and conceptual comparison. ACM computing surveys (CSUR), 35(3), 268-308.
Chen, L., Li, L., & Kuang, W. (2021). A hybrid multiverse optimisation algorithm based on differential evolution and adaptive mutation. Journal of Experimental & Theoretical Artificial Intelligence, 33(2), 239-261.
Chu, S. C., Tsai, P. W., & Pan, J. S. (2006, August). Cat swarm optimization. In Pacific Rim international conference on artificial intelligence (pp. 854-858). Springer, Berlin, Heidelberg.
Colorni, A., Dorigo, M., & Maniezzo, V. (1991, December). Distributed optimization by ant colonies. In Proceedings of the first European conference on artificial life (Vol. 142, pp. 134-142).
Dupanloup, I., Schneider, S., & Excoffier, L. (2002). A simulated annealing approach to define the genetic structure of populations. Molecular ecology, 11(12), 2571-2581.
Eglese, R. W. (1990). Simulated annealing: a tool for operational research. European journal of operational research, 46(3), 271-281.
Faris, H., Aljarah, I., Mirjalili, S., Castillo, P. A., & Guervós, J. J. M. (2016, November). EvoloPy: An Open-source Nature-inspired Optimization Framework in Python. In IJCCI (ECTA) (pp. 171-177).
Hawking, S. W. (1988). The Illustrated A Brief History of Time: Updated and Expanded Edition. Bantam Dell Publishing Group.
Heidari, A. A., Mirjalili, S., Faris, H., Aljarah, I., Mafarja, M., & Chen, H. (2019). Harris hawks optimization: Algorithm and applications. Future generation computer systems, 97, 849-872.
Henderson, D., Jacobson, S. H., & Johnson, A. W. (2003). The theory and practice of simulated annealing. In Handbook of metaheuristics (pp. 287-319). Springer, Boston, MA.
Jangir, P., Parmar, S. A., Trivedi, I. N., & Bhesdadiya, R. H. (2017). A novel hybrid particle swarm optimizer with multi verse optimizer for global numerical optimization and optimal reactive power dispatch problem. Engineering Science and Technology, an International Journal, 20(2), 570-586.
Jia, H., Peng, X., Song, W., Lang, C., Xing, Z., & Sun, K. (2019). Hybrid multiverse optimization algorithm with gravitational search algorithm for multithreshold color image segmentation. IEEE Access, 7, 44903-44927.
Jovanovic, R., & Tuba, M. (2013). Ant colony optimization algorithm with pheromone correction strategy for the minimum connected dominating set problem. Computer Science and Information Systems, 10(1), 133-149.
Karaboga, D. (2005). An idea based on honey bee swarm for numerical optimization (Vol. 200, pp. 1-10). Technical report-tr06, Erciyes university, engineering faculty, computer engineering department.
Serkan, K. A. Y. A., & FIĞLALI, N. (2018). Çok amaçlı esnek atölye tipi çizelgeleme problemlerinin çözümünde meta sezgisel yöntemlerin kullanımı. Harran Üniversitesi Mühendislik Dergisi, 3(3), 222-233.
Kennedy, J., & Eberhart, R. (1995, November). Particle swarm optimization. In Proceedings of ICNN'95-international conference on neural networks (Vol. 4, pp. 1942-1948). IEEE.
Khurma, R. A., Aljarah, I., Sharieh, A., & Mirjalili, S. (2020). Evolopy-fs: An open-source nature-inspired optimization framework in python for feature selection. In Evolutionary Machine Learning Techniques (pp. 131-173). Springer, Singapore.
Kirkpatrick, S., Gelatt, C. D., & Vecchi, M. P. (1983). Optimization by simulated annealing. science, 220(4598), 671-680.
Komossa, S. (2015). Tidal disruption of stars by supermassive black holes: Status of observations. Journal of High Energy Astrophysics, 7, 148-157.
Laporte, G., Gendreau, M., Potvin, J. Y., & Semet, F. (2000). Classical and modern heuristics for the vehicle routing problem. International transactions in operational research, 7(4‐5), 285-300.
Li, X., Zhang, J., & Yin, M. (2014). Animal migration optimization: an optimization algorithm inspired by animal migration behavior. Neural Computing and Applications, 24(7), 1867-1877.
