Self-adaptive teaching-learning-based optimizer with improved RBF and sparse autoencoder for high-dimensional problems
Document Type
Article
Publication Date
6-1-2023
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.
Identifier
85148692072 (Scopus)
Publication Title
Information Sciences
External Full Text Location
https://doi.org/10.1016/j.ins.2023.02.044
ISSN
00200255
First Page
463
Last Page
481
Volume
630
Grant
62073005
Fund Ref
National Natural Science Foundation of China
Recommended Citation
Bi, Jing; Wang, Ziqi; Yuan, Haitao; Zhang, Jia; and Zhou, Meng Chu, "Self-adaptive teaching-learning-based optimizer with improved RBF and sparse autoencoder for high-dimensional problems" (2023). Faculty Publications. 1708.
https://digitalcommons.njit.edu/fac_pubs/1708