Performance Evaluation of Capuchin Search Algorithm Through Non-linear Problems, and Optimization of Gear Train Design Problem

Yıl 2023, , 142 - 149, 31.12.2023

Erdal Eker

https://doi.org/10.36222/ejt.1391524

Öz

The purpose of this paper is to demonstrate the superiority of the Capuchin Search Algorithm (CapSA), a metaheuristic, in competitive environments and its advantages in optimizing engineering design problems. To achieve this, the CEC 2019 function set was used. Due to the challenging characteristics of the CEC 2019 function set in reaching a global solution, it effectively showcases the algorithm's quality. For this comparison, sea-horse optimizer (SHO), grey wolf optimizer (GWO), sine-cosine algorithm (SCA), and smell agent optimization (SAO) were chosen as current and effective alternatives to the CapSA algorithm. Furthermore, the gear train design problem (GTD) was selected as an engineering design problem. In addition to the CapSA algorithm, a hybrid of SCA and GWO algorithm (SC-GWO) and genetic algorithm (GA) were chosen as alternatives for optimizing this problem. The performance superiority and optimization power of the CapSA algorithm were assessed using statistical metrics and convergence curves, then compared with alternative algorithms. Experimental results conclusively demonstrate the significant effectiveness and advantages of the CapSA algorithm.