Luo, Q., Ma, M., & Zhou, Y. (2016). A novel animal migration algorithm for global numerical optimization. Computer Science and Information Systems, 13(1), 259-285.
Mafarja, M. M., & Mirjalili, S. (2017). Hybrid whale optimization algorithm with simulated annealing for feature selection. Neurocomputing, 260, 302-312.
Mirjalili, S., & Hashim, S. Z. M. (2010, December). A new hybrid PSOGSA algorithm for function optimization. In 2010 international conference on computer and information application (pp. 374-377). IEEE.
Mirjalili, S. (2015). Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm. Knowledge-based systems, 89, 228-249.
Mirjalili, S., Mirjalili, S. M., & Hatamlou, A. (2016). Multi-verse optimizer: a nature-inspired algorithm for global optimization. Neural Computing and Applications, 27(2), 495-513.
Mirjalili, S., Gandomi, A. H., Mirjalili, S. Z., Saremi, S., Faris, H., & Mirjalili, S. M. (2017). Salp Swarm Algorithm: A bio-inspired optimizer for engineering design problems. Advances in Engineering Software, 114, 163-191.
Murty, K. G. (2003). Optimization models for decision making: Volume. University of Michigan, Ann Arbor.
Pan, X., Xue, L., Lu, Y., & Sun, N. (2019). Hybrid particle swarm optimization with simulated annealing. Multimedia Tools and Applications, 78(21), 29921-29936.
Qaddoura, R., Faris, H., Aljarah, I., & Castillo, P. A. (2021). EvoCluster: An Open-Source Nature-Inspired Optimization Clustering Framework. SN Computer Science, 2(3), 1-12.
Rashedi, E., Nezamabadi-Pour, H., & Saryazdi, S. (2009). GSA: a gravitational search algorithm. Information sciences, 179(13), 2232-2248.
Sayed, G. I., & Hassanien, A. E. (2018). A hybrid SA-MFO algorithm for function optimization and engineering design problems. Complex & Intelligent Systems, 4(3), 195-212.
Sayed, G. I., Darwish, A., & Hassanien, A. E. (2019). Quantum multiverse optimization algorithm for optimization problems. Neural Computing and Applications, 31(7), 2763-2780.
Song, R., Zeng, X., & Han, R. (2020). An Improved Multi-Verse Optimizer Algorithm For Multi-Source Allocation Problem. International Journal of Innovative Computing, Information and Control, 16(6), 1845–1862.
Storn, R. (1996, June). On the usage of differential evolution for function optimization. In Proceedings of North American Fuzzy Information Processing (pp. 519-523). IEEE.
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.
Talbi, E. G. (2002). A taxonomy of hybrid metaheuristics. Journal of heuristics, 8(5), 541-564.
Talbi, E. G. (2009). Metaheuristics: from design to implementation (Vol. 74). John Wiley & Sons.
Ting, T. O., Yang, X. S., Cheng, S., & Huang, K. (2015). Hybrid metaheuristic algorithms: past, present, and future. Recent advances in swarm intelligence and evolutionary computation, 71-83.
Wang, C., Lin, M., Zhong, Y., & Zhang, H. (2016). Swarm simulated annealing algorithm with knowledge-based sampling for travelling salesman problem. International Journal of Intelligent Systems Technologies and Applications, 15(1), 74-94.
Wilcoxon, F. (1992). Individual comparisons by ranking methods. In Breakthroughs in statistics (pp. 196-202). Springer, New York, NY.
Yang, X. S. (2010). Nature-inspired metaheuristic algorithms. Luniver press.
Yang, X. S., & Deb, S. (2009, December). Cuckoo search via Lévy flights. In 2009 World congress on nature & biologically inspired computing (NaBIC) (pp. 210-214). IEEE.
Abdel-Basset, M., Ding, W., & El-Shahat, D. (2021). A hybrid Harris Hawks optimization algorithm with simulated annealing for feature selection. Artificial Intelligence Review, 54(1), 593-637.
