Research Article
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Year 2022, , 113 - 124, 01.12.2022
https://doi.org/10.33187/jmsm.1115792

Abstract

References

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  • [19] M. Khishe, M. R. Mosavi,Classification of underwater acoustical dataset using neural network trained by Chimp Optimization Algorithm, Applied Acoustics, 157 (2020), doi.org/10.1016/j.apacoust.2019.107005.
  • [20] M. Khishe, A. Safari , Classification of sonar targets using an MLP neural network trained by dragonfly algorithm, Wireless Personal Communications, 108(4) (2019), doi.org/10.1007/s11277-019-06520-w.
  • [21] W. Qiao, M. Khishe, S. Ravakhah, Underwater targets classification using local wavelet acoustic pattern and Multi-Layer Perceptron neural network optimized by modified Whale Optimization Algorithm, Ocean Engineering, 219 (2021), doi.org/10.1016/j.oceaneng.2020.108415.
  • [22] J. Wang, M. Khishe, M. Kaveh, H. Mohammadi, Binary chimp optimization algorithm (BChOA): A new binary meta-heuristic for solving optimization problems, Cognitive Computation, 13(5) (2021), doi:10.1007/s12559-021-09933-7.
  • [23] M. Khishe, M. Nezhadshahbodaghi, M. R. Mosavi, D. Mart´ın, A weighted chimp optimization algorithm, IEEE Access, 9 (2021), doi: 10.1109/ACCESS. 2021.3130933.
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  • [25] A. Kumar, R. K. Misra, D. Singh, S. Mishra, S. Das, The spherical search algorithm for bound-constrained global optimization problems, Appl. Soft Comput., 85 (2019), doi.org/10.1016/j.asoc.2019.105734.
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  • [27] G. Dhiman, V. Kumar,Seagull optimization algorithm: Theory and its applications for large-scale industrial engineering problems, Knowledge-based systems, 165 (2019), doi.org/10.1016/j.knosys.2018.11.024.
  • [28] L. zhendong, ArtificialWater Drop Algorithm (AWDA),MATLAB Central File Exchange, (2022),https://www.mathworks.com/matlabcentral/fileexchange/104480-artificial-water-drop-algorithm-awda.
  • [29] H. P. Peraza-Va´zquez, A. F. Pen˜a-Delgado, G. E. Castillo, A. B. Morales-Cepeda, J. Velasco-A´ lvarez, F. Ruiz-Perez, A bio-inspired method for engineering design optimization inspired by dingoes hunting strategies, Math. Probl. Eng., 2021 (2021), Article ID 9107547 — doi.org/10.1155/2021/9107547.
  • [30] A. S. Heidari, S. Mirjalili, H. Faris, I. Aljarah, M. Mafarja, H. Chen, Harris hawks optimization: Algorithm and applications, Future generation computer systems, 97 (2019), Article ID 2218594 — doi.org/10.1155/2022/2218594.
  • [31] M. K. Naik, R. Panda, A. Abraham,Normalized square difference based multilevel thresholding technique for multispectral images using leader slime mould algorithm, Journal of King Saud University-Computer and Information Sciences, 34 (2022), doi.org/10.1016/j.jksuci.2020.10.030.
  • [32] M. K. Naik, R. Panda,A. Abraham,Adaptive opposition slime mould algorithm, Soft Computing, 25(22) (2021),doi.org/10.1007/s00500-021-06140-2.
  • [33] M. H. Qais, H. M. Hasanien, S. Alghuwainem, Augmented grey wolf optimizer for grid-connected PMSG-based wind energy conversion systems, Applied Soft Computing, 69 (2018), doi.org/10.1016/j.asoc.2018.05.006.
  • [34] S. Sharma, R. Kapoor, S. Dhiman, A Novel Hybrid Metaheuristic Based on Augmented Grey Wolf Optimizer and Cuckoo Search for Global Optimization, 2021 2nd International Conference on Secure Cyber Computing and Communications (ICSCCC), (2021), 376-381.
  • [35] A. Mohammadi-Balani, M. D.Nayeri, A. Azar, M. Taghizadeh-Yazdi, Golden eagle optimizer: A nature-inspired metaheuristic algorithm, Computers, Industrial Engineering, 152 (2021), doi.org/10.1016/j.cie.2020.107050.
  • [36] A. A. Heidari, S. Mirjalili, H. Faris, I. Aljarah, M. Mafarja, H. Chen, Harris hawks optimization: Algorithm and applications, Future generation computer systems, 97 (2019), doi.org/10.1016/j.future.2019.02.028.
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  • [40] S. Mirjalilia, S. M. Mirjalili, A. Lewisa, Grey Wolf Optimizer, Advances in Engineering Software, 69 (2014),doi.org/10.1016/j.advengsoft.2013.12.007, 46-61.
  • [41] E. Rashedi, H. Nezamabadi-pour, S. Saryazdi, GSA: A Gravitational Search Algorithm, Information Sciences, 179(13) (2009), doi.org/10.1016/j.ins.2009.03.004,2232-2248

