How to cite this paper
Erdemir, E. (2023). Hybrid algorithm proposal for optimizing benchmarking problems: Salp swarm algorithm enhanced by arithmetic optimization algorithm.International Journal of Industrial Engineering Computations , 14(2), 309-322.
Refrences
Akyol, S. (2021). Global optimizasyon için yeni bir hibrit yöntem: kaya kartalı optimizasyonu-tanjant arama algoritması. Fırat Üniversitesi Mühendislik Bilimleri Dergisi, 33(2), 721-733.
Alba, E., & Dorronsoro, B. (2005). The exploration/exploitation tradeoff in dynamic cellular genetic algorithms. IEEE Transactions on Evolutionary Computation, 9(2), 126-142.
Abualigah, L., Diabat, A., Mirjalili, S., Abd Elaziz, M., & Gandomi, A.H. (2021). The arithmetic optimization algorithm. Computer Methods in Applied Mechanics and Engineering, 376, 113609, pages 38.
Abualigah, L. (2022). The Arithmetic Optimization Algorithm (AOA). MATLAB Central File Exchange. https://www.mathworks.com/matlabcentral/fileexchange/84742-the-arithmetic-optimization-algorithm-aoa
Bairathi, D., & Gopalani, D. (2019). Salp swarm algorithm (SSA) for training feed-forward neural networks, in: Bansal, J., Das, K., Nagar, A., Deep, K. & Ojha, A. (Eds.), Soft Computing for Problem Solving. Advances in Intelligent Systems and Computing, Springer, Singapore, 816, pp. 521-534.
Baş, E. (2021). Hybrid the arithmetic optimization algorithm for constrained optimization problems. Konya Mühendislik Bilimleri Dergisi, 9(3), 713-734.
Britannica, T. Editors of Encyclopaedia (2022). Algorithm. Encyclopedia Britannica. https://www.britannica.com/science/algorithm
Brownlee, J.A. (2021). Gentle introduction to stochastic optimization algorithms. Machine Learning Mastery. https://machinelearningmastery.com/stochastic-optimization-for-machine-learning/.
Črepinšek, M., Liu, S.-H., & Mernik, M. (2013). Exploration and exploitation in evolutionary algorithms: A survey. ACM Comput. Surv, 45(3), Article 35, 33 pages.
Dede, T., Grzywiński, M., & Rao, R.V. (2020). Jaya: A new meta-heuristic algorithm for the optimization of braced dome structures, in: Rao, R.V., & Taler, J. (Eds.), Advanced Engineering Optimization Through Intelligent Techniques. Advances in Intelligent Systems and Computing, Springer, Singapure, 949, pp. 13-20.
Derrac, J., García, S., Molina, D., & Herrera, F. (2011). A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm and Evolutionary Computation, 1(1), 3-18.
Faris, H., Qaddoura, R., Aljarah, I., Bae, J.W., Fouad, M.M., Floden, L., Wolz, D., Bawagan, P., & Merelo-Guervós, J.J. (2016a). EvoloPy. Github. https://github.com/7ossam81/EvoloPy
Faris, H., Aljarah, I., Mirjalili, S., Castillo, P.A., & Merelo, J.J. (2016b). Evolopy: an open-source nature-inspired optimization framework in python. in: Proceedings Of The 8th International Joint Conference On Computational Intelligence-ECTA (IJCCI 2016). SciTePress, Porto, 1, pp. 171-177.
Faris H., Mirjalili S., Aljarah I., Mafarja M., & Heidari A.A. (2020). Salp swarm algorithm: theory, literature review, and application in extreme learning machines. in: Mirjalili, S., Song Dong, J., & Lewis, A. (Eds.), Studies in Computational Intelligence. Nature-Inspired Optimizers. Springer, Cham, 811, pp. 185-199.
Fletcher, R., & Powell, M.J.D. (1963). A rapidly convergent descent method for minimization. The Computer Journal, 6(2), 163–168.
Friedrich, T., Kötzing, T., Krejca, M.S., & Rajabi, A. (2022). Escaping local optima with local search: a theory-driven discussion, in: International Conference on Parallel Problem Solving from Nature. Springer, Cham, pp. 442-455.