Alizada, B. (2019). Hybridization of swarm-based ant lion and whale optimization algorithms with physics-based algorithms. (Master's thesis, Erciyes University). Erciyes University Research Information System. https://avesis.erciyes.edu.tr/file?id=8a77bd62-5fea-44cb-a5f3-d61118c8a9ab
Alkhateeb, F., & Abed-Alguni, B. H. (2019). A hybrid cuckoo search and simulated annealing algorithm. Journal of Intelligent Systems, 28(4), 683-698.
Blum, C., & Roli, A. (2003). Metaheuristics in combinatorial optimization: Overview and conceptual comparison. ACM computing surveys (CSUR), 35(3), 268-308.
Chen, L., Li, L., & Kuang, W. (2021). A hybrid multiverse optimisation algorithm based on differential evolution and adaptive mutation. Journal of Experimental & Theoretical Artificial Intelligence, 33(2), 239-261.
Chu, S. C., Tsai, P. W., & Pan, J. S. (2006, August). Cat swarm optimization. In Pacific Rim international conference on artificial intelligence (pp. 854-858). Springer, Berlin, Heidelberg.
Colorni, A., Dorigo, M., & Maniezzo, V. (1991, December). Distributed optimization by ant colonies. In Proceedings of the first European conference on artificial life (Vol. 142, pp. 134-142).
Dupanloup, I., Schneider, S., & Excoffier, L. (2002). A simulated annealing approach to define the genetic structure of populations. Molecular ecology, 11(12), 2571-2581.
Eglese, R. W. (1990). Simulated annealing: a tool for operational research. European journal of operational research, 46(3), 271-281.
Faris, H., Aljarah, I., Mirjalili, S., Castillo, P. A., & Guervós, J. J. M. (2016, November). EvoloPy: An Open-source Nature-inspired Optimization Framework in Python. In IJCCI (ECTA) (pp. 171-177).
Hawking, S. W. (1988). The Illustrated A Brief History of Time: Updated and Expanded Edition. Bantam Dell Publishing Group.
Heidari, A. A., Mirjalili, S., Faris, H., Aljarah, I., Mafarja, M., & Chen, H. (2019). Harris hawks optimization: Algorithm and applications. Future generation computer systems, 97, 849-872.
Henderson, D., Jacobson, S. H., & Johnson, A. W. (2003). The theory and practice of simulated annealing. In Handbook of metaheuristics (pp. 287-319). Springer, Boston, MA.
Jangir, P., Parmar, S. A., Trivedi, I. N., & Bhesdadiya, R. H. (2017). A novel hybrid particle swarm optimizer with multi verse optimizer for global numerical optimization and optimal reactive power dispatch problem. Engineering Science and Technology, an International Journal, 20(2), 570-586.
Jia, H., Peng, X., Song, W., Lang, C., Xing, Z., & Sun, K. (2019). Hybrid multiverse optimization algorithm with gravitational search algorithm for multithreshold color image segmentation. IEEE Access, 7, 44903-44927.
Jovanovic, R., & Tuba, M. (2013). Ant colony optimization algorithm with pheromone correction strategy for the minimum connected dominating set problem. Computer Science and Information Systems, 10(1), 133-149.
Karaboga, D. (2005). An idea based on honey bee swarm for numerical optimization (Vol. 200, pp. 1-10). Technical report-tr06, Erciyes university, engineering faculty, computer engineering department.
Serkan, K. A. Y. A., & FIĞLALI, N. (2018). Çok amaçlı esnek atölye tipi çizelgeleme problemlerinin çözümünde meta sezgisel yöntemlerin kullanımı. Harran Üniversitesi Mühendislik Dergisi, 3(3), 222-233.
Kennedy, J., & Eberhart, R. (1995, November). Particle swarm optimization. In Proceedings of ICNN'95-international conference on neural networks (Vol. 4, pp. 1942-1948). IEEE.
Khurma, R. A., Aljarah, I., Sharieh, A., & Mirjalili, S. (2020). Evolopy-fs: An open-source nature-inspired optimization framework in python for feature selection. In Evolutionary Machine Learning Techniques (pp. 131-173). Springer, Singapore.
Kirkpatrick, S., Gelatt, C. D., & Vecchi, M. P. (1983). Optimization by simulated annealing. science, 220(4598), 671-680.
Komossa, S. (2015). Tidal disruption of stars by supermassive black holes: Status of observations. Journal of High Energy Astrophysics, 7, 148-157.