Comparison of Recent Meta-Heuristic Optimization Algorithms Using Different Benchmark Functions

Year 2022, , 113 - 124, 01.12.2022
https://doi.org/10.33187/jmsm.1115792

Abstract

Meta-heuristic optimization algorithms are used in many application areas to solve optimization problems. In recent years, meta-heuristic optimization algorithms have gained importance over deterministic search algorithms in solving optimization problems. However, none of the techniques are equally effective in solving all optimization problems. Therefore, researchers have focused on either improving current meta-heuristic optimization techniques or developing new ones. Many alternative meta-heuristic algorithms inspired by nature have been developed to solve complex optimization problems. It is important to compare the performances of the developed algorithms through statistical analysis and determine the better algorithm. This paper compares the performances of sixteen meta-heuristic optimization algorithms (AWDA, MAO, TSA, TSO, ESMA, DOA, LHHO, DSSA, LSMA, AOSMA, AGWOCS, CDDO, GEO, BES, LFD, HHO) presented in the literature between 2021 and 2022. In this context, various test functions, including single-mode, multi-mode, and fixed-size multi-mode benchmark functions, were used to evaluate the efficiency of the algorithms used.

References

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  • [2] S. Kaura, L. K. Awasthia, G. Dhiman, Tunicate Swarm Algorithm: A new bio-inspired based metaheuristic paradigm for global optimization, Engineering Applications of Artificial Intelligence, 90 (2020),103-541.
  • [3] Y. V. Rey, J. L. Vel´azquez-Rodr´ıguez, M. D. Alanis-Tamez, M. Moreno-Ibarra, C. Y´a˜nez-M´arquez, Mexican axolotl optimization: a novel bioinspired heuristic, Mathematics, 9(7) (2021), https://doi.org/10.3390/math9070781.
  • [4] M. K. Naik, R. Panda, A. Abraham, Normalized square difference based multilevel thresholding technique for multispectral images using leader slime mould algorithm, Journal of King Saud University-Computer and Information Sciences, 2020, https://doi.org/10.1016/j.jksuci.2020.10.030.
  • [5] M. K. Naik, R. Panda, A.Wunnava, B. Jena, A. Abraham , A leader Harris hawks optimization for 2-D Masi entropy-based multilevel image thresholding, Multimedia Tools and Applications, 80(28) (2021), 35543-35583, https://doi.org/10.1007/s11042-020-10467-7
  • [6] E. H.Houssein, M. R. Saad, F. A. Hashim, H. Shaban, M. Hassaballah, L´evy flight distribution: A new metaheuristic algorithm for solving engineering optimization problems, Engineering Applications of Artificial Intelligence, 94 (2020), doi:10.1016/j.engappai.2020.103731.
  • [7] A. Mohammadi-Balani, M. D. Nayeri, A. Azar, M. Taghizadeh-Yazdi, Golden eagle optimizer: A nature-inspired metaheuristic algorithm, Computers & Industrial Engineering, 152 (2021), doi.org/10.1016/j.cie.2020.107050.
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  • [9] Y. Wang, T. Du, An improved squirrel search algorithm for global function optimization,Algorithms, 12 (2019), doi.org/10.3390/a12040080.
  • [10] Y. Wang, T. Du, A multi-objective improved squirrel search algorithm based on decomposition with external population and adaptive weight vectors adjustment, Physica A: Statistical Mechanics and its Applications, 547 (2020), doi.org/10.1016/j.physa.2019.123526.
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  • [12] S. Abdulhameed, T. A. Rashid, Child drawing development optimization algorithm based on child’s cognitive development, Arabian Journal for Science and Engineering, 47(2) (2022),doi.org/10.1007/s13369-021-05928-6.
  • [13] A. Ramadan, S. Kamel, M. H. Hassan, T. Khurshaid, C. Rahmann, An improved bald eagle search algorithm for parameter estimation of different photovoltaic models, Processes, 9(7) (2021), doi.org/10.3390/pr9071127.
  • [14] H. A. Alsattar, A. A. Zaidan, B. B. Zaidan , Novel meta-heuristic bald eagle search optimisation algorithm, Artificial Intelligence Review, 53(3) (2020),doi.org/10.1007/s10462-019-09732-5.
  • [15] M. K. Naik, R. Panda, A. Abraham, Adaptive opposition slime mould algorithm, Soft Computing, 25(22) (2021), doi.org/10.1007/s00500-021-06140-2, 14297–14313.
  • [16] S. Padhy, P. Sidhartha, Application of a simplified Grey Wolf optimization technique for adaptive fuzzy PID controller design for frequency regulation of a distributed power generation system, Protection and Control of Modern Power Systems, 6(1) (2021), doi.org/10.1186/s41601-021-00180-4.
  • [17] H. Bayzidi, S. Talatahari, M. Saraee, C. P. Lamarche,Social network search for solving engineering optimization problems, Computational Intelligence and Neuroscience, 2021 (2021), Article ID 8548639 — https://doi.org/10.1155/2021/8548639.
  • [18] B. Abdollahzadeha, F. S. Gharehchopogha, S. Mirjalili, African vultures optimization algorithm: A new nature-inspired metaheuristic algorithm for global optimization problems, Computers, Industrial Engineering, 158 (2021), doi.org/10.1016/j.cie.2021.107408.