Gavana, A. (2013). Global Optimization Benchmarks and AMPGO. Test Functions Index. http://infinity77.net/global_optimization/test_functions.html
Holland, J.H. (1992). Genetic algorithms. Scientific American, 267(1), 66-72.
Hussain, K., Salleh, M.N.M., Cheng, S., & Naseem, R. (2017). Common benchmark functions for metaheuristic evaluation: a review. JOIV: International Journal on Informatics Visualization, 1(4-2), 218-223.
Jamil, M., & Yang, X.-S. (2013). A literature survey of benchmark functions for global optimization problems. International Journal of Mathematical Modelling and Numerical Optimisation (IJMMNO), 4(2), 150-194.
Kennedy, J. & Eberhart, R. (1995). Particle swarm optimization, in: Proceedings of ICNN'95 - International Conference on Neural Networks. Perth, WA, Australia, 4, pp. 1942-1948.
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: Mirjalili, S., Faris, H. & Aljarah, I. (Eds.), Evolutionary Machine Learning Techniques. Algorithms for Intelligent Systems. Springer, Singapore, pp. 131-173.
Kochenderfer, M.J., & Wheeler, T.A. (2019). Algorithms for optimization. Mit Press.
Mirjalili, S., Mirjalili, S.M., & Lewis, A. (2014). Grey wolf optimizer. Advances in Engineering Software, 69, 46-61.
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.
Mirjalili, S. (2018). SSA: Salp swarm algorithm. MATLAB Central File Exchange. https://www.mathworks.com/matlabcentral/fileexchange/63745-ssa-salp-swarm-algorithm.
Naik, M.K., Samantaray, L., & Panda, R. (2016). A hybrid CS–GSA algorithm for optimization, in: Bhattacharyya, S., Dutta, P., & Chakraborty, S. (Eds.), Hybrid Soft Computing Approaches. Studies in Computational Intelligence. Springer, New Delhi, 611, pp. 3-35.
Qaddoura, R., Faris, H., Aljarah, I., & Castillo, P.A. (2020). EvoCluster: an open-source nature-inspired optimization clustering framework in python. in: International Conference on the Applications of Evolutionary Computation (Part of EvoStar). Spain, Seville, pp. 20-36.
Ramesh, J., & Manavalan, R. (2021). Hybrid of salp swarm optimization algorithm and grasshopper optimization algorithm (SSOAGOA) for feature selection. International Journal of Grid and Distributed Computing, 14(1), 1350–1366.
Rao, R.V. (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.
Sergeyev, Y.D., Kvasov, D.E., & Mukhametzhanov, M.S. (2017). Operational zones for comparing metaheuristic and deterministic one-dimensional global optimization algorithms. Mathematics and Computers in Simulation, 141, 96-109.
Sharma, V., & Tripathi, A.K. (2022). A systematic review of meta-heuristic algorithms in IoT based application. Array, 14, 100164, pages 8.
Şenel, F.A., Gökçe, F., Yüksel, A.S., & Yiğit, T. (2019). A novel hybrid PSO–GWO algorithm for optimization problems. Engineering with Computers, 35, 1359–1373.
Talbi, E.G. (2002). A taxonomy of hybrid metaheuristics. Journal of Heuristics, 8(5), 541-564.
Tarik, B.M., Zerhouni, F.Z., Stambouli, A.B., Tioursi, M., & M'harer, A. (2016). Parameter optimization of photovoltaic solar cell and panel using genetic algorithms strategy, in: Vasant, P., Weber, G., & Dieu, V. (Eds.), Handbook of Research on Modern Optimization Algorithms and Applications in Engineering and Economics. IGI Global, pp. 581-600.
Ting, T.O., Yang, X.-S., Cheng, S., & Huang, K. (2015). Hybrid metaheuristic algorithms: past, present, and future, in: Yang, X.-S. (Ed.), Recent Advances in Swarm Intelligence and Evolutionary Computation. Studies in Computational Intelligence. Springer, Cham, 585, 71-83.
