Application of Classical Bird Swarm Learning Algorithm as a Method of Optimization in Nanotechnology Systems
الموضوعات : فصلنامه نانوساختارهای اپتوالکترونیکی
Abdorreza Asrar
1
(Faculty of Naval Aviation, Malek Ashtar University of Technology, Iran)
Milad Yasrebi
2
(Faculty of Naval Aviation, Malek Ashtar University of Technology, Iran)
الکلمات المفتاحية: Optimization, Nanotechnology, Quantum, Swarm Algorithm, Cost, Speed, Particle, Standard Deviation,
ملخص المقالة :
شکی نیست که فناوری نانو نقش عمده ای در
فناوری آینده ما خواهد داشت. علوم کامپیوتر فرصت های بیشتری برای
سیستم های کوانتومی و فناوری نانو فراهم می کند. تکنیک های محاسبات نرم مانند هوش انبوه ، می
توانند سیستم هایی با ویژگی های ظهور مطلوب را قادر سازند. بهینه سازی یک
فعالیت مهم و تعیین کننده در طراحی سازه است. نیاز ارزان در حافظه و
محاسبات با عوامل مستقل نانومتری که توانایی آنها ممکن است
توسط اندازه آنها محدود شود مناسب است. برای اعمال در کنترل نانوربات ، اصلاح الگوریتم PSO
مورد نیاز است. با استفاده از رفتار یادگیری شرطی سازی کلاسیک پرندگان در این مقاله ، ذرات
یاد می گیرند که یک رفتار طبیعی شرطی را نسبت به محرک بی قید و شرط انجام دهند.
ذرات موجود در فضای مسئله به چند دسته تقسیم می شوند و اگر ذره
ای تنوع رده خود را در سطح پایین پیدا کند ، سعی می کند به سمت بهترین
تجربه شخصی خود حرکت کند. ما همچنین از ایده حساسیت پرندگان به فضایی که
آنها در آن پرواز می کنند استفاده کردیم و سعی کردیم ذرات را در فضاهای نامناسب با سرعت بیشتری حرکت دهیم تا
از فضاها خارج شوند. برعکس ، ما سرعت ذرات را در
فضاهای ارزشمند کاهش می دهیم تا بیشتر جستجو کنیم. روش پیشنهادی در
نرم افزار MATLAB پیاده سازی شده و با نتایج مشابه مقایسه شده است. نشان داده شد که روش پیشنهادی
بدون توجه به عملکردهای غیر تعیین کننده یا
شرایط تصادفی ، راه حل خوبی برای مسئله پیدا می کند .
[1] R.L. Haupt, S. Ellen Haupt, Practical genetic algorithms. (2004).
[2] S. Boyd, S.P. Boyd, L. Vandenberghe, Convex Optimization, Cambridge University Press, (2004)
[3] D. Ezzat, S. Amin, H.A. Shedeed, & M. F. Tolba, A new nano-robots control strategy for killing cancer cells using quorum sensing technique and directed particle swarm optimization algorithm. In International Conference on Advanced Machine Learning Technologies and Applications (pp. 218-226). Springer, Cham. (2019, March)
[4] W. Sun, Y. Yuan., Optimization Theory and Methods: Nonlinear Programming, Springer Science + Business Media, LLC Press, (2006)
[5] H. D. Phan, K. Ellis, J. C. Barca, & A. Dorin, A survey of dynamic parameter setting methods for nature-inspired swarm intelligence algorithms. Neural Computing and Applications, 1-22. E. H. Miller, A note on reflector arrays, IEEE Trans. Antennas Propagat., to be published. (2020).
[6] S. Hazra, & P. K. Roy, Newly-Developed Swarm Intelligence Algorithms Applied to Renewable Energy-Based Load Dispatch Real-World Problems. In Handbook of Research on Advancements of Swarm Intelligence Algorithms for Solving Real-World Problems (pp. 1-26). IGI Global (2020).
