ورود
مقالات فارسیISIکنفرانسهاژورنالها

یک الگوریتم الهام گرفته از طبیعت مبتنی بر نظریه شرطی سازی کلاسیک

محل انتشار: فصلنامه مهندسی برق دانشگاه تبریز، دوره: 49، شماره: 2
سال انتشار: 1398
نوع سند: مقاله ژورنالی
زبان: انگلیسی
مشاهده: 404

فایل این مقاله در 17 صفحه با فرمت PDF قابل دریافت می باشد

استخراج به نرم افزارهای پژوهشی:

لینک ثابت به این مقاله:
https://civilica.com/doc/966352

شناسه ملی سند علمی:

JR_TJEE-49-2_003

تاریخ نمایه سازی: 20 آذر 1398

چکیده مقاله:

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.

کلیدواژه ها:

PSO Algorithm ، optimization ، particles cost ، velocity equation ، classical conditioning

نویسندگان

R. Omidvar

Department of Computer Engineering, Yasooj Branch, Islamic Azad University, Yasooj, Iran

S. Nejatian

Department of Electrical Engineering, Yasooj Branch, Islamic Azad University| Young Researchers and Elite Clubs, Yasooj Branch, Islamic Azad University

H. Parvin

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

V. Rezaei

Young Researchers and Elite Clubs, Yasooj Branch, Islamic Azad University, Yasooj, Iran | Department of Mathematic, Yasooj Branch, Islamic Azad University, Yasooj, Iran

مراجع و منابع این مقاله:

لیست زیر مراجع و منابع استفاده شده در این مقاله را نمایش می دهد. این مراجع به صورت کاملا ماشینی و بر اساس هوش مصنوعی استخراج شده اند و لذا ممکن است دارای اشکالاتی باشند که به مرور زمان دقت استخراج این محتوا افزایش می یابد. مراجعی که مقالات مربوط به آنها در سیویلیکا نمایه شده و پیدا شده اند، به خود مقاله لینک شده اند :
  • م. امیرعباسیان، ح. نظام آبادی پور، الگوریتم جستجوی گرانشی چندهدفه ... [مقاله ژورنالی]
  • ش. جمالی، س. ملک تاجی، م. آنالویی، مکان یابی ماشین ... [مقاله ژورنالی]
  • م. محمدپور، ح. پروین، الگوریتم ژنتیک آشوب گونه مبتنی بر ... [مقاله ژورنالی]
  • R. Haupt and S. E. Haupt, Practical Genetic Algorithms , ...
  • H. Yapıcı and N. Çetinkaya, An Improved Particle Swarm Optimization ...
  • W. Sun and Y. Yuan, Optimization Theory and Methods: Nonlinear ...
  • Classical conditioning, The Gale encyclopedia of psychology , Gale Group, ...
  • J. Holland, Genetic algorithms and the optimal allocation of trials ...
  • F. Ali and M. Tawhid, A hybrid particle swarm optimization ...
  • J. Kennedy and R. Eberhar, Particle Swarm Optimization , Proceedings ...
  • N F. Wan and L. Nolle, Solving a multi-dimensional knapsack ...
  • K B. Deep, A socio-cognitive particle swarm optimization for multi-dimensional ...
  • X. Shen, Y. Li, C. Chen, J. Yang, D. Zhang, ...
  • H S. Lopes and L S. Coelho, Particle swarn optimization ...
  • D. Karaboga and B. Basturk, A powerful and efficient algorithm ...
  • A. Banharnsakun and B. Sirinaovakul, T. Achalakul, Job shop scheduling ...
  • D. Karaboga and B. Gorkemli, A combinatorial artificial bee colony ...
  • Z. Geem, J. Kim,  G. Loganathan, A new heuristic optimization ...
  • DT. Pham, A. Ghanbarzadeh, E. Koc, S. Otri, S. Rahim, ...
  • D T. Pham, S. Otri, A. Afify, M. Mahmuddin, H. ...
  • D. Pham, E. Koc, J. Lee, J. Phrueksanant, Using the ...
  • X. Miao, J. Chu, L. Zhang, J. Qiao, An Evolutionary ...
  • M. Cheng and L. Lien, Hybrid artificial intelligencebased pba for ...
  • W. Feng and Ch. Liu, A Novel Particle Swarm Optimization ...
  • X. S. Yang and S. Deb, Cuckoo search via Levy ...
  • P. Civicioglu, Transforming geocentric cartesian coordinates to geodeticcoordinates by using ...
  • A. Gandomi, Bird mating optimizer: An optimization algorithm inspired by ...
  • A. Draa, S. Bouzoubia, I. Boukhalfa, A sinusoidal differential evolution ...
  • G. Sun, R. Zhao, Y. Lan, Joint operations algorithm for ...
  • X. Xu, Y. Tang, J. Li, CC. Hua, X P. ...
  • J. Wang, B. Zhou, Sh, Zhou, An Improved Cuckoo Search ...
  • E R. Tanweer, S. Suresh, N. Sundararajan, Self regulating particle ...
  • F T. Zhao, Zh. Yao, J. Luan, X. Son, A ...
  • M. Thankur, A new genetic algorithm for global optimization of ...
  • Q. Zhang, A.Zhou, Sh. Zhao, P. Suganthan, W. Liu, S. ...
  • R. Storn and K. Price, Differential evolution a simple and ...
  • A. Gao and W B. Xu, A new particle swarm ...
  • R. Mallipeddi, P N. Suganthan, Q. Pan, M. Tasgetiren, Differential ...
  • Y. Liang, Y. Liu,  L. Zhang, An Improved Artificial Bee ...
  • X S. Yang, Nature-Inspired Metaheuristic Algorithms: Second Edition , Luniver ...
  • E. Rashedi, H. Nezamabadi-pour, S. Saryazdi, GSA: A Gravitational Search ...
  • R M. Rizk Allah, Hybridization of Fruit Fly Optimization Algorithm ...
  • J. G. Villegas, Using Nonparametric Test to Compare the Performance ...
  • Statistical Consultant for Doctoral Students and Researchers, http://www.statisticallysignificantconsulting.com/Ttest.htm. ...
    • نمایش کامل مراجع