Anahtar Kelimeler

metaheuristic algorithm, optimization, CapSA, gear train design problem

Kaynakça

• [1] L. Costa ve P. Oliveira, “Evolutionary algorithms approach to the solution of mixed integer non-linear programming problems”, Computers & Chemical Engineering, c. 25, sy 2-3, ss. 257-266, 2001.
• [2] A. V. Fiacco ve G. P. McCormick, “The Sequential Unconstrained Minimization Technique for Nonlinear Programing, a Primal-Dual Method”, Management Science, c. 10, sy 2, ss. 360-366, Oca. 1964, doi: 10.1287/mnsc.10.2.360.
• [3] H. Adeli ve N. Cheng, “Augmented Lagrangian Genetic Algorithm for Structural Optimization”, J. Aerosp. Eng., c. 7, sy 1, ss. 104-118, Oca. 1994, doi: 10.1061/(ASCE)0893-1321(1994)7:1(104).
• [4] A. Ben-Israel, “A Newton-Raphson method for the solution of systems of equations”, Journal of Mathematical analysis and applications, c. 15, sy 2, ss. 243-252, 1966.
• [5] P. T. Boggs ve J. W. Tolle, “Sequential quadratic programming”, Acta numerica, c. 4, ss. 1-51, 1995.
• [6] B. Widrow ve J. McCool, “A comparison of adaptive algorithms based on the methods of steepest descent and random search”, IEEE transactions on antennas and propagation, c. 24, sy 5, ss. 615-637, 1976.
• [7] J. Zou, S. Ahmed, ve X. A. Sun, “Stochastic dual dynamic integer programming”, Math. Program., c. 175, sy 1-2, ss. 461-502, May. 2019, doi: 10.1007/s10107-018-1249-5.
• [8] B. Bullins, K. Patel, O. Shamir, N. Srebro, ve B. E. Woodworth, “A stochastic newton algorithm for distributed convex optimization”, Advances in Neural Information Processing Systems, c. 34, ss. 26818-26830, 2021.
• [9] K. Sörensen ve F. Glover, “Metaheuristics”, Encyclopedia of operations research and management science, c. 62, ss. 960-970, 2013.
• [10] S. E. De León-Aldaco, H. Calleja, ve J. A. Alquicira, “Metaheuristic optimization methods applied to power converters: A review”, IEEE Transactions on Power Electronics, c. 30, sy 12, ss. 6791-6803, 2015.
• [11] D. H. Wolpert ve W. G. Macready, “No free lunch theorems for optimization”, IEEE transactions on evolutionary computation, c. 1, sy 1, ss. 67-82, 1997.
• [12] E. Eker, M. Kayri, S. Ekinci, ve D. Izci, “A new fusion of ASO with SA algorithm and its applications to MLP training and DC motor speed control”, Arabian Journal for Science and Engineering, c. 46, ss. 3889-3911, 2021.
• [13] E. Eker, M. Kayri, S. Ekinci, ve D. İzci, “Comparison of Swarm-based Metaheuristic and Gradient Descent-based Algorithms in Artificial Neural Network Training”, ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, c. 12, sy 1, ss. e29969-e29969, 2023.
• [14] J. O. Agushaka, A. E. Ezugwu, L. Abualigah, S. K. Alharbi, ve H. A. E.-W. Khalifa, “Efficient initialization methods for population-based metaheuristic algorithms: a comparative study”, Archives of Computational Methods in Engineering, c. 30, sy 3, ss. 1727-1787, 2023.
• [15] E. Eker, M. Kayri, S. Ekinci, ve M. A. Kaçmaz, “Performance Evaluation of PDO Algorithm through Benchmark Functions and MLP Training”, Electrica, c. 23, sy 3, 2023, Erişim: 04 Ekim 2023. [Çevrimiçi]. Erişim adresi: https://electricajournal.org/Content/files/sayilar/96/597-606.pdf
• [16] M. Braik, A. Sheta, ve H. Al-Hiary, “A novel meta-heuristic search algorithm for solving optimization problems: capuchin search algorithm”, Neural Comput & Applic, c. 33, sy 7, ss. 2515-2547, Nis. 2021, doi: 10.1007/s00521-020-05145-6.
• [17] M. Braik, “A hybrid multi-gene genetic programming with capuchin search algorithm for modeling a nonlinear challenge problem: Modeling industrial winding process, case study”, Neural Processing Letters, c. 53, sy 4, ss. 2873-2916, 2021.
• [18] M. Kanipriya, C. Hemalatha, N. Sridevi, S. R. SriVidhya, ve S. J. Shabu, “An improved capuchin search algorithm optimized hybrid CNN-LSTM architecture for malignant lung nodule detection”, Biomedical Signal Processing and Control, c. 78, s. 103973, 2022.
• [19] A. Fathy, D. Yousri, H. Rezk, ve H. S. Ramadan, “An efficient capuchin search algorithm for allocating the renewable based biomass distributed generators in radial distribution network”, Sustainable Energy Technologies and Assessments, c. 53, s. 102559, 2022.
• [20] M. Zakaria, M. S. Seif, ve M. A. Mehanna, “Energy Management of MG Considering the Emission and Degradation Costs using A CAP-SA Optimization”, International Journal of Renewable Energy Research (IJRER), c. 12, sy 3, ss. 1452-1462, 2022.
• [21] S. Ramu, R. Ranganathan, ve R. Ramamoorthy, “Capuchin search algorithm based task scheduling in cloud computing environment”, Yanbu Journal of Engineering and Science, c. 19, sy 1, ss. 18-29, 2022.