Wang, D., Zhou, Y., Jiang, S., & Liu, X. (2018). A simplex method-based salp swarm algorithm for numerical and engineering optimization, in: Shi, Z., Mercier-Laurent, E. & Li, J. (Eds.), Intelligent Information Processing IX. IIP 2018. IFIP Advances in Information and Communication Technology. Springer, Cham, 538, pp.150-159.
Wang, S., Jia, H., Abualigah, L., Liu, Q., & Zheng, R. (2021). An improved hybrid aquila optimizer and harris hawks algorithm for solving industrial engineering optimization problems. Processes, 9(9), article 1551, pages 28.
Wolpert D.H., & Macready, W.G. (1997). No free lunch theorems for optimization. IEEE Transactions on Evolutionary Computation, 1(1), 67-82.
Wright, S. J. (2022). Optimization. Encyclopedia Britannica. https://www.britannica.com/science/optimization
Yang, X.-S., & Deb, S. (2009). Cuckoo search via lévy flights, in: 2009 World Congress on Nature & Biologically Inspired Computing (NaBIC). Coimbatore, India, pp. 210-214.
Yang, X.-S. (2010a). Engineering optimization, in: Engineering Optimization: An Introduction with Metaheuristic Applications. Wiley, Hoboken, NJ, pp. 15-28
Yang, X.-S. (2010b). Firefly algorithm, stochastic test functions and design optimisation. International Journal of Bio-Inspired Computation, 2 (2), 78-84.
Yang, X., & Deb, S. (2015). Cuckoo search for optimization and computational intelligence, in: Khosrow-Pour, M. (Ed.), Encyclopedia of Information Science and Technology Third Edition. IGI Global, pp. 133-142.
Yılmaz, Ö., Altun, A.A., & Köklü, M. (2022a). A new hybrid algorithm based on MVO and SA for function optimization. International Journal of Industrial Engineering Computations, 13(2), 237-254.
Yılmaz, Ö., Altun, A.A., & Köklü, M. (2022b). Optimizing the learning process of multi-layer perceptrons using a hybrid algorithm based on MVO and SA. International Journal of Industrial Engineering Computations, 13(4), 617-640.
Zhang, Q., Chen, H., Heidari, A.A., Zhao, X., Xu, Y., Wang, P., Li, Y., & Li, C. (2019). Chaos-induced and mutation-driven schemes boosting salp chains-inspired optimizers. IEEE Access, 7, 31243-31261.
Alba, E., & Dorronsoro, B. (2005). The exploration/exploitation tradeoff in dynamic cellular genetic algorithms. IEEE Transactions on Evolutionary Computation, 9(2), 126-142.
Abualigah, L., Diabat, A., Mirjalili, S., Abd Elaziz, M., & Gandomi, A.H. (2021). The arithmetic optimization algorithm. Computer Methods in Applied Mechanics and Engineering, 376, 113609, pages 38.
Abualigah, L. (2022). The Arithmetic Optimization Algorithm (AOA). MATLAB Central File Exchange. https://www.mathworks.com/matlabcentral/fileexchange/84742-the-arithmetic-optimization-algorithm-aoa
Bairathi, D., & Gopalani, D. (2019). Salp swarm algorithm (SSA) for training feed-forward neural networks, in: Bansal, J., Das, K., Nagar, A., Deep, K. & Ojha, A. (Eds.), Soft Computing for Problem Solving. Advances in Intelligent Systems and Computing, Springer, Singapore, 816, pp. 521-534.
Baş, E. (2021). Hybrid the arithmetic optimization algorithm for constrained optimization problems. Konya Mühendislik Bilimleri Dergisi, 9(3), 713-734.
Britannica, T. Editors of Encyclopaedia (2022). Algorithm. Encyclopedia Britannica. https://www.britannica.com/science/algorithm
Brownlee, J.A. (2021). Gentle introduction to stochastic optimization algorithms. Machine Learning Mastery. https://machinelearningmastery.com/stochastic-optimization-for-machine-learning/.
Črepinšek, M., Liu, S.-H., & Mernik, M. (2013). Exploration and exploitation in evolutionary algorithms: A survey. ACM Comput. Surv, 45(3), Article 35, 33 pages.
