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

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 d...

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Vydáno v:	Information sciences Ročník 630; s. 463 - 481
Hlavní autoři:	Bi, Jing, Wang, Ziqi, Yuan, Haitao, Zhang, Jia, Zhou, MengChu
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Elsevier Inc 01.06.2023
Témata:	Autoencoders Evolutionary algorithms Radial basis function model Swarm intelligence algorithms Teaching-learning-based optimizer Swarm intelligence algorithms Teaching-learning-based optimizer Evolutionary algorithms Radial basis function model Autoencoders
ISSN:	0020-0255, 1872-6291
On-line přístup:	Získat plný text
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Popis
Shrnutí:	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. •A self-adaptive algorithm named STORA is proposed to solve high-dimensional problems.•It uses a sparse autoencoder (SAE) to compress a search space into a lower-dimensional one.•It uses an improved RBF (IRBF) as a surrogate model to save computational resources.•STORA uses a dynamic population allocation strategy to integrate SAE and IRBF.•STORA is applied to solve a real-world computation offloading problem in mobile edge computing.
ISSN:	0020-0255 1872-6291
DOI:	10.1016/j.ins.2023.02.044