Self-adaptive teaching-learning-based optimizer with improved RBF and sparse autoencoder for high-dimensional problems

Jing Bi, Ziqi Wang, Haitao Yuan, Jia Zhang, Meng Chu Zhou

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

Abstract

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.

Original languageEnglish (US)
Pages (from-to)463-481
Number of pages19
JournalInformation sciences
Volume630
DOIs
StatePublished - Jun 2023

All Science Journal Classification (ASJC) codes

  • Software
  • Information Systems and Management
  • Artificial Intelligence
  • Theoretical Computer Science
  • Control and Systems Engineering
  • Computer Science Applications

Keywords

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

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