  • [19] M. Khishe, M. R. Mosavi,Classification of underwater acoustical dataset using neural network trained by Chimp Optimization Algorithm, Applied Acoustics, 157 (2020), doi.org/10.1016/j.apacoust.2019.107005.
  • [20] M. Khishe, A. Safari , Classification of sonar targets using an MLP neural network trained by dragonfly algorithm, Wireless Personal Communications, 108(4) (2019), doi.org/10.1007/s11277-019-06520-w.
  • [21] W. Qiao, M. Khishe, S. Ravakhah, Underwater targets classification using local wavelet acoustic pattern and Multi-Layer Perceptron neural network optimized by modified Whale Optimization Algorithm, Ocean Engineering, 219 (2021), doi.org/10.1016/j.oceaneng.2020.108415.
  • [22] J. Wang, M. Khishe, M. Kaveh, H. Mohammadi, Binary chimp optimization algorithm (BChOA): A new binary meta-heuristic for solving optimization problems, Cognitive Computation, 13(5) (2021), doi:10.1007/s12559-021-09933-7.
  • [23] M. Khishe, M. Nezhadshahbodaghi, M. R. Mosavi, D. Mart´ın, A weighted chimp optimization algorithm, IEEE Access, 9 (2021), doi: 10.1109/ACCESS. 2021.3130933.
  • [24] W. Kaidi, M. Khishe, M. Mohammadi, Dynamic levy flight chimp optimization, Knowledge-Based Systems, 235 (2011), doi.org/10.1016/j.knosys.2021.107625.
  • [25] A. Kumar, R. K. Misra, D. Singh, S. Mishra, S. Das, The spherical search algorithm for bound-constrained global optimization problems, Appl. Soft Comput., 85 (2019), doi.org/10.1016/j.asoc.2019.105734.
  • [26] A. Faramarzi, M. Heidarinejad, B. Stephens, S. Mirjalili,Equilibrium optimizer: A novel optimization algorithm, Knowledge-Based Systems, 191 (2020), doi.org/10.1016/j.knosys.2019.105190.
  • [27] G. Dhiman, V. Kumar,Seagull optimization algorithm: Theory and its applications for large-scale industrial engineering problems, Knowledge-based systems, 165 (2019), doi.org/10.1016/j.knosys.2018.11.024.
  • [28] L. zhendong, ArtificialWater Drop Algorithm (AWDA),MATLAB Central File Exchange, (2022),https://www.mathworks.com/matlabcentral/fileexchange/104480-artificial-water-drop-algorithm-awda.
  • [29] H. P. Peraza-Va´zquez, A. F. Pen˜a-Delgado, G. E. Castillo, A. B. Morales-Cepeda, J. Velasco-A´ lvarez, F. Ruiz-Perez, A bio-inspired method for engineering design optimization inspired by dingoes hunting strategies, Math. Probl. Eng., 2021 (2021), Article ID 9107547 — doi.org/10.1155/2021/9107547.
  • [30] A. S. Heidari, S. Mirjalili, H. Faris, I. Aljarah, M. Mafarja, H. Chen, Harris hawks optimization: Algorithm and applications, Future generation computer systems, 97 (2019), Article ID 2218594 — doi.org/10.1155/2022/2218594.
  • [31] M. K. Naik, R. Panda, A. Abraham,Normalized square difference based multilevel thresholding technique for multispectral images using leader slime mould algorithm, Journal of King Saud University-Computer and Information Sciences, 34 (2022), doi.org/10.1016/j.jksuci.2020.10.030.
  • [32] M. K. Naik, R. Panda,A. Abraham,Adaptive opposition slime mould algorithm, Soft Computing, 25(22) (2021),doi.org/10.1007/s00500-021-06140-2.
  • [33] M. H. Qais, H. M. Hasanien, S. Alghuwainem, Augmented grey wolf optimizer for grid-connected PMSG-based wind energy conversion systems, Applied Soft Computing, 69 (2018), doi.org/10.1016/j.asoc.2018.05.006.
  • [34] S. Sharma, R. Kapoor, S. Dhiman, A Novel Hybrid Metaheuristic Based on Augmented Grey Wolf Optimizer and Cuckoo Search for Global Optimization, 2021 2nd International Conference on Secure Cyber Computing and Communications (ICSCCC), (2021), 376-381.
  • [35] A. Mohammadi-Balani, M. D.Nayeri, A. Azar, M. Taghizadeh-Yazdi, Golden eagle optimizer: A nature-inspired metaheuristic algorithm, Computers, Industrial Engineering, 152 (2021), doi.org/10.1016/j.cie.2020.107050.
  • [36] A. A. Heidari, S. Mirjalili, H. Faris, I. Aljarah, M. Mafarja, H. Chen, Harris hawks optimization: Algorithm and applications, Future generation computer systems, 97 (2019), doi.org/10.1016/j.future.2019.02.028.
  • [37] G. Dhiman, K. K. Singh, M. Soni, A. Nagar, M. Dehghani, A. Slowik, A. Kaur, A. Sharma, E. H. Houssein, K. Cengiz, MOSOA: A new multi-objective seagull optimization algorithm, Expert Systems with Applications, 167 (2021), doi.org/10.1016/j.eswa.2020.114150.
  • [38] A. Afroughinia, R. K. Moghaddam, Competitive learning: a new meta-heuristic optimization algorithm, International Journal on Artificial Intelligence Tools, 27(8 (2018), doi.org/10.1142/S0218213018500355.
  • [39] M. Khishe, M. R. Mosavi, Chimp optimization algorithm, Expert Systems with Applications, 149 (2020), doi.org/10.1016/j.eswa.2020.113338.
  • [40] S. Mirjalilia, S. M. Mirjalili, A. Lewisa, Grey Wolf Optimizer, Advances in Engineering Software, 69 (2014),doi.org/10.1016/j.advengsoft.2013.12.007, 46-61.
  • [41] E. Rashedi, H. Nezamabadi-pour, S. Saryazdi, GSA: A Gravitational Search Algorithm, Information Sciences, 179(13) (2009), doi.org/10.1016/j.ins.2009.03.004,2232-2248
There are 41 citations in total.

