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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Veröffentlicht in:Information sciences Jg. 630; S. 463 - 481
Hauptverfasser: Bi, Jing, Wang, Ziqi, Yuan, Haitao, Zhang, Jia, Zhou, MengChu
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier Inc 01.06.2023
Schlagworte:
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
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Zusammenfassung: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