Laporte, G., Gendreau, M., Potvin, J. Y., & Semet, F. (2000). Classical and modern heuristics for the vehicle routing problem. International transactions in operational research, 7(4‐5), 285-300.
Li, X., Zhang, J., & Yin, M. (2014). Animal migration optimization: an optimization algorithm inspired by animal migration behavior. Neural Computing and Applications, 24(7), 1867-1877.
Luo, Q., Ma, M., & Zhou, Y. (2016). A novel animal migration algorithm for global numerical optimization. Computer Science and Information Systems, 13(1), 259-285.
Mafarja, M. M., & Mirjalili, S. (2017). Hybrid whale optimization algorithm with simulated annealing for feature selection. Neurocomputing, 260, 302-312.
Mirjalili, S., & Hashim, S. Z. M. (2010, December). A new hybrid PSOGSA algorithm for function optimization. In 2010 international conference on computer and information application (pp. 374-377). IEEE.
Mirjalili, S. (2015). Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm. Knowledge-based systems, 89, 228-249.
Mirjalili, S., Mirjalili, S. M., & Hatamlou, A. (2016). Multi-verse optimizer: a nature-inspired algorithm for global optimization. Neural Computing and Applications, 27(2), 495-513.
Mirjalili, S., Gandomi, A. H., Mirjalili, S. Z., Saremi, S., Faris, H., & Mirjalili, S. M. (2017). Salp Swarm Algorithm: A bio-inspired optimizer for engineering design problems. Advances in Engineering Software, 114, 163-191.
Murty, K. G. (2003). Optimization models for decision making: Volume. University of Michigan, Ann Arbor.
Pan, X., Xue, L., Lu, Y., & Sun, N. (2019). Hybrid particle swarm optimization with simulated annealing. Multimedia Tools and Applications, 78(21), 29921-29936.
Qaddoura, R., Faris, H., Aljarah, I., & Castillo, P. A. (2021). EvoCluster: An Open-Source Nature-Inspired Optimization Clustering Framework. SN Computer Science, 2(3), 1-12.
Rashedi, E., Nezamabadi-Pour, H., & Saryazdi, S. (2009). GSA: a gravitational search algorithm. Information sciences, 179(13), 2232-2248.
Sayed, G. I., & Hassanien, A. E. (2018). A hybrid SA-MFO algorithm for function optimization and engineering design problems. Complex & Intelligent Systems, 4(3), 195-212.
Sayed, G. I., Darwish, A., & Hassanien, A. E. (2019). Quantum multiverse optimization algorithm for optimization problems. Neural Computing and Applications, 31(7), 2763-2780.
Song, R., Zeng, X., & Han, R. (2020). An Improved Multi-Verse Optimizer Algorithm For Multi-Source Allocation Problem. International Journal of Innovative Computing, Information and Control, 16(6), 1845–1862.
Storn, R. (1996, June). On the usage of differential evolution for function optimization. In Proceedings of North American Fuzzy Information Processing (pp. 519-523). IEEE.
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.
Talbi, E. G. (2002). A taxonomy of hybrid metaheuristics. Journal of heuristics, 8(5), 541-564.
Talbi, E. G. (2009). Metaheuristics: from design to implementation (Vol. 74). John Wiley & Sons.
Ting, T. O., Yang, X. S., Cheng, S., & Huang, K. (2015). Hybrid metaheuristic algorithms: past, present, and future. Recent advances in swarm intelligence and evolutionary computation, 71-83.
Wang, C., Lin, M., Zhong, Y., & Zhang, H. (2016). Swarm simulated annealing algorithm with knowledge-based sampling for travelling salesman problem. International Journal of Intelligent Systems Technologies and Applications, 15(1), 74-94.
Wilcoxon, F. (1992). Individual comparisons by ranking methods. In Breakthroughs in statistics (pp. 196-202). Springer, New York, NY.
Yang, X. S. (2010). Nature-inspired metaheuristic algorithms. Luniver press.
Yang, X. S., & Deb, S. (2009, December). Cuckoo search via Lévy flights. In 2009 World congress on nature & biologically inspired computing (NaBIC) (pp. 210-214). IEEE.