یک الگوریتم الهام‌گرفته از طبیعت مبتنی بر نظریه شرطی‌سازی کلاسیک

نویسندگان

1 دانشکده مهندسی کامپیوتر - واحد یاسوج - دانشگاه آزاد اسلامی

2 دانشکده مهندسی برق - دانشگاه آزاد اسلامی - واحد یاسوج

3 باشگاه پژوهشگران جوان و نخبگان - واحد یاسوج - دانشگاه ازاد اسلامی

4 دانشکده مهندسی کامپیوتر - واحد نورآباد ممسنی - دانشگاه آزاد اسلامی

5 باشگاه پژوهشگران جوان و نخبگان - واحد نوراباد ممسنی - دانشگاه آزاد اسلامی

6 دانشکده ریاضی - واحد یاسوج - دانشگاه آزاد اسلامی

چکیده

الگوریتم‌های فرا ابتکاری الهام‌گرفته از طبیعت که به تقلید از طبیعت می‌باشند، یک دوره جدید را در حل مسائل بهینه‌سازی باز کردند. در این مقاله با استفاده از رفتار یادگیری شرطی‌سازی کلاسیک پرندگان، ذرات یاد می‌گیرند یک رفتار طبیعی شرطی را در قبال یک محرک غیرشرطی انجام دهند. ذرات در فضای مسئله به چندین دسته تقسیم خواهند شد و هر ذره اگر تنوع دسته خود را در سطح پایینی دید، سعی خواهد کرد به سمت بهترین تجربه شخصی خود حرکت کند و اگر سطح تنوع دسته بالا بود ذره یاد خواهد گرفت که در این شرایط به سمت بهینه عمومی دسته خود متمایل شود. همچنین با استفاده از ایده حساسیت پرندگان نسبت به فضایی که در آن پرواز می‌کنند، سعی شده که ذرات در فضاهای نامناسب با سرعت بیشتری به‌حرکت درآمده تا ذره از آن فضا دور گردد و بالعکس در فضاهای پرارزش سرعت ذرات جهت جستجوی بیشتر، پایین خواهد آمد. در جمعیت‌دهی اولیه نیز با استفاده از رفتار غریزی پرندگان، یک جمعیت‌دهی براساس شایستگی ذرات انجام خواهد شد. روش پیشنهادی در نرم‌افزار متلب پیاده‌سازی شده و نتایج در چندین بخش با روش‌های مختلف مشابه مقایسه و نتایج حاکی از آن بوده که روش پیشنهادی یک الگوریتم قابل‌اتکا در حل مسائل بهینه‌سازی ایستا می‌باشد.

کلیدواژه‌ها


عنوان مقاله [English]

A Nature-inspired Algorithm based on Classical-conditioning Theory

نویسندگان [English]

  • R. Omidvar 1
  • S. Nejatian 2 3
  • H. Parvin 4 5
  • V. Rezaei 3 6
  • M. Yasrebi 4
1 Department of Computer Engineering, Yasooj Branch, Islamic Azad University, Yasooj, Iran
2 Department of Electrical Engineering, Yasooj Branch, Islamic Azad University| Young Researchers and Elite Clubs, Yasooj Branch, Islamic Azad University
3 Department of Electrical Engineering, Yasooj Branch, Islamic Azad University| Young Researchers and Elite Clubs, Yasooj Branch, Islamic Azad University
4 Department of Computer Engineering, Nourabad Mamasani Branch, Islamic Azad University, Nourabad Mamasani | Young Researchers and Elite Club, Nourabad Mamasani Branch, Islamic Azad University, Nourabad Mamasani, Iran
5 Department of Computer Engineering, Nourabad Mamasani Branch, Islamic Azad University, Nourabad Mamasani | Young Researchers and Elite Club, Nourabad Mamasani Branch, Islamic Azad University, Nourabad Mamasani, Iran
6 Young Researchers and Elite Clubs, Yasooj Branch, Islamic Azad University, Yasooj, Iran | Department of Mathematic, Yasooj Branch, Islamic Azad University, Yasooj, Iran
چکیده [English]

Nature-inspired algorithms are the imitation of nature opened a new era in calculations for solving optimization problems. In this thesis, we will provide an optimization algorithm inspired by nature using the instinctive behavior of birds. In this thesis, particles learn to have a conditional normal behavior towards an unconditioned stimulus using the classical conditioning learning behavior of birds. Particles will be divided into multiple categories in the problem space. If any particle had a low-level category, it will try to move towards its best personal experience. If any particle had a high-level category, it will learn to move towards the global optimum in its category. Using the idea of birds’ sensitivity towards the environment, in which birds are flying, we tried to move particles in incompetent spaces more quickly so that the particle goes far away from that space, and vice versa, we will bring down the particles’ speed in valuable spaces to search for more. We selected a population based on the particles’ merit in the initial population selection using the instinctive behavior of birds. The proposed method was implemented in MATLAB software, and the results have been compared in several different ways. The results showed that the proposed method is a reliable algorithm to solve the static problems.

کلیدواژه‌ها [English]

  • PSO Algorithm
  • optimization
  • particles cost
  • velocity equation
  • classical conditioning
[1] R. Haupt and S. E. Haupt, “Practical Genetic Algorithms”, 2nd Edition, John Wiley & Sons Inc, 2004.
[2] H. Yapıcı and N. Çetinkaya, “An Improved Particle Swarm Optimization Algorithm Using Eagle Strategy for Power Loss Minimization”, Hindawi, Mathematical Problems in Engineering, doi.org/10.1155/2017/1063045, 2017.
[3] W. Sun and Y. Yuan, “Optimization Theory and Methods: Nonlinear Programming”, Springer Science Business Media, LLC Press, 2006.
[4] Classical conditioning, “The Gale encyclopedia of psychology”, Gale Group, p. 124, 2001.
[5] J. Holland, “Genetic algorithms and the optimal allocation of trials”.SIAM J. Comput. 2, 88-105, 1979.
[6] F. Ali and M. Tawhid, “A hybrid particle swarm optimization and genetic algorithm with population partitioning for large scale optimization problems”, Ain Shams Engineering Journal, doi.org/10.1016/j.asej.2016.07.008, 2016.
[7] J. Kennedy and R. Eberhar, “Particle Swarm Optimization”, Proceedings of the 4th IEEE International Conference on Neural Networks, pp. 1942-1948, 1995.
[8] N F. Wan and L. Nolle, “Solving a multi-dimensional knapsack problem using hybrid particle”.23rd European Conference on Modelling and Simulation, 2008.
[9] K B. Deep, “A socio-cognitive particle swarm optimization for multi-dimensional”. First International Conference on Emerging Trends in Engineering and, 355–360, 2008.
[10] 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.
[11] H S. Lopes and L S. Coelho, “Particle swarn optimization with fast local search for the blind traveling salesman problem”. International Conference on Hybrid Intelligent Systems, 245–250, 2005.
[12] D. Karaboga and B. Basturk, “A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm”. Journal of Global Optimization 39, 2007.
[13] A. Banharnsakun and B. Sirinaovakul, “T. Achalakul, Job shop scheduling with the best-so-far ABC”.Engineering Applications of Artificial Intelligence 25 (3), 583–593, 2012.
[14] D. Karaboga and B. Gorkemli, “A combinatorial artificial bee colony algorithm for traveling salesman problem”.International Symposium on Intelligent Systems and Applications, pp. 50–53, 2011.
[15] Z. Geem, J. Kim,  G. Loganathan, “A new heuristic optimization algorithm: Harmony search”.Simulation, 60, 2011.
[16] DT. Pham, A. Ghanbarzadeh, E. Koc, S. Otri, S. Rahim, M. Zaidi, “The bees algorithm”. Technical note, Cardiff University, UK: Manufacturing Engineering Center, 2005.
[17] D T. Pham, S. Otri, A. Afify, M. Mahmuddin, H. Al-Jabbouli, “Data clustering using the bees algorithm”. 40thCIRPInternational Seminar on Manufacturing Systems, 2007.
[18] 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, 430–439, CMM and Machine, 2007.
[19] X. Miao, J. Chu, L. Zhang, J. Qiao, “An Evolutionary Neural Network Approach to Simple Prediction of Dam Deformation”, Journal of Information & Computational Science, 1315–1324, 2013.
[20] M. Cheng and L. Lien, “Hybrid artificial intelligencebased pba for benchmark functions and facility layout design optimization”.  Journal of Computing in Civil Engineering, 26, 612–624, 2012.
[21] W. Feng and Ch. Liu, “A Novel Particle Swarm Optimization Algorithm for Global Optimization”, Hindawi Publishing Corporation Computational Intelligence and Neuroscience Volume 2016, Article ID 9482073, 9 pages, 2016.
[22] X. S. Yang and S. Deb, “Cuckoo search via Levy flights”, in ´ Proc. NaBIC 2009, IEEE Publications, 210-214, Dec. 2009. 18 / Information Sciences XX 1–22 19, 2014.
[23] P. Civicioglu, “Transforming geocentric cartesian coordinates to geodeticcoordinates by using differential search algorithm”. Comput, Geosciuk, 229-247, 2012.
[24] A. Gandomi, “Bird mating optimizer: An optimization algorithm inspired by birdmating strategies”. Commun Nonlinear Sci, 1213-1228, 2014.
[25] A. Draa, S. Bouzoubia, I. Boukhalfa, “A sinusoidal differential evolution algorithmfor numerical optimization”, Appl. Soft Comput, 99–126, 2015.
[26] G. Sun, R. Zhao, Y. Lan, “Joint operations algorithm for large-scale global optimization”. Applied Soft Computing, 38: 1025-1039, 2016.
[27] X. Xu, Y. Tang, J. Li, CC. Hua, X P. Guan, “Dynamic multi-swarm particle swarmoptimizer with cooperative learning strategy”, Appl. Soft Comput. 29, 169–183, 2015.
[28] J. Wang, B. Zhou, Sh, Zhou, “An Improved Cuckoo Search Optimization Algorithm for the Problem of haotic Systems Parameter Estimation”, Hindawi Publishing Corporation, Computational Intelligence and Neuroscience, Volume 2016, 10.1155/2016/2959370, 2016.
[29] E R. Tanweer, S. Suresh, N. Sundararajan, “Self regulating particle swarm optimization algorithm”, Innovative Applications of Artificial Neural Networks in Engineering, Volume 294, 182–202, 2015.
[30] F T. Zhao, Zh. Yao, J. Luan, X. Son, “A Novel Fused Optimization Algorithm of Genetic Algorithm and Ant Colony Optimization”, athematical Problems in Engineering, Volume 2016 (2016), Article ID 2167413, 2016.
[31] M. Thankur, “A new genetic algorithm for global optimization of multimodal continuous functions”, Journal of Computational Science, 298–311, 2014.
[32] Q. Zhang, A.Zhou, Sh. Zhao, P. Suganthan, W. Liu, S. Tiwari, “Multiobjective optimization test instances for the CEC 2009 Special Session and Competition”, Technical Report CES-487, 2009.
[33] R. Storn and K. Price, “Differential evolution a simple and efficient heuristic for global optimization over continuous spaces”, Journal of Global Optimization, 11 (1997) 341–359, 1997.
[34] A. Gao and W B. Xu, “A new particle swarm algorithm and its globally convergent modifications”, IEEE Trans. Syst. Man. Cy. B, vol. 41, no. 5, 1334-1351, 2011.
[35] R. Mallipeddi, P N. Suganthan, Q. Pan, M. Tasgetiren, “Differential evolution algorithm with ensemble of parameters and mutation strategies”, Appl. Soft. Comput, 1679-1696, 2011.
[36] 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.
[37] X S. Yang, “Nature-Inspired Metaheuristic Algorithms: Second Edition”, Luniver Press, 2011.
[38] E. Rashedi, H. Nezamabadi-pour, S. Saryazdi, “GSA: A Gravitational Search Algorithm”, Inform. Sciences, 2232-2248, 2009.
[39] R M. Rizk Allah, “Hybridization of Fruit Fly Optimization Algorithm and Firefly Algorithm for Solving Nonlinear Programming Problems”, International Journal of Swarm Intelligence and  Evolutionary Computation, 2016.
[40] J. G. Villegas, “Using Nonparametric Test to Compare the Performance of Metaheuristics”, friedman-test-24062011.pdf, 2001.
[41] Statistical Consultant for Doctoral Students and Researchers, http://www.statisticallysignificantconsulting.com/Ttest.htm.
[42] م. امیرعباسیان، ح. نظام‌آبادی پور، «الگوریتم جستجوی گرانشی چندهدفه مبتنی بر مرتب‌سازی چبهه‌های مغلوب‌نشده»، مجله مهندسی برق دانشگاه تبریز، شماره 1 جلد 41، ص61-81‌، 1391.
[43] ش. جمالی، س. ملک تاجی، م. آنالویی، « مکان‌یابی ماشین‌های مجازی با استفاده از الگوریتم رقابت استعماری»، مجله مهندسی برق دانشگاه تبریز، شماره 1 جلد 46، ص75، 1395.
[44] م. محمدپور، ح. پروین، « الگوریتم ژنتیک آشوب‌گونه مبتنی بر حافظه و خوشه‌بندی برای حل مسائل بهینه‌سازی پویا»، مجله مهندسی برق دانشگاه تبریز، شماره 3 جلد 46، ص77، 1395.