Bayesian Regularization Algorithm Based Recurrent Neural Network Method and NSGA-II for the Optimal Design of the Reflector

The optical-mechanical system of a space camera is composed of several complex components, and the effects of several factors (weight, gravity, modal frequency, temperature, etc.) on its system performance need to be considered during ground tests, launch, and in-orbit operation. In order to meet the system specifications of the optical camera system, the dimensional parameters of the optical camera structure need to be optimized. There is a highly nonlinear functional relationship between the dimensional parameters of the optical machine structure and the design indexes. The traditional method takes a significant amount of time for finite element calculation and is less efficient. In order to improve the optimization efficiency, a recurrent neural network prediction model based on the Bayesian regularization algorithm is proposed in this paper, and the NSGA-II is used to globally optimize multiple prediction objectives of the prediction model. The reflector of the space camera is used as an example to predict the weight, first-order modal frequency, and gravitational mirror deformation root mean square of the reflector, and to complete the lightweight design. The results show that the prediction model established by BR-RNN-NSGA-II offers high prediction accuracy for the design indexes of the reflector, which all reach over 99.6%, and BR-RNN-NSGA-II can complete the multi-objective optimization search efficiently and accurately. This paper provides a new idea of optimization of optical machine structure, which enriches the theory of complex structure design.

Research on the Optimization of Cross-Border Logistics Paths of the “Belt and Road” in the Inland Regions

Taking logistics time, logistics cost, and carbon emissions as optimization objectives, air transportation is included in the cross-border logistics paths optimization of multimodal transportation. Considering the scale effect of transportation, a multiobjective optimization model of cross-border logistics paths including road, water, railway, and air is constructed. The problem of cross-border logistics paths along the “Belt and Road” regions for cities in inland is studied via the NSGA-II method. The research results show that Chengdu and Xi’an should bear a large number of cross-border air transportation and be constructed as the national airport-type logistics hub. The foreign destinations of cross-border air transportation are distributed in different regions, mainly in Eastern Europe and Eastern Central Europe. The optimization result shows that if there is a 1-fold increase in logistics cost, the logistics time can reduce by 1.37 folds after the cross-border air transportation joins in the model. Such a result has effectively guided the transition from cross-border water transportation to cross-border air transportation.

Multi-Objective Optimization of Low Reynolds Number Airfoil Using Convolutional Neural Network and Non-Dominated Sorting Genetic Algorithm

The airfoil is the prime component of flying vehicles. For low-speed flights, low Reynolds number airfoils are used. The characteristic of low Reynolds number airfoils is a laminar separation bubble and an associated drag rise. This paper presents a framework for the design of a low Reynolds number airfoil. The contributions of the proposed research are twofold. First, a convolutional neural network (CNN) is designed for the aerodynamic coefficient prediction of low Reynolds number airfoils. Data generation is discussed in detail and XFOIL is selected to obtain aerodynamic coefficients. The performance of the CNN is evaluated using different learning rate schedulers and adaptive learning rate optimizers. The trained model can predict the aerodynamic coefficients with high accuracy. Second, the trained model is used with a non-dominated sorting genetic algorithm (NSGA-II) for multi-objective optimization of the low Reynolds number airfoil at a specific angle of attack. A similar optimization is performed using NSGA-II directly calling XFOIL, to obtain the aerodynamic coefficients. The Pareto fronts of both optimizations are compared, and it is concluded that the proposed CNN can replicate the actual Pareto in considerably less time.

Multi-objective flower pollination algorithm applied to 5G vehicular networks communication

The Cellular Vehicle-to-Everything (C-V2X) technology, as a widest version of Vehicular Ad-hoc Network (VANET), aims to interconnect vehicles and any other latest technological infrastructures. In this context, the fifth generation of mobile networks (5G) based on millimeter waves (mmWave) is an excellent alternative for the implementation of vehicular networks, mainly because it is capable of providing high data rates (Gbps) and ultra-low latency, requirements of C-V2X. On the other hand, mmWave signals are highly susceptible to blocking, causing low quality of service (QoS) in VANETs, compromising network functionality and the safety of drivers and pedestrians. Thus, in this work evolutionary computing techniques are applied in the simulation of a 5G vehicular network based on millimeter waves, exploring Media Access Control (MAC) sublayer parameters to optimize packet loss, latency and throughput, in order to optimize inter-vehicular communication. The Multi-objective Flower Pollination Algorithm (MOFPA) was used for this purpose. The results obtained show that the adopted approach can reach results close to the optimal pareto of non-dominated solutions, with a 75% reduction in exploration time in relation to the exhaustive search process. Finally, the performance of the metaheuristics adopted is compared with the non-dominated genetic classification algorithm (NSGA-II) and the multi-objective differential evolutionary algorithm (MODE).

MODELING AND MULTI-RESPONSE OPTIMIZATION OF ABRASIVE WATER JET MACHINING USING ANN COUPLED WITH NSGA-II

This paper aims to develop a predictive model and optimize the performance of the abrasive water jet machining (AWJM) during machining of carbon fiber-reinforced plastic (CFRP) epoxy laminates composite through a unique approach of artificial neural network (ANN) linked with the nondominated sorting genetic algorithm-II (NSGA-II). Initially, 80 AWJM experimental runs were carried out to generate the data set to train and test the ANN model. During the experimentation, the stand-off distance (SOD), water pressure, traverse speed and abrasive mass flow rate (AMFR) were selected as input AWJM variables and the average surface roughness and kerf width were considered as response variables. The established ANN model predicted the response variable with mean square error of 0.0027. Finally, the ANN coupled NSGA-II algorithm was applied to determine the optimum AWJM input parameters combinations based on multiple objectives.

Optimization of the Process Parameters for Micro-Milling Thin-Walled Micro Parts Using Advanced Algorithms

The surface integrity and machining accuracy of thin-walled micro parts are significantly affected by micro-milling parameters mostly because of their weak stiffness. Furthermore, there is still a lack of studies focusing on parameters optimization for the fabrication of thin-walled microscale parts. In this paper, an innovative approach is proposed for the optimization of machining parameters with the objectives of surface quality and dimension accuracy, which integrates the Taguchi method, principal component analysis method (PCA) and the Non-dominated sorting genetic algorithm (NSGA-II). In the study, surface arithmetic average height Sa, surface root mean square height Sq, and 3-D fractal dimension Ds are selected to evaluate surface quality. Then micro-milling experiments are conducted based on the Taguchi method. According to the experimental results, the significance of machining parameters can be determined by range analysis. Besides, regression models for the responses are developed comparatively, and the PCA method is employed for dimension reduction of the optimization objective space. Finally, two combinations of machining parameters with the highest satisfaction are obtained through NSGA-II, and verification experiments are carried out. The results show that the surface quality and dimension accuracy of the thin-walled microscale parts can be simultaneously improved by using the proposed approach.