scholarly journals Analog Beamforming in Millimeter Wave MIMO Systems

2020 ◽  
Vol 9 (8) ◽  
pp. 729-736
Keyword(s):  
Mimo Systems ◽  
Weighted Sum ◽  
Robust Beamforming ◽  
Multi Objective ◽  
Weighted Sum Method ◽  
Beam Selection ◽  
Channel Information ◽  
Constant Magnitude ◽  
User Interference ◽  
Constraint Method

In traditional analog beamforming schemes, like the beam selection method, use the strongest path array steering vector of the channel to generate a beam pointing to the user. In multi-user systems, such schemes will result in the large interference among the users, especially when the users are closely located. In this paper, we designed an analog beamforming scheme for downlink mm-wave multi-user systems to enhance the beamforming gain and suppress the inter-user interference at the same time. A multi-objective problem is developed to beat a balance between the inter-user interference and the beamforming gain. To solve the problem, we firstly use the weighted-sum method and then 𝜺 -constraint method to transform the multi-objective problem into a single-objective problem. Then, the analog beamforming is made tractable with the constant-magnitude constraints with the use of semidefinite programing technique. Adding to these, the robust beamforming is designed to mitigate the effects of the channel estimation and to provide the robustness against the imperfect channel information. The simulation results shows that the 𝜺 -constraint method outperforms when compared with the weighted-sum method at high SNR’s for the robust multi-user analog beamforming

Study on Multi-Objective Reconstruction of Random Media

2016 ◽  
Vol 825 ◽  
pp. 153-160
Author(s):  
Adéla Hlobilová ◽  
Matěj Lepš
Keyword(s):  
Genetic Algorithm ◽  
Random Media ◽  
Probability Function ◽  
Volume Fraction ◽  
Weighted Sum ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Weighted Sum Method ◽  
Original