Dede, T., Grzywiński, M., & Rao, R.V. (2020). Jaya: A new meta-heuristic algorithm for the optimization of braced dome structures, in: Rao, R.V., & Taler, J. (Eds.), Advanced Engineering Optimization Through Intelligent Techniques. Advances in Intelligent Systems and Computing, Springer, Singapure, 949, pp. 13-20.
Derrac, J., García, S., Molina, D., & Herrera, F. (2011). A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm and Evolutionary Computation, 1(1), 3-18.
Faris, H., Qaddoura, R., Aljarah, I., Bae, J.W., Fouad, M.M., Floden, L., Wolz, D., Bawagan, P., & Merelo-Guervós, J.J. (2016a). EvoloPy. Github. https://github.com/7ossam81/EvoloPy
Faris, H., Aljarah, I., Mirjalili, S., Castillo, P.A., & Merelo, J.J. (2016b). Evolopy: an open-source nature-inspired optimization framework in python. in: Proceedings Of The 8th International Joint Conference On Computational Intelligence-ECTA (IJCCI 2016). SciTePress, Porto, 1, pp. 171-177.
Faris H., Mirjalili S., Aljarah I., Mafarja M., & Heidari A.A. (2020). Salp swarm algorithm: theory, literature review, and application in extreme learning machines. in: Mirjalili, S., Song Dong, J., & Lewis, A. (Eds.), Studies in Computational Intelligence. Nature-Inspired Optimizers. Springer, Cham, 811, pp. 185-199.
Fletcher, R., & Powell, M.J.D. (1963). A rapidly convergent descent method for minimization. The Computer Journal, 6(2), 163–168.
Friedrich, T., Kötzing, T., Krejca, M.S., & Rajabi, A. (2022). Escaping local optima with local search: a theory-driven discussion, in: International Conference on Parallel Problem Solving from Nature. Springer, Cham, pp. 442-455.
Gavana, A. (2013). Global Optimization Benchmarks and AMPGO. Test Functions Index. http://infinity77.net/global_optimization/test_functions.html
Holland, J.H. (1992). Genetic algorithms. Scientific American, 267(1), 66-72.
Hussain, K., Salleh, M.N.M., Cheng, S., & Naseem, R. (2017). Common benchmark functions for metaheuristic evaluation: a review. JOIV: International Journal on Informatics Visualization, 1(4-2), 218-223.
Jamil, M., & Yang, X.-S. (2013). A literature survey of benchmark functions for global optimization problems. International Journal of Mathematical Modelling and Numerical Optimisation (IJMMNO), 4(2), 150-194.
Kennedy, J. & Eberhart, R. (1995). Particle swarm optimization, in: Proceedings of ICNN'95 - International Conference on Neural Networks. Perth, WA, Australia, 4, pp. 1942-1948.
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: Mirjalili, S., Faris, H. & Aljarah, I. (Eds.), Evolutionary Machine Learning Techniques. Algorithms for Intelligent Systems. Springer, Singapore, pp. 131-173.
Kochenderfer, M.J., & Wheeler, T.A. (2019). Algorithms for optimization. Mit Press.
Mirjalili, S., Mirjalili, S.M., & Lewis, A. (2014). Grey wolf optimizer. Advances in Engineering Software, 69, 46-61.
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.
Mirjalili, S. (2018). SSA: Salp swarm algorithm. MATLAB Central File Exchange. https://www.mathworks.com/matlabcentral/fileexchange/63745-ssa-salp-swarm-algorithm.
Naik, M.K., Samantaray, L., & Panda, R. (2016). A hybrid CS–GSA algorithm for optimization, in: Bhattacharyya, S., Dutta, P., & Chakraborty, S. (Eds.), Hybrid Soft Computing Approaches. Studies in Computational Intelligence. Springer, New Delhi, 611, pp. 3-35.
Qaddoura, R., Faris, H., Aljarah, I., & Castillo, P.A. (2020). EvoCluster: an open-source nature-inspired optimization clustering framework in python. in: International Conference on the Applications of Evolutionary Computation (Part of EvoStar). Spain, Seville, pp. 20-36.