• [22] A. Broumandnia, S. Rostami, ve A. Khademzadeh, “An Energy-Efficient Task Scheduling Method for Heterogeneous Cloud Computing Systems Using Capuchin Search and Inverted Ant Colony Optimization Algorithms”, Available at SSRN 4270250, Erişim: 13 Ekim 2023. [Çevrimiçi]. Erişim adresi: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4270250
• [23] C. Qin, H. Hu, H. Chen, ve B. Yan, “Research on Optimization Control of Deep Hole Machining Based on Capuchin Search Algorithm to Optimize Fuzzy PID”, içinde 2022 IEEE 21st International Conference on Ubiquitous Computing and Communications (IUCC/CIT/DSCI/SmartCNS), IEEE, 2022, ss. 288-293. Erişim: 13 Ekim 2023. [Çevrimiçi]. Erişim adresi: https://ieeexplore.ieee.org/abstract/document/10086312/
• [24] C. U. Om Kumar, J. Durairaj, S. A. Ahamed Ali, Y. Justindhas, ve S. Marappan, “Effective intrusion detection system for IOT using optimized capsule auto encoder model”, Concurrency and Computation, c. 34, sy 13, s. e6918, Haz. 2022, doi: 10.1002/cpe.6918.
• [25] V. U. Rani ve L. R. Burthi, “Power Quality Enhancement of Smart Home Energy Management System in Smart Grid Using MAORDF-CapSA Technique”, Ecological Engineering & Environmental Technology, c. 23, 2022, Erişim: 13 Ekim 2023. [Çevrimiçi]. Erişim adresi: https://yadda.icm.edu.pl/yadda/element/bwmeta1.element.baztech-16773c9e-e170-4b5c-a9cf-242d6fe17d16
• [26] M. Ehteram, F. Panahi, A. N. Ahmed, A. H. Mosavi, ve A. El-Shafie, “Inclusive multiple model using hybrid artificial neural networks for predicting evaporation”, Frontiers in Environmental Science, c. 9, s. 789995, 2022.
• [27] A. R. A. Alphonse, A. P. P. G. Raj, ve M. Arumugam, “Simultaneously allocating electric vehicle charging stations ( EVCS ) and photovoltaic ( PV ) energy resources in smart grid considering uncertainties: A hybrid technique”, Intl J of Energy Research, c. 46, sy 11, ss. 14855-14876, Eyl. 2022, doi: 10.1002/er.8187.
• [28] L. Wang, Q. Cao, Z. Zhang, S. Mirjalili, ve W. Zhao, “Artificial rabbits optimization: A new bio-inspired meta-heuristic algorithm for solving engineering optimization problems”, Engineering Applications of Artificial Intelligence, c. 114, s. 105082, 2022.
• [29] H. T. Sadeeq ve A. M. Abdulazeez, “Giant trevally optimizer (GTO): A novel metaheuristic algorithm for global optimization and challenging engineering problems”, IEEE Access, c. 10, ss. 121615-121640, 2022.
• [30] N. Chopra ve M. M. Ansari, “Golden jackal optimization: A novel nature-inspired optimizer for engineering applications”, Expert Systems with Applications, c. 198, s. 116924, 2022.
• [31] S. Mirjalili, “Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm”, Knowledge-based systems, c. 89, ss. 228-249, 2015.
• [32] S. Mirjalili, “The ant lion optimizer”, Advances in engineering software, c. 83, ss. 80-98, 2015.
• [33] V. F. Lenzen, “Newton’s Third Law of Motion”, Isis, c. 27, sy 2, ss. 258-260, Ağu. 1937, doi: 10.1086/347244.
• [34] S. Mirjalili, S. M. Mirjalili, ve A. Lewis, “Grey wolf optimizer”, Advances in engineering software, c. 69, ss. 46-61, 2014.
• [35] S. Zhao, T. Zhang, S. Ma, ve M. Wang, “Sea-horse optimizer: a novel nature-inspired meta-heuristic for global optimization problems”, Applied Intelligence, c. 53, sy 10, ss. 11833-11860, 2023.
• [36] S. M. Mirjalili, S. Z. Mirjalili, S. Saremi, ve S. Mirjalili, “Sine cosine algorithm: theory, literature review, and application in designing bend photonic crystal waveguides”, Nature-inspired optimizers: theories, literature reviews and applications, ss. 201-217, 2020.
• [37] A. T. Salawudeen, M. B. Mu’azu, A. Yusuf, ve A. E. Adedokun, “A Novel Smell Agent Optimization (SAO): An extensive CEC study and engineering application”, Knowledge-Based Systems, c. 232, s. 107486, 2021.
• [38] M. Mendez, D. A. Rossit, B. González, ve M. Frutos, “Proposal and comparative study of evolutionary algorithms for optimum design of a gear system”, IEEE Access, c. 8, ss. 3482-3497, 2019.
• [39] E. Sandgren, “Nonlinear Integer and Discrete Programming in Mechanical Design Optimization”, Journal of Mechanical Design, c. 112, sy 2, ss. 223-229, Haz. 1990, doi: 10.1115/1.2912596.
• [40] S. Gupta, K. Deep, H. Moayedi, L. K. Foong, ve A. Assad, “Sine cosine grey wolf optimizer to solve engineering design problems”, Engineering with Computers, c. 37, sy 4, ss. 3123-3149, Eki. 2021, doi: 10.1007/s00366-020-00996-y.
• [41] S.-J. Wu ve P.-T. Chow, “Genetic Algorithms for Nonlinear Mixed Discrete-Integer Optimization Problems via Meta-Genetic Parameter Optimization”, Engineering Optimization, c. 24, sy 2, ss. 137-159, May. 1995, doi: 10.1080/03052159508941187.