Sibel ARSLAN

Güncel Metasezgisel Algoritmalarının Performansları Üzerine Karşılaştırılmalı Bir Çalışma

Günümüzde, metasezgiseller optimizasyon problemlerinin çözümünde çok önemli bir rol oynamaktadır. Bu çalışmada sürü zekasından ve doğadaki canlıların yiyecek arama davranışlarından esinlenerek geliştirilen üç yeni metasezgisel (Afrika Akbabaları Optimizasyon Algoritması (African Vulture Optimization Algorithm, AVOA), Geliştirilmiş Gri Kurt Optimizasyon Algoritması (Improved Gray Wolf Optimization Algorithm, I-GWO) ve Deniz Avcıları Algoritması (Marine Predators Algorithm, MPA)), kıyaslamalarda en çok kullanılan metasezgisellerden biri olan Parçacık Sürü Optimizasyon Algoritması (Particle Swarm Optimization, PSO) ile kıyaslanmıştır. Deneysel çalışmalara göre, AVOA ve MPA’nın diğer algoritmalara göre daha başarılı sonuçlara sahip olduğu görülmektedir. Sonuçların istatiksel anlamlılığı, Friedman ve Wilcoxon işaretli sıralar testleri ile değerlendirilerek bu iki algoritmanın üstünlüğü kanıtlanmıştır.

Anahtar Kelimeler:

Afrika Akbabaları Optimizasyon Algoritması, Geliştirilmiş Gri Kurt Optimizasyon Algoritması, Deniz Avcıları Algoritması, Parçacık Sürü Optimizasyon Algoritması

Comparison of Current Metaheuristic Algorithms with Different Performance Criteria

Nowadays, metaheuristics play a very important role in solving optimization problems. In this study, Particle Swarm Optimization Algorithm (PSO), one of the most commonly used metaheuristics, was compared in three new metaheuristic (African Vulture Optimization Algorithm-AVOA, Improved Gray Wolf Optimization Algorithm- I-GWO and Marine Predators Algorithm-MPA) comparisons inspired by swarm intelligence and foraging behavior of creatures in nature. According to the experimental studies, AVOA and MPA achieve more successful results than other algorithms. The statistical significance of the results was evaluated using the Friedman Wilcoxon signed-rank test, and the significant superiority of these two algorithms was proven.

Keywords:

African Vulture Optimization Algorithm, Improved Gray Wolf Optimization Algorithm, Marine Predators Algorithm, Particle Swarm Optimization,

