Education System Search: A New Population-based Metaheuristic Optimization Algorithm

سال انتشار: 1398
نوع سند: مقاله ژورنالی
زبان: انگلیسی
مشاهده: 120

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شناسه ملی سند علمی:

JR_MJEE-13-3_011

تاریخ نمایه سازی: 25 بهمن 1401

چکیده مقاله:

Optimization algorithms inspired by nature as intelligent optimization methods with classical methods have demonstrated significant success. Some of these techniques are genetic algorithms, inspired by biological evolution of humans and other creatures) ant colony optimization and simulated annealing method (inspired by the refrigeration process metals). The methods for solving optimization problems in many different areas such as determining the optimal course of their work, designing optimal control for industrial processes, solving industrial engineering major issues such as the optimal layout design for industrial units, problem solving, and queuing in the design of intelligent agents have been used. This paper introduces a new algorithm for optimization, which is not a natural phenomenon, but a phenomenon inspired teaching-human. It is entitled Education System algorithm (ESA).   Results demonstrate this method is better than other method in this area.

نویسندگان

Hossein Moradi

Department of Computer Engineering, Faculty of Computer and Electrical Engineering, University of Kashan, Kashan, Iran

Hossein Ebrahimpour-Komleh

Department of Computer Engineering, Faculty of Computer and Electrical Engineering, University of Kashan, Kashan, Iran

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  • Holland JH. Genetic algorithms. Sci Am ۱۹۹۲; ۲۶۷:۶۶–۷۲ ...
  • Dorigo M, Birattari M, Stutzle T. Ant colony optimization. Comput ...
  • Ji, M. and Tang, H. ۲۰۰۴. Global Optimizations and Tabu ...
  • Eiben, A.E., Smith, J.E., Introduction to Evolutionary Computiong, Springer ۲۰۰۳ ...
  • I. Boussaid, J. Lepagnot, P. Siarry, A survey on optimization ...
  • Yao X, Liu Y, Lin G. Evolutionary programming made faster. ...
  • Digalakis J, Margaritis K. On benchmarking functions for genetic algorithms. ...
  • Molga M, Smutnicki C. Test functions for optimization needs. Test ...
  • Yang X-S. Test problems in optimization, arXiv, preprint arXiv:۱۰۰۸.۰۵۴۹; ۲۰۱۰ ...
  • Mirjalili S, Lewis A. S-shaped versus V-shaped transfer functions for ...
  • Mirjalili S, Mirjalili SM, Yang X. Binary bat algorithm. Neural ...
  • Liang J, Suganthan P, Deb K. Novel composition test functions ...
  • van den Bergh F, Engelbrecht A. A study of particle ...
  • Liang J, Suganthan P, Deb K. Novel composition test functions ...
  • Kennedy J, Eberhart R. Particle swarm optimization, in Neural Networks, ...
  • Rashedi E, Nezamabadi-Pour H, Saryazdi S. GSA: a gravitational search ...
  • Storn R, Price K. Differential evolution – a simple and ...
  • Yao X, Liu Y, Lin G. Evolutionary programming made faster. ...
  • Mirjalili, Seyedali, Seyed Mohammad Mirjalili, and Andrew Lewis. "Grey wolf ...
  • Atashpaz-Gargari E, Lucas C (۲۰۰۷) Imperialist competitive algorithm: an algorithm ...
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