Simulation of Speed Reducer Design with the Modified Ant Colony Optimization Algorithm

Yıl 2024, Cilt: 6 Sayı: 1, 53 - 64, 30.04.2024

Kürşat Tanrıver , Mustafa Ay

https://doi.org/10.46387/bjesr.1435356

Öz

This paper focuses on the innovative application of the Modified Ant Colony Optimization (DEKKO) algorithm for the optimization of speed reducer engineering problem. The main contribution of this study is the development of DEKKO, which combines the advantageous features of Ant Colony Algorithm (KKO). The aim of DEKKO is to achieve better results than those previously solved with different techniques in the literature by modifying KKO.
The algorithm was run 20 times until the most effective result was achieved, with the best performance outcome of 3105. 8779 obtained at 14 iterations. This process utilized 100 ants and was completed in 66.81 seconds. When compared with similar results in the literature, DEKKO has achieved success with a solution that stands out among the literature results.
Users can easily obtain information about Welded beam design and pre-production through simulation using the DEKKO algorithm. This aims to contribute to cost and time savings.

Anahtar Kelimeler

Ant Colony, Engineering Design, MATLAB, Optimization, Speed Reducer Design

Değiştirilmiş Karınca Kolonisi Optimizasyon Algoritması ile Redüktör Tasarımının Simülasyonu

Öz

Bu makale, değiştirilmiş karınca kolonisi optimizasyonu (DEKKO) algoritmasının redüktör mühendislik probleminin çözümüne yeniden odaklanılmasına dayanmaktadır. DEKKO, Karınca Kolonisi Algoritmasının (KKO) avantajlı özelliklerinin birleştirilmesiyle oluşturulmuştur.DEKKO ile KKO ’da değişiklik yapılarak daha önceden literatürde farklı tekniklerle yapılan çalışmalardan daha iyi sonuçların elde edilmesi amaçlanmıştır.
Algoritma, en etkili sonuç elde edilene kadar 20 kez çalıştırılmıştır. İterasyon sayısı 14 olmak üzere en iyi performans sonucu olarak 3105,8779 sonucu elde edilmiştir. Bu işlem, algoritmada 100 adet karınca kullanılarak 66,81saniyede tamamlanmıştır. Literatürdeki sonuçlarla karşılaştırıldığında DEKKO, literatür sonuçları arasında olduğu ve başarılı bir çözümle sonuçlandığı gözlemlenmiştir.
Kullanıcılar, DEKKO algoritmasını kullanarak simülasyon yoluyla redüktör tasarımı ve ön üretim hakkında kolaylıkla bilgi edinebilmektedir. Böylelikle maliyet ve zaman tasarrufun açısından kullanıcılara katkıda bulunulması amaçlanmıştır.

