Providing a Bird Swarm Algorithm based on Classical Conditioning Learning Behavior and Comparing this Algorithm with sinDE, JOA, NPSO and D-PSO-C Based on Using in Nanoscience
الموضوعات : فصلنامه نانوساختارهای اپتوالکترونیکیAbdorreza Asrar 1 , Mojtaba Servatkhah 2 , Milad Yasrebi 3
1 - Malek Ashtar University of Technology
2 - Department of Physics, Marvdasht Branch, Islamic Azad University, Marvdasht, Iran
3 - Faculty of Naval Aviation, Malek Ashtar University of Technology, Iran
الکلمات المفتاحية: Optimization, Nanotechnology, Quantum, Swarm Algorithm, Cost, Speed, Particle, Standard Deviation,
ملخص المقالة :
There can be no doubt that nanotechnology will play a major role in our future
technology. Computer science offers more opportunities for quantum and
nanotechnology systems. Soft Computing techniques such as swarm intelligence, can
enable systems with desirable emergent properties. Optimization is an important and
decisive activity in structural designing. The inexpensive requirement in memory and
computation suits well with nanosized autonomous agents whose capabilities may be
limited by their size. To apply in nanorobot control, a modification of PSO algorithm is
required. Using birds’ classical conditioning learning behavior in this paper, particles will
learn to perform a natural conditional behavior towards an unconditioned stimulus.
Particles in the problem space are divided into multiple categories and if any particle finds
the diversity of its category in a low level, it will try to move towards its best personal
experience. We also used the idea of birds’ sensitivity to the space in which they fly and
tried to move the particles more quickly in improper spaces so that they would depart the
spaces. On the contrary, we reduced the particles’ speed in valuable spaces in order to do
more search. The proposed method was implemented in MATLAB software and
compared to similar results. It was shown that the proposed method finds a good solution
to the problem regardless of nondeterministic functions or stochastic conditions.
[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)
Providing a Bird Swarm Algorithm based on Classical Conditioning Learning Behavior * 55
[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).
[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).
56 * Journal of Optoelectronical Nanostructures Summer 2020 / Vol. 5, No. 3
[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).
[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)
Providing a Bird Swarm Algorithm based on Classical Conditioning Learning Behavior * 57
[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)
[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] Y. Liang, Y. Liu, L. Zhang, An Improved Artificial Bee Colony (ABC) Algorithm for Large Scale Optimization, 2nd International Symposium on Instrumentation and Measurement, Sensor Network and Automation (IMSNA), IEEE, 978-1-4799-2716-6/13/$31.00, (2013).
[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)
58 * Journal of Optoelectronical Nanostructures Summer 2020 / Vol. 5, No. 3
[37] A. Ghadimi, and M. Ahmadzadeh, Effect of variation of specification o quantum well and contact length on performance of InP-based Vertical Cavity Surface Emitting Laser (VCSEL), JOURNAL of OPTOELECTRONICAL NANO STRUCTURES,5(1),(2020.),19-34,Available: http://jopn.miau.ac.ir/article_4031.html
[38] M. Rezvani. J, Simulation of Direct Pumping of Quantum Dots in a Quantum Dot Laser, JOPN. [Online]. 2(2), (2017), 61-70, Available: http://jopn.miau.ac.ir/article_2425.html
[39] M. Riahinasab, and E. Darabi, Analytical Investigation of Frequency Behavior in Tunnel Injection Quantum Dot VCSEL, JOURNAL of OPTOELECTRONICAL NANOSTRUCTURES, 3(2), (2020.), 65-86, Available: http://jopn.miau.ac.ir/article_4031.html
[40] Z. Danesh. K, Improving Blue InGaN Laser Diodes Performance with Waveguide Structure Engineering, JOPN. [Online]. 4(1), (2019), 1-26, Available: http://jopn.miau.ac.ir/article_3382.html
[41] M. Zaki, M. Hosseini, Controlling the Occurrence of Rogue Waves in an Optically Injected Semiconductor Laser via Changing The Injection Strength, JOPN. [Online]. 2(3), (2017), 39-46, Available: http://jopn.miau.ac.ir/article_2430.html
[42] A. Horri, S. Z. Mirmoeini, Analysis of Kirk Effect in Nanoscale Quantum Well Heterojunction Bipolar Transistor Laser, JOPN. [Online]. 5(2), (2020), 25-38, Available: http://jopn.miau.ac.ir/article_4216.html