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[1] T. Dokeroglu, E. Sevinc, T. Kucukyilmaz, and A. Cosar, “A survey on new generation metaheuristic algorithms,” Computers & Industrial Engineering, vol. 137, no. 5, 2019.
[2] F. S. Gharehchopogh, H. Shayanfar, and H. Gholizadeh, “A comprehensive survey on symbiotic organisms search algorithms,” Artificial Intelligence Review, vol. 53, no. 56, pp. 1–48, 2020.
[3] K. Hussain, M. N. M. Salleh, S. Cheng, and Y. Shi, “Metaheuristic research: a comprehensive survey,” Artificial Intelligence Review, vol. 52, no. 4, pp. 2191–2233, 2019.
[4] V. Stojanovic, S. He, and B. Zhang, “State and parameter joint estimation of linear stochastic systems in presence of faults and non-Gaussian noises,” International Journal of Robust and Nonlinear Control, vol. 30, no. 16, pp. 6683–6700, 2020.
[5] B. Abdollahzadeh, and F. S. Gharehchopogh, “A multi-objective optimization algorithm for feature selection problems,” Engineering with Computers, pp. 1–19, 2021.
[6] F. S. Gharehchopogh, I. Maleki, and Z. A. Dizaji, “Chaotic vortex search algorithm: Metaheuristic algorithm for feature selection,” Evolutionary Intelligence, pp. 1–32, 2021.
[7] J. Kennedy and R. Eberhart, “Particle swarm optimization,” Proceedings of ICNN’95 - International Conference on Neural Networks, 1995, pp. 1942–1948.
[8] C. W. Cleghorn, and B. Stapelberg, “Particle swarm optimization: stability analysis using n-informers under arbitrary coefficient distributions,” Swarm and Evolutionary Computation, vol. 71, 2022.
[9] P. Hu, J.S. Pan, S. C. Chu, and C. Sun, “Multi-surrogate assisted binary particle swarm optimization algorithm and its application for feature selection,” Applied Soft Computing, vol. 121, 2022.
[10] X. Chen, and K. Li, “Collective information-based particle swarm optimization for multi-fuel CHP economic dispatch problem,” Knowledge-Based Systems, vol. 248, 2022.
[11] P. B. Fernandes, R. C. L. Oliveira, and J. F. Neto, “Trajectory planning of autonomous mobile robots applying a particle swarm optimization algorithm with peaks of diversity,” Applied Soft Computing, vol. 116, 2022.
[12] F. Wang, X. Wang, and S. Sun, “A reinforcement learning level-based particle swarm optimization algorithm for large-scale optimization,” Information Sciences, vol. 602, pp. 298-312, 2022.
[13] B. Abdollahzadeh, F. S. Gharehchopogh, and S. Mirjalili, “African vultures optimization algorithm: A new nature-inspired metaheuristic algorithm for global optimization problems,” Computers & Industrial Engineering, vol. 158, 2021.
[14] H. A. Bagal, Y. N. Soltanabad, M. Dadjuo, K. Wakil, M. Zare, and A. S. Mohammed, “SOFC model parameter identification by means of Modified African Vulture Optimization algorithm,” Energy Reports, vol. 7, pp. 7251-7260, 2021.
[15] Y. Wang, S. Li, H. Sun, C. Huang, and N. Youssefi, “The utilization of adaptive African vulture optimizer for optimal parameter identification of SOFC,” Energy Reports, vol. 8, pp. 551-560, 2022.
[16] Y. Chen, and G. Zhang, “New parameters identification of Proton exchange membrane fuel cell stacks based on an improved version of African vulture optimization algorithm,” Energy Reports, vol. 8, pp. 3030-3040, 2022.
[17] M. Alanazi, A. Fathy, D. Yousri, and H. Rezk, “Optimal reconfiguration of shaded PV based system using African vultures optimization approach,” Alexandria Engineering Journal, vol. 61, no. 12, pp. 12159-12185, 2022.
[18] Y. Wang, J. Wang, L. Yang, B. Ma, G. Sun, and N. Youssefi, “Optimal designing of a hybrid renewable energy system connected to an unreliable grid based on enhanced African vulture optimizer,” ISA Transactions, 2022.
[19] S. Mirjalili, S. M. Mirjalili, and A. Lewis, “Grey wolf optimizer,” Advances in Engineering Software, vol. 69, pp. 46-61, 2014.
[20] M. H. Nadimi-Shahraki, S. Taghian, and S. Mirjalili, “An improved grey wolf optimizer for solving engineering problems,” Expert Systems with Applications, vol. 166, 2021.
[21] D. Hua, X. Liu, S. Sun, Z. Li, Z. Li and W. Li, “Precise locomotion controller design for a novel magnetorheological fluid robot based on improved gray wolf optimization algorithm,” Smart Materials and Structures, vol. 30, no. 2, 2021.
[22] A. Faramarzi, M. Heidarinejad, S. Mirjalili, and A. H. Gandomi, “Marine Predators Algorithm: A nature-inspired metaheuristic,” Expert Systems with Applications, vol. 152, 2020.
[23] M. Abd Elaziz, D. Mohammadi, D. Oliva, and K. Salimifard, “Quantum marine predators algorithm for addressing multilevel image segmentation,” Applied Soft Computing, vol. 110, 2021.
[24] Z. Xing, and Y. He, “Many-objective multilevel thresholding image segmentation for infrared images of power equipment with boost marine predators algorithm,” Applied Soft Computing, vol. 113, 2021. [25] A. S. Sadiq, A. A. Dehkordi, S. Mirjalili, and Q. V. Pham, “Nonlinear marine predator algorithm: A cost-effective optimizer for fair power allocation in NOMA-VLC-B5G networks,” Expert Systems with Applications, vol. 203, 2022.
[26] M. H. Hassan, D. Yousri, S. Kamel, and C. Rahmann, “A modified marine predators algorithm for solving single-and multi-objective combined economic emission dispatch problems,” Computers & Industrial Engineering, vol. 164, 2022.
[27] E. H. Houssein, I. E. Ibrahim, M. Kharrich, and S. Kamel, “An improved marine predators algorithm for the optimal design of hybrid renewable energy systems,” Engineering Applications of Artificial Intelligence, vol. 110, 2022.
[28] X. Yao, Y. Liu, and G. Lin, “Evolutionary programming made faster,” IEEE Transactions on Evolutionary Computation, vol. 3, no. 2, pp. 82-102, 1999.
[29] S. Saremi, S. Mirjalili, and A. Lewis, “Grasshopper optimisation algorithm: theory and application,” Advances in Engineering Software, vol. 105, pp. 30-47, 2017.
[30] F. MiarNaeimi, G. Azizyan, and M. Rashki, “Horse herd optimization algorithm: a nature-inspired algorithm for high-dimensional optimization problems,” Knowledge-Based Systems, vol. 213, 2021.