[7] X. Gong, L. Liu, S. Fong, Q. Xu, T. Wen, & Z. Liu, Comparative Research of Swam Intelligence Clustering Algorithms for Analyzing Medical Data. IEEE Access, 7, 137560-137569 (2019).
124 * Journal of Optoelectronical Nanostructures Winter 2021 / Vol. 6, No. 1
[8] J. P. Devarajan, & T. P. Robert, Swarm Intelligent Data Aggregation in Wireless Sensor Network. International Journal of Swarm Intelligence Research (IJSIR), 11(2), 1-18. (2020)
[9] Y. C. Lee, & J. Y. Moon, Bio-Nanorobotics: Mimicking Life at the Nanoscale. In Introduction to Bionanotechnology (pp. 93-114). Springer, Singapore. R. J. Vidmar. On the use of atmospheric plasmas as electromagnetic reflectors. IEEE Trans. Plasma Sci. [Online]. 21(3) (1992, Aug.) 876–880. Available: http://www.halcyon.com/pub/journals/21ps03-vidmar (2020)
[10] E. Mendez-Flores, E. M. Martinez-Galicia, J. D. J. Lozoya-Santos, R. Ramirez-Mendoza, R. Morales-Menendez, I. Macias-Hidalgo, A. & Molina-Gutierrez. Self-Balancing Robot Control Optimization Using PSO. In 2020 5th International Conference on Control and Robotics Engineering (ICCRE) (pp. 7-10). IEEE (2020, April).
[11] M. Li, N. Xi, Y. Wang, & L. Liu. Progress in nanorobotics for advancing biomedicine. IEEE Transactions on Biomedical Engineering. (2020)
[12] A. M. R. Kabir, D. Inoue, & A. Kakugo. Molecular swarm robots: recent progress and future challenges. Science and Technology of Advanced Materials, (just-accepted). (2020).
[13] J. Kennedy, R. C. Eberhart. Particle Swarm Optimization, Proceedings of the 4th IEEE International Conference on Neural Networks, pp. 1942-1948.(1995)
[14] N. F. Wan, L. Nolle, Solving a multi-dimensional knapsack problem using hybrid particle. 23rd European Conference on Modelling and Simulation. (2008)
[15] K. B. Deep, A socio-cognitive particle swarm optimization for multi-dimensional. First International Conference on Emerging Trends in Engineering and, pp. 355–360. (2008).
[16] X. Shen, Y. Li, C. Chen, J. Yang, D. Zhang. Greedy continuous particle swarm optimisation algorithm for the knapsack problems. International Journal of Computer Applications in Technology 44 (2), 37–144. (2012).
[17] H. S. Lopes, L. S. Coelho. Particle swarn optimization with fast local search for the blind traveling salesman problem. International Conference on Hybrid Intelligent Systems, pp. 245–250. (2005)
[18] H. Zhou, M. Song, W. Pedrycz,.A comparative study of improved GA and PSO in solving multiple traveling salesmen problem. Applied Soft Computing, 64, 564-580. (2018).
