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Self-adaptive teaching-learning-based optimizer with improved RBF and sparse autoencoder for high-dimensional problems by Jing Bi; Ziqi Wang; Haitao Yuan; Jia Zhang; MengChu Zhou is a Computer Science article available to read on EtoBox.

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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

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Author

Jing Bi; Ziqi Wang; Haitao Yuan; Jia Zhang; MengChu Zhou

Publisher

Elsevier BV

Published

2023

Language

EN

Field

Computer Science (Physical Sciences)