Ramesh, J., & Manavalan, R. (2021). Hybrid of salp swarm optimization algorithm and grasshopper optimization algorithm (SSOAGOA) for feature selection. International Journal of Grid and Distributed Computing, 14(1), 1350–1366.
Rao, R.V. (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.
Sergeyev, Y.D., Kvasov, D.E., & Mukhametzhanov, M.S. (2017). Operational zones for comparing metaheuristic and deterministic one-dimensional global optimization algorithms. Mathematics and Computers in Simulation, 141, 96-109.
Sharma, V., & Tripathi, A.K. (2022). A systematic review of meta-heuristic algorithms in IoT based application. Array, 14, 100164, pages 8.
Şenel, F.A., Gökçe, F., Yüksel, A.S., & Yiğit, T. (2019). A novel hybrid PSO–GWO algorithm for optimization problems. Engineering with Computers, 35, 1359–1373.
Talbi, E.G. (2002). A taxonomy of hybrid metaheuristics. Journal of Heuristics, 8(5), 541-564.
Tarik, B.M., Zerhouni, F.Z., Stambouli, A.B., Tioursi, M., & M'harer, A. (2016). Parameter optimization of photovoltaic solar cell and panel using genetic algorithms strategy, in: Vasant, P., Weber, G., & Dieu, V. (Eds.), Handbook of Research on Modern Optimization Algorithms and Applications in Engineering and Economics. IGI Global, pp. 581-600.
Ting, T.O., Yang, X.-S., Cheng, S., & Huang, K. (2015). Hybrid metaheuristic algorithms: past, present, and future, in: Yang, X.-S. (Ed.), Recent Advances in Swarm Intelligence and Evolutionary Computation. Studies in Computational Intelligence. Springer, Cham, 585, 71-83.
Wang, D., Zhou, Y., Jiang, S., & Liu, X. (2018). A simplex method-based salp swarm algorithm for numerical and engineering optimization, in: Shi, Z., Mercier-Laurent, E. & Li, J. (Eds.), Intelligent Information Processing IX. IIP 2018. IFIP Advances in Information and Communication Technology. Springer, Cham, 538, pp.150-159.
Wang, S., Jia, H., Abualigah, L., Liu, Q., & Zheng, R. (2021). An improved hybrid aquila optimizer and harris hawks algorithm for solving industrial engineering optimization problems. Processes, 9(9), article 1551, pages 28.
Wolpert D.H., & Macready, W.G. (1997). No free lunch theorems for optimization. IEEE Transactions on Evolutionary Computation, 1(1), 67-82.
Wright, S. J. (2022). Optimization. Encyclopedia Britannica. https://www.britannica.com/science/optimization
Yang, X.-S., & Deb, S. (2009). Cuckoo search via lévy flights, in: 2009 World Congress on Nature & Biologically Inspired Computing (NaBIC). Coimbatore, India, pp. 210-214.
Yang, X.-S. (2010a). Engineering optimization, in: Engineering Optimization: An Introduction with Metaheuristic Applications. Wiley, Hoboken, NJ, pp. 15-28
Yang, X.-S. (2010b). Firefly algorithm, stochastic test functions and design optimisation. International Journal of Bio-Inspired Computation, 2 (2), 78-84.
Yang, X., & Deb, S. (2015). Cuckoo search for optimization and computational intelligence, in: Khosrow-Pour, M. (Ed.), Encyclopedia of Information Science and Technology Third Edition. IGI Global, pp. 133-142.
Yılmaz, Ö., Altun, A.A., & Köklü, M. (2022a). A new hybrid algorithm based on MVO and SA for function optimization. International Journal of Industrial Engineering Computations, 13(2), 237-254.
Yılmaz, Ö., Altun, A.A., & Köklü, M. (2022b). Optimizing the learning process of multi-layer perceptrons using a hybrid algorithm based on MVO and SA. International Journal of Industrial Engineering Computations, 13(4), 617-640.
Zhang, Q., Chen, H., Heidari, A.A., Zhao, X., Xu, Y., Wang, P., Li, Y., & Li, C. (2019). Chaos-induced and mutation-driven schemes boosting salp chains-inspired optimizers. IEEE Access, 7, 31243-31261.