Teaching Learning Based Optimization (TLBO)是一种population method,它基于以下知识:与学生共享教室的老师会想办法提高班级的知识水平。 此外,通过班级学生的平均value of the qualification评估学生。同时,学生之间的互动学习可以改善求解结果。 population由一组学生组成,所提供的科目由变量构成,适应度与学生的学习结果相...
This chapter examines the benefits of the state-of-the-art approaches to optimization-based meta-learning: classical long short-term memory (LSTM) meta-learner; model-agnostic meta-learning (MAML) and its variations in few-shot learning, reinforcement learning, and intimation learning; first-order...
Combinatorial optimization problems are ubiquitous and computationally hard to solve in general. Quantum approximate optimization algorithm (QAOA), one of the most representative quantum-classical hybrid algorithms, is designed to solve combinatorial optimization problems by transforming the discrete optimization ...
The process of selecting a set of optimal hyper-parameters for a learning algorithm is known as hyper-parameter tuning or optimization. A hyper-parameter is a value for a parameter that is used to influence the learning process. We used the optimal tuning parameters for SVR are C=100, gamma...
Learning-based joint UAV trajectory and power allocation optimization for secure IoT networks邓单 Dan Deng ¹, 李兴旺 Xingwang Li ², Varun Menon ³, Md Jalil Piran ⁴, 陈慧 Hui Chen ², Mian Ahmad …
In this study, two optimization algorithms, the teaching-learning-based optimization (TLBO) and satin bowerbird optimizer (SBO), were employed to promote the predictive capability of the original ANFIS model in the Linyou County of China, which is a landslide prone area. Based on ArcGIS tools,...
Learning-based Optimization of the Under-sampling Pattern in MRI Acquisition of Magnetic Resonance Imaging (MRI) scans can be accelerated by under-sampling in k-space (i.e., the Fourier domain). In this paper, we consider the problem of optimizing the sub-sampling pattern in a data-driven ...
simulator-based optimization is that one can perform an optimal search on a computer without the need for trial-and-error in physical space. The utility of a simulator for optimization is directly linked to its range of validity, which provides hard bounds to the possible search space. Such ...
In this study, the teaching-learning-based optimization (TLBO) and satin bowerbird optimizer (SBO) algorithms were applied to optimize the adaptive neuro-fuzzy inference system (ANFIS) model for landslide susceptibility mapping. In the study area, 152 landslides were identified and randomly divided ...
(ISHD) and learning-based approaches for developing optimization methods through a deep synergy of theoretical insights. We first establish the convergence condition for ensuring the convergence of the solution trajectory of ISHD. Then, we show that provided the stability condition, another relaxed ...