Anahtar Kelimeler

Karınca Kolonisi, Mühendislik Tasarımı, MATLAB, Optimizasyon, Redüktör Tasarımı

Kaynakça

• Ö. Akçay, “Structural Optimization of the Brake Pedal using Artificial Intelligence,” International Journal of Automotive Science and Technology, vol. 7, no. 3, pp. 187–195, Sep. 2023.
• K. Tanriver, M. Ay, “Experimental, software and topological optimization study of unpredictable forces in bolted connections,” Tehnicki Vjesnik-technical Gazette, vol. 30, no. 4, Aug. 2023.
• W. Zhao, Y. Liu, Y. Li, C. Hu, and S. Rui, “Multi-robot coverage path planning for dimensional inspection of large free-form surfaces based on hierarchical optimization,” The International Journal of Advanced Manufacturing Technology, vol. 127, no. 11–12, pp. 5471–5486, Jul. 2023.
• C. Baştemur Kaya, E. Kaya, “Yüksek Boyutlu Nümerik Optimizasyon Problemlerinin Çözümünde Kelebek Optimizasyon Algoritmasının Performansının Değerlendirilmesi,” Müh.Bil.ve Araş.Dergisi, 4:296–30, 2022.
• A.M. Ebid, M.Y. Abdel-Kader, I.M. Mahdi, I.Abdel-Rasheed, “Ant Colony Optimization based algorithm to determine the optimum route for overhead power transmission lines, ” Ain Shams Engineering Journal, vol. 15, no 1,2024,102344.
• V.K. Harikrishnan, A.I. Sivakumar, S. Sampath, and S. Paramasivam, “A Time-Performance Improvement Model with Optimal Ergonomic Risk Level Using Genetic Algorithm,” Transactions of FAMENA, vol. 47, no. 4, pp. 109–128, Jan. 2023.
• İ. Avcı and M.N. Yıldırım, “Solving Weapon-Target Assignment Problem with Salp Swarm Algorithm,” Tehnicki Vjesnik-technical Gazette, vol. 30, no. 1, Feb. 2023.
• S. Katiyar and A. Dutta, “Comparative analysis on path planning of ATR using RRT*, PSO, and modified APF in CG-Space,” Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, vol. 236, no. 10, pp. 5663–5677, Jan. 2022.
• E.S. Eşsiz, V.N. Kiliç, and M. Oturakçi, “Firefly-Based feature selection algorithm method for air pollution analysis for Zonguldak region in Turkey,” Turkish Journal of Engineering, vol. 7, no. 1, pp. 17–24, Jan. 2023.
• B. Irmak and Ş. Gülcü, “Training of the feed-forward artificial neural networks using butterfly optimization algorithm,” MANAS Journal of Engineering, vol. 9, no. 2, pp. 160–168, Dec. 2021.
• M.C. Cihan, M.B. Çetinkaya, ve H. Duran “Performance comparison of artificial bee colony algorithm based approaches for retinal vessel segmentation”, BAUN Fen. Bil. Enst. Dergisi, vol. 23, no. 2, pp. 792–807, 2021.
• J.D. Farmer, N.H. Packard, and A. S. Perelson, “The immune system, adaptation, and machine learning, ” Physica D: Nonlinear Phenomena, vol. 22, no. 1-3, pp. 187-204, 1986.
• E.K. Yaylacı, A.E. Yılmaz, H.N. Özdeş, “Kızıl Tilki Optimizasyon Algoritması ile Da-Da Alçaltıcı Tip Dönüştürücü Kontrolör Katsayılarının Optimizasyonu, ” Müh.Bil.ve Araş.Dergisi, vol. 4, no. 2, pp. 129–140, 2022.
• A. Tuncer, “Otonom mobil robotların Voronoi diyagramı ve karınca kolonisi optimizasyonuna dayalı yol planlaması,” Journal of Innovative Engineering and Natural Science, Dec. 2023.
• D. Zhang, R. Luo, Y.-B. Yin, and S. Zou, “Multi-objective path planning for mobile robot in nuclear accident environment based on improved ant colony optimization with modified A∗,” Nuclear Engineering and Technology, vol. 55, no. 5, pp. 1838–1854, May 2023.
• L. Sun, Y.S. Chen, W. Ding, and J. Xu, “LEFSA: label enhancement-based feature selection with adaptive neighborhood via ant colony optimization for multilabel learning,” International Journal of Machine Learning and Cybernetics, Aug. 2023.
• A. Durmuş, Z. Yıldırım, “Synthesis of Linear Antenna Arrays with Physics Based AOA, CryStAl and LA Algorithms, ” Müh.Bil.ve Araş.Dergisi, vol. 4, no. 2, pp. 164–172, 2022.
• S. Wu, R.Guo, X. Li, "Quasi-Static Force Analysis and Tooth Profile Modification Optimization of the Cycloid Speed Reducer," Applied Sciences, vol. 14, no. 2, p. 845, 2024.
• A. Lanzotti, M. Calise, M. Molaro, S. Patalano, F.Renno, ark. ‘Federica’s MOOC’ (Massive Open Online Course): a blended course in engineering drawing at Federico II,” Int J Interact Des Manuf vol. 13, pp. 1115–1128, 2019.
• X. Zhou, W. Gui, A.A. Heidari, Z. Cai, G. Liang, and H. Chen, “Random following ant colony optimization: Continuous and binary variants for global optimization and feature selection,” Applied Soft Computing, vol. 144, p. 110513, Sep. 2023.
• Ö. Hi̇Zaroğlu, “Diz Eklemi Simülatör Prototipinin ISO 14243/3 Standardına Uygun Sistem Modelleme Çalışmasının Yapılması ve Matlab Simulink Ortamında Yürüyüş Profilinin Simule Edilmesi,” Jan. 01, 2024.
• A. Hashemi, M. Joodaki, N.Z. Joodaki, and M. B. Dowlatshahi, “Ant colony optimization equipped with an ensemble of heuristics through multi-criteria decision making: A case study in ensemble feature selection,” Applied Soft Computing, vol. 124, p. 109046, Jul. 2022.
• V. Maniezzo, L.M. Gambardella, and F. De Luigi, “Ant Colony Optimization,” in Studies in fuzziness and soft computing, pp. 101–121, 2004.
• X. Zheng, Z. Wang, D. Liu, and H. Wang, “A path planning algorithm for PCB surface quality automatic inspection,” Journal of Intelligent Manufacturing, vol. 33, no. 6, pp. 1829–1841, Apr. 2021.
• M. Das, A. Roy, S. Maity, and S. Kar, “A Quantum-inspired Ant Colony Optimization for solving a sustainable four-dimensional traveling salesman problem under type-2 fuzzy variable,” Advanced Engineering Informatics, vol. 55, p. 101816, Jan. 2023.
• J. Heins, J. Bossek, J. Pohl, M. Seiler, H. Trautmann, P. Kerschke, “A study on the effects of normalized TSP features for automated algorithm selection,” Theoretical Computer Science, 940, Part B, pp. 123-145, 2022.
• T. Ray and P. Saini, Engineering design optimization using swarm with an intelligent information sharing among individuals,” Engineering Optimization, vol. 33, no. 6, pp. 735–748, Aug. 2001.
• A.D. Belegundu, “A Study of Mathematical Programming Methods for Structural Optimization, ” Dept. of Civil and Environmental Engineering, University of Iowa, Iowa, IA, 1982.
• T. Ray and K.M. Liew, “Society and civilization: an optimization algorithm based on the simulation of social behavior,” IEEE Transactions on Evolutionary Computation, vol. 7, no. 4, pp. 386–396, Aug. 2003,
• V. Grković and R. Bulatović, “Modified Ant Colony Algorithm for Solving Engineering Optimization Problems, ” IMK-14 – Research & Development, vol. 18, no. 4, EN115-122 UDK 621, 2012.
• L.D.S. Coelho and V.C. Mariani, “Use of chaotic sequences in a biologically inspired algorithm for engineering design optimization,” Expert Systems With Applications, vol. 34, no. 3, pp. 1905–1913, Apr. 2008.
• L. Cagnina, S.C. Esquivel, and C. a. C. Coello, “Solving Engineering Optimization Problems with the Simple Constrained Particle Swarm Optimizer, ” Informatica, vol. 32, pp. 319–326, 2008.
• S.S. Rao, “Engineering Optimization: Theory and Practice, ” John Wiley & Sons, 1996.
• Q. Li, P. Xu, L. Li, W. Xu, “Tan, D. Investigation on the Lubrication Heat Transfer Mechanism of the Multilevel Gearbox by the Lattice Boltzmann Method,” Processes, vol. 12, p. 381, 2024.
• L. Maccioni, F. Concli, M. Blagojevic, “A new three-stage gearbox concept for high reduction ratios: Use of a nested-cycloidal architecture to increase the power density,” Mechanism and Machine Theory,vol. 181, p. 105203, 2023.
• M.Habib Farhat, T. Hentati, X. Chiementin, F.Bolaers, F. Chaari, M. Haddar, “ Numerical model of a single stage gearbox under variable regime,” Mechanics Based Design of Structures and Machines, vol. 51, no. 2, pp. 1054-1081, 2023.
• A.B. Tatar, “Planet Redüktörlü Robotik Aktüatör Tasarımı ve Üç Boyutlu (3B) Yazıcı ile İmalatı,” IJ3DPTDI, vol. 7, no. 2, pp. 161–168, 2023.
• M. Demir, F. Güner, “Generating a Matlab Code with Parameter Optimization in Gearbox,” Karadeniz Fen Bilimleri Dergisi, vol. 12, no. 2, pp. 1098-1107, 2022.
• S. Koçak, Y. Kaplan, A.T. Güner, “Sonsuz Vida Mekanizması ile Yeni Tasarlanan Bilyeli Sonsuz Vida Mekanizmasının Verimlerinin Deneysel Olarak Karşılaştırılması,” Gazi University Journal of Science Part C: Design and Technology, vol. 8, no. 1, pp. 160-168, 2020.
• H. Saruhan, İ. Uygur, “Design optimization of mechanical system using genetic algortihms,” SAUJS, vol. 7, no. 2, pp. 77–84, 2003.
• H. Eskandar, A. Sadollah, A. Bahreininejad, and M. Hamdi, “Water cycle algorithm – A novel metaheuristic optimization method for solving constrained engineering optimization problems,” Computers & Structures, vol. 110–111, pp. 151–166, Nov. 2012.
• A. Baykasoglu, F.B. Ozsoydan, “Adaptive firefly algorithm with chaos for mechanical design optimization problems, ” Appl soft Comput, vol. 36, pp. 152–164, 2015.
• N.B. Guedria, “ Improved accelerated pso algorithm for mechanical engineering optimization problems,” Appl Soft Comput, vol. 40, pp. 455–467, 2016.
• S. Akhtar, K. Tai, T. Ray, “A socio-behavioural simulation model of engineeringdesign optimization,” Eng. Optimiz., vol. 34, pp. 341–354, 2002.
• E. Mezura-Montes, C.A.C. Coello, R. Landa-Becerra, “Engineering optimizationusing a simple evolutionary algorithm,” in: Proceedings of the 15th IEEE Inter-national Conference on Tools with Artificial Intelligence, 2003.
• H. Aguirre, A.M. Zavala, E.V. Diharce, S.B. Rionda, “COPSO: constrained optimiza-tion via PSO algorithm,” Technical report No. I-07-04/22-02-2007, Center forResearch in Mathematics (CIMAT), 2007.
• G. Tomassetti, “A cost-effective algorithm for the solution of engineering prob-lems with particle swarm optimization,” Eng. Optimiz., vol. 42, pp. 471–495, 2010.
• B. Akay, D. Karaboga, “Artificial bee colony algorithm for large-scale prob-lems and engineering design optimization,” J. Intell. Manuf., vol. 23, no. 4, pp. 1001–1014, 2012.
• A.H. Gandomi, X.-S. Yang, A.H. Alavi, S. Talatahari, “Cuckoo search algorithm: ametaheuristic approach to solve structural optimization problems,”Eng. Appl.Artif. Intell., vol. 29, pp. 17–35, 2013.
• I. Brajevic, M. Tuba, An upgraded artificial bee colony (ABC) algorithm for con-strained optimization problems, J. Intell. Manuf., vol. 24, pp. 729–740, 2013.
• X.-S. Yang, A.H. Gandomi, “Bat algorithm: a novel approach for global engineer-ing optimization,” Eng. Comput., vol. 29, no. 5, pp. 464–483, 2012.
• E. Mezura-Montes and C. a. C. Coello, “Useful Infeasible Solutions in Engineering Optimization with Evolutionary Algorithms,” in Lecture Notes in Computer Science, pp. 652–662, 2005.
• J.N. Siddall, “Analytical Decision-Making in Engineering Design,” Prentice-Hall,Englewood Cliffs, 1972.
• J. Golinski, “An adaptive optimization system applied to machine synthesis,” Mech. Mach. Synth., vol. 8, pp. 419–436, 1973.
• M.A. Elaziz,, L. Abualigah,, A.A. Ewees, et al., “Triangular mutation-based manta-ray foraging optimization and orthogonal learning for global optimization and engineering problems”, Appl Intell, vol. 53, pp. 7788–7817, 2023.
• E.O. Wilson, B. Hölldobler, “Dense hierarchies’ and mass communication as the basis of organization in ant colonies,” Trends in Ecology and Evolution, vol. 3, no. 3, pp. 65-8, 1988.
• B. Benhala ve ark. , "Sizing of current conveyors by means of an Ant Colony Optimization technique,” International Conference on Multimedia Computing and Systems,” Ouarzazate, Morocco, 2011, pp. 1-6, 2011.
• M. Dorigo, V. Maniezzo, A. Colorni, “The Ant System: Optimization by a colony of cooperating agents”, IEEE Transactions on Systems, Man, and Cybernetics–Part B, vol. 26, no. 1, pp. 1-13, 1996.
• Q. Shen, J. Jiang, J. Tao, G. Shen, and R. Yu, “Modified Ant colony Optimization Algorithm for variable selection in QSAR modeling: QSAR studies of cyclooxygenase inhibitors,” Journal of Chemical Information and Modeling, vol. 45, no. 4, pp. 1024–1029, Apr. 2005.
• S. Gorbatyuk, A. Khan, M. Doddamani, V. Fiore, M.M. Moure Cuadrado, “Drilling characteristics and properties analysis of fiber reinforced polymer composites: A comprehensive review, ” Heliyon, vol. 9, no. 3.
• C. Li, G. Wang, Y, Jiang, H. Song, “A novel traction reducer: analysis and verification”, Journal of Advanced Mechanical Design, Systems, and Manufacturing, vol. 18, no. 2, pp. 1-18, 2024.
• E. Kalaman, “İki Kademeli Redüktör Tasarımı Ve Optimizasyonu,” Lisans tezi, Makina Mühendisliği Bölümü, T.C Beykent Üniversitesi, İstanbul, Türkiye, 2023.