Details

Primary Language English
Subjects Mathematical Sciences
Journal Section Articles
Authors

Mahmut Dirik 0000-0003-1718-5075

Publication Date December 1, 2022
Submission Date May 12, 2022
Acceptance Date October 20, 2022
Published in Issue Year 2022

Cite

APA Dirik, M. (2022). Comparison of Recent Meta-Heuristic Optimization Algorithms Using Different Benchmark Functions. Journal of Mathematical Sciences and Modelling, 5(3), 113-124. https://doi.org/10.33187/jmsm.1115792
AMA Dirik M. Comparison of Recent Meta-Heuristic Optimization Algorithms Using Different Benchmark Functions. Journal of Mathematical Sciences and Modelling. December 2022;5(3):113-124. doi:10.33187/jmsm.1115792
Chicago Dirik, Mahmut. “Comparison of Recent Meta-Heuristic Optimization Algorithms Using Different Benchmark Functions”. Journal of Mathematical Sciences and Modelling 5, no. 3 (December 2022): 113-24. https://doi.org/10.33187/jmsm.1115792.
EndNote Dirik M (December 1, 2022) Comparison of Recent Meta-Heuristic Optimization Algorithms Using Different Benchmark Functions. Journal of Mathematical Sciences and Modelling 5 3 113–124.
IEEE M. Dirik, “Comparison of Recent Meta-Heuristic Optimization Algorithms Using Different Benchmark Functions”, Journal of Mathematical Sciences and Modelling, vol. 5, no. 3, pp. 113–124, 2022, doi: 10.33187/jmsm.1115792.
ISNAD Dirik, Mahmut. “Comparison of Recent Meta-Heuristic Optimization Algorithms Using Different Benchmark Functions”. Journal of Mathematical Sciences and Modelling 5/3 (December 2022), 113-124. https://doi.org/10.33187/jmsm.1115792.
JAMA Dirik M. Comparison of Recent Meta-Heuristic Optimization Algorithms Using Different Benchmark Functions. Journal of Mathematical Sciences and Modelling. 2022;5:113–124.
MLA Dirik, Mahmut. “Comparison of Recent Meta-Heuristic Optimization Algorithms Using Different Benchmark Functions”. Journal of Mathematical Sciences and Modelling, vol. 5, no. 3, 2022, pp. 113-24, doi:10.33187/jmsm.1115792.
Vancouver Dirik M. Comparison of Recent Meta-Heuristic Optimization Algorithms Using Different Benchmark Functions. Journal of Mathematical Sciences and Modelling. 2022;5(3):113-24.

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