Application of Classical Bird Swarm Learning Algorithm as a Method of Optimization … * 125
[19] A. Banharnsakun, B. Sirinaovakul, T. Achalakul. Job shop scheduling with the best-so-far ABC. Engineering Applications of Artificial Intelligence 25 (3), pp. 583–593. (2012)
[20] D. Karaboga, B. Gorkemli, A combinatorial artificial bee colony algorithm for traveling salesman problem. International Symposium on Intelligent Systems and Applications, pp. 50–53. (2011)
[21] Z. Geem, J. Kim, G. Loganathan. A new heuristic optimization algorithm: Harmony search.Simulation, 60. (2001)
[22] D. T. Pham, A. Ghanbarzadeh, E. Koc, S. Otri, S. Rahim, M. Zaidi, The bees algorithm. Technical note, Cardiff University, UK: Manufacturing Engineering Center. (2005)
[23] D. T. Pham, S. Otri, A. Afify, M. Mahmuddin, H. Al-Jabbouli, Data clustering using the bees algorithm. 40thCIRPInternational Seminar on Manufacturing Systems, p. p. s.p. (2007)
[24] D. Pham, E. Koc, J. Lee, J. Phrueksanant, Using the bees algorithm to schedule jobs for a machine. Proceedings of Eighth International Conference on Laser Metrology, pp. 430–439, CMM and Machine. (2007)
[25] S. Hazra, & P. K. Roy,. Newly-Developed Swarm Intelligence Algorithms Applied to Renewable Energy-Based Load Dispatch Real-World Problems. In Handbook of Research on Advancements of Swarm Intelligence Algorithms for Solving Real-World Problems (pp. 1-26). IGI Global. (2020)
[26] P. Civicioglu. Transforming geocentric cartesian coordinates to geodeticcoordinates by using differential search algorithm. Comput, Geosciuk, vol. 46,no. 9, pp. 229-247, Sep. (2012)
[27] A. Gandomi, Bird mating optimizer: An optimization algorithm inspired by birdmating strategies. Commun Nonlinear Sci, vol. 19, no. 4, pp. 1213-1228, Apr. (2014)
[28] A. Draa, S. Bouzoubia, L. Boukhalfa, A sinusoidal differential evolution algorithmfor numerical optimisation, Appl. Soft Comput. 27 (2015) 99–126. (2015).
[29] G. Sun, R. Zhao, Y. Lan, Joint operations algorithm for large-scale global optimization. Applied Soft Computing, 38: 1025-1039. (2016).
[30] X. Yan, F. He, N. Hou, & H. Ai. An efficient particle swarm optimization for large-scale hardware/software co-design system. International Journal of Cooperative Information Systems, 27(01), 1741001. (2018)
126 * Journal of Optoelectronical Nanostructures Winter 2021 / Vol. 6, No. 1
[31] B. Tang, Z. Zhu, H. S. Shin, A. Tsourdos, & J. Luo. A framework for multi-objective optimisation based on a new self-adaptive particle swarm optimisation algorithm. Information Sciences, 420, 364-385. (2017)
[32] H. Su, Z. Fu, & Z. Wen. SFPSO algorithm-based multi-scale progressive inversion identification for structural damage in concrete cut-off wall of embankment dam. Applied Soft Computing, 84, 105679. (2019)
[33] X. Xu, Y. Tang, J. Li, C. C. Hua, X. P. Guan, Dynamic multi-swarm particle swarmoptimizer with cooperative learning strategy, Appl. Soft Comput. 29, 169–183. (2015).
[34] I. G. Tsoulos, A. Tzallas, & E. Karvounis, Improving the PSO method for global optimization problems. Evolving Systems, 1-9. (2020)
[35] M. Yasrebi, A. E. Baghban, H. Parvin, H., & M. Mohammadpour Optimisation inspiring from behaviour of raining in nature: droplet optimisation algorithm. International Journal of Bio-Inspired Computation, 12(3), 152. (2018).
[36] X. S. Yang, S. Deb. S. Cuckoo, search via Levy flights, in Proc. NaBIC 2009, IEEE Publications, pp. 210-214, Dec. 2009. 18 / Information Sciences XX 1–22 19. (2014)
[37] E. Rashedi, H. Nezamabadi-pour, S, Saryazdi. GSA: A Gravitational Search Algorithm, Inform. Sciences, vol. 179, no. 13, pp. 2232-2248. (2009)
[38] P. N. Suganthan, N. Hansen, J. J. Liang, Problem definitions and evaluation criteria for the CEC 2005 Special Session on Real Parameter Optimization, Nanyang Technological University, Singapore, Tech. Rep, May. 2005[Online].
Available: http:// www3.ntu.edu.sg/home/EPNSugan/index f iles/CEC-05/Tech- Repot-May-30-05.pdf. (2005
