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Assessment of GTO: Performance evaluation via constrained benchmark function, and Optimized of Three Bar Truss Design Problem

The aim of this paper is to show that the Artificial Gorilla Troops optimization (GTO) Algorithm, as an optimizer, can cope with test functions such as CEC2019, and also to best optimize the Three Bar Truss Design Problem as a constrained optimization problem. As a method, two statistical measures such as the best values provided by the algorithms and the standard deviation showing the distance between the values were studied. At the same time, the convergence veliocity of the algorithms compared by the convergence curves were examined. For this purpose, it has been compete against two other swarm-based algorithms, Sine-Cosine Algorithm (SCA) and Golden Eagle Optimization (GEO). The optimization of the Three Bar Truss Design Problem, which is another side of the study, has been made. The GTO algorithm reached the best values in the optimization of the parameters of the problem. In addition to the convergence curve, statistical results has examined and the advantages of GTO are revealed through box-plot figures that evaluate the relationship between median and quartiles and the distribution among all results.

Anahtar Kelimeler:

Metaheuristic, Artificial Gorilla Troops Algorithm, Truss Bar Design Problem

Assessment of GTO: Performance evaluation via constrained benchmark function, and Optimized of Three Bar Truss Design Problem

The aim of this paper is to show that the artificial gorilla troops optimization (GTO) algorithm, as an optimizer, can cope with test functions such as CEC2019, and also to best optimize the three bar truss design problem as a constrained optimization problem. As a method, two statistical measures such as the best values provided by the algorithms and the standard deviation showing the distance between the values were studied. At the same time, the convergence rate of the algorithms compared by the convergence curves were examined. For this purpose, it has been competed against two other swarm-based algorithms, sine-cosine algorithm (SCA) and golden eagle optimization (GEO). The optimization of the three bar truss design problem, which is another side of the study, has been made. The GTO algorithm reached the best values in the optimization of the parameters of the problem. In addition to the convergence curve, statistical results have examined, and the advantages of GTO are revealed through box-plot figures that evaluate the relationship between median and quartiles and the distribution among all results.

Keywords:

Metaheuristic, Artificial Gorilla Troops Algorithm, Truss Bar Design Problem,

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[1] Tejani, G. G., Pholdee, N., Bureerat, S., & Prayogo, D, Multiobjective adaptive symbiotic organisms search for truss optimization problems. Knowledge-based systems, vol.161, pp.398-414, 2018.
[2] Kumar, S., Jangir, P., Tejani, G. G., Premkumar, M., & Alhelou, H. H, MOPGO: A new physics-based multi-objective plasma generation optimizer for solving structural optimization problems. IEEE Access, vol.9, pp.84982-85016, 2021.
[3] Khalid, O. W., Isa, N. A. M., & Sakim, H. A. M., Emperor penguin optimizer: A comprehensive review based on state-of-the-art meta-heuristic algorithms. Alexandria Engineering Journal, 2022.
[4] Yücel, M., Bekdaş, G., & Nigdeli, S. M., Prediction of optimum 3-bar truss model parameters with an ANN model. In International Conference on Harmony Search Algorithm, pp. 317-324. April, 2020.
[5] Zitouni, F., Harous, S., & Maamri, R. (2020). The solar system algorithm: a novel metaheuristic method for global optimization. IEEE Access, vol.9, pp.4542-4565.
[6] Wang, Z., Luo, Q., & Zhou, Y., Hybrid metaheuristic algorithm using butterfly and flower pollination base on mutualism mechanism for global optimization problems. Engineering with Computers, vol.37, no.4, pp. 3665-3698, 2021.
[7] Abdollahzadeh, B., Soleimanian Gharehchopogh, F., & Mirjalili, S., Artificial gorilla troops optimizer: a new nature‐inspired metaheuristic algorithm for global optimization problems. International Journal of Intelligent Systems, vol.36, no.10, 5887-5958, 2021.
[8] Abd Elaziz, M., Abualigah, L., Issa, M., & Abd El-Latif, A. A., Optimal parameters extracting of fuel cell based on Gorilla Troops Optimizer. Fuel, 332, 2023.
[9] Abdel-Basset, M., El-Shahat, D., Sallam, K. M., & Munasinghe, K., Parameter extraction of photovoltaic models using a memory-based improved gorilla troops optimizer. Energy Conversion and Management, 252, 2022.
[10] Houssein, E. H., Saad, M. R., Ali, A. A., & Shaban, H., An efficient multi-objective gorilla troops optimizer for minimizing energy consumption of large-scale wireless sensor networks. Expert Systems with Applications, 212, 2023.
[11] Wu, T., Wu, D., Jia, H., Zhang, N., Almotairi, K. H., Liu, Q., & Abualigah, L. (2022). A Modified Gorilla Troops Optimizer for Global Optimization Problem. Applied Sciences, 12(19), 10144.
[12] El-Dabah, M. A., Hassan, M. H., Kamel, S., & Zawbaa, H. M. (2022). Robust Parameters Tuning of Different Power System Stabilizers Using a Quantum Artificial Gorilla Troops Optimizer. IEEE Access, 10, 82560-82579.
[13] Gong, J., Yang, X., Wang, H., Shen, J., Liu, W., & Zhou, F. (2022). Coordinated method fusing improved bubble entropy and artificial gorilla troops optimizer optimized KELM for rolling bearing fault diagnosis. Applied Acoustics, 195, 108844.
[14] Murugan, S., Jaishankar, M., & Premkumar, K., Hybrid DC–AC Microgrid Energy Management System Using an Artificial Gorilla Troops Optimizer Optimized Neural Network. Energies, 15(21), 2022.
[15] Mohammadi-Balani, A., Nayeri, M. D., Azar, A., & Taghizadeh-Yazdi, M., Golden eagle optimizer: A nature-inspired metaheuristic algorithm. Computers & Industrial Engineering, 152, 2021.
[16] Mirjalili, S., SCA: a sine cosine algorithm for solving optimization problems. Knowledge-based systems, 96, pp.120-133, 2016.
[17] 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.
[18] Abualigah, L., Diabat, A., Altalhi, M., & Elaziz, M. A. (2022). Improved gradual change-based Harris Hawks optimization for real-world engineering design problems. Engineering with Computers, 1-41.
[19] Magesh, T., Devi, G., & Lakshmanan, T., Improving the performance of grid connected wind generator with a PI control scheme based on the metaheuristic golden eagle optimization algorithm. Electric Power Systems Research, 214, 2023
[20] Gholizadeh, S., & Sojoudizadeh, R., Modified sine-cosine algorithm for sizing optimization of truss structures with discrete design variables. Iran University of Science & Technology, 9(2), pp.195-212, 2019.
[21] Naruei, I., & Keynia, F. (2022). Wild horse optimizer: A new meta-heuristic algorithm for solving engineering optimization problems. Engineering with computers, 38(4), 3025-3056.
[22] Ray, T., & Saini, P., Engineering design optimization using a swarm with an intelligent information sharing among individuals. Engineering Optimization, vol.33, no.6, pp.735-748, 2001.
[23] Price K V, Awad NH, Ali MZ, Suganthan PN., Problem defnitions and evaluation criteria for the 100 digit challenge special session and competition on single objective numerical optimization. In: Technical Report. Nanyang Technological University, 2018.
[24] A. Viktorin, R. Senkerik, M. Pluhacek, T. Kadavy and A. Zamuda, "DISH Algorithm Solving the CEC2019 100-Digit Challenge," 2019 IEEE Congress on Evolutionary Computation (CEC), pp. 1-6, 2019, doi: 10.1109/CEC.2019.8789936.