Deep Progressive Reinforcement Learning-Based Flexible Resource Scheduling Framework for IRS and UAV-Assisted MEC System.

The intelligent reflecting surface (IRS) and unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) system is widely used in temporary and emergency scenarios. Our goal is to minimize the energy consumption of the MEC system by jointly optimizing UAV locations, IRS phase shift, task offloading, and resource allocation with a variable number of UAVs. To this end, we propose a flexible resource scheduling (FRES) framework by employing a novel deep progressive reinforcement learning that includes the following innovations. First, a novel multitask agent is presented to deal with the mixed integer nonlinear programming (MINLP) problem. The multitask agent has two output heads designed for different tasks, in which a classified head is employed to make offloading decisions with integer variables while a fitting head is applied to solve resource allocation with continuous variables. Second, a progressive scheduler is introduced to adapt the agent to the varying number of UAVs by progressively adjusting a part of neurons in the agent. This structure can naturally accumulate experiences and be immune to catastrophic forgetting. Finally, a light taboo search (LTS) is introduced to enhance the global search of the FRES. The numerical results demonstrate the superiority of the FRES framework, which can make real-time and optimal resource scheduling even in dynamic MEC systems.

Designing a Mixed Multilayer Wavelet Neural Network for Solving ERI Inversion Problem With Massive Amounts of Data: A Hybrid STGWO-GD Learning Approach.

This study aims to develop a novel wavelet neural-network (WNN) model for solving electrical resistivity imaging (ERI) inversion with massive amounts of measured data in control and measurement fields. In the proposed method, we design a mixed multilayer WNN (MMWNN) which uses Morlet and Mexican wavelons as different activation functions in a cascaded hidden layer structure. Meanwhile, a hybrid STGWO-GD learning approach is used to improve the learning ability of the MMWNN, which is a combination of the self-tuning grey wolf optimizer (STGWO) and the gradient descent (GD) algorithm adopting the advantages of each other. Moreover, updating formulas of the GD algorithm are derived, and a Gaussian updating operator with weighted hierarchical hunting, a chaotic oscillation equation, and a nonlinear modulation coefficient are introduced to improve the hierarchical hunting and the control parameter adjustment of the modified STGWO. Five examples are used with the aim of assessing the availability and feasibility of the proposed inversion method. The inversion results are promising and show that the introduced method is superior to other competitors in terms of inversion accuracy and computational efficiency. Furthermore, the effectiveness of the proposed method is demonstrated over a classical benchmark successfully.

Electrical resistivity imaging inversion: An ISFLA trained kernel principal component wavelet neural network approach.

The traditional artificial neural network (ANN) inversion of electrical resistivity imaging (ERI) based on gradient descent algorithm is known to be inept for its low computation efficiency and does not ensure global convergence. In order to solve above problems, a kernel principal component wavelet neural network (KPCWNN) trained by an improved shuffled frog leaping algorithm (ISFLA) method is proposed in this study. An additional kernel principal component (KPC) layer is applied to reduce the dimensionality of apparent resistivity data and increase the computational efficiency of wavelet neural network (WNN). Meanwhile, a novel ISFLA algorithm is adopted for improving the learning ability and inversion quality of WNN. In the proposed ISFLA, a hybrid LC mutation attractor is used to enhance the exploitation ability and a differential updating rule is used to enhance the exploration ability. Four groups of experiments are considered to demonstrate the feasibility of the proposed inversion method. The inversion results of the synthetic and field examples show that the introduced method is superior to other algorithms in terms of prediction accuracy and computational efficiency, which contribute to better inversion results.