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作者:

Bi, Jing (Bi, Jing.) | Wang, Ziqi (Wang, Ziqi.) | Yuan, Haitao (Yuan, Haitao.) | Zhang, Jia (Zhang, Jia.) | Zhou, MengChu (Zhou, MengChu.)

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EI Scopus SCIE

摘要:

Evolutionary algorithms and swarm intelligence ones are commonly used to solve many complex optimization problems in different fields. Yet, some of them have limited performance when dealing with high-dimensional complex problems because they often require enormous computational resources to yield desired solutions, and some of them may easily trap into local optima. To solve this problem, this work proposes a Self-adaptive Teaching-learning-based Optimizer with an improved Radial basis function model and a sparse Autoencoder (STORA). In STORA, a Self-adaptive Teaching-Learning-Based Optimizer (STLBO) is designed to dynamically adjust parameters for balancing exploration and exploitation abilities. Then, a sparse autoencoder (SAE) is adopted as a dimension reduction method to compress a search space into a lower-dimensional one for more efficiently guiding a population to converge towards global optima. Besides, an Improved Radial Basis Function model (IRBF) is designed as a surrogate one to balance training time and prediction accuracy. It is adopted to save computational resources for improving overall performance. In addition, a dynamic population allocation strategy is adopted to well integrate SAE and IRBF in STORA. We evaluate STORA by comparing it with several state-of-the-art algorithms through eight benchmark functions. We further test its actual performance by applying it to solve a real-world computation offloading problem.

关键词:

Autoencoders Teaching-learning-based optimizer Swarm intelligence algorithms Radial basis function model Evolutionary algorithms

作者机构:

  • [ 1 ] [Bi, Jing]Beijing Univ Technol, Sch Software Engn, Fac Informat Technol, Beijing 100124, Peoples R China
  • [ 2 ] [Wang, Ziqi]Beijing Univ Technol, Sch Software Engn, Fac Informat Technol, Beijing 100124, Peoples R China
  • [ 3 ] [Yuan, Haitao]Beihang Univ, Sch Automat Sci & Elect Engn, Beijing 100191, Peoples R China
  • [ 4 ] [Zhang, Jia]Southern Methodist Univ, Dept Comp Sci, Lyle Sch Engn, Dallas, TX 75205 USA
  • [ 5 ] [Zhou, MengChu]New Jersey Inst Technol, Dept Elect & Comp Engn, Newark, NJ 07102 USA

通讯作者信息:

  • [Yuan, Haitao]Beihang Univ, Sch Automat Sci & Elect Engn, Beijing 100191, Peoples R China;;

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来源 :

INFORMATION SCIENCES

ISSN: 0020-0255

年份: 2023

卷: 630

页码: 463-481

8 . 1 0 0

JCR@2022

ESI学科: COMPUTER SCIENCE;

ESI高被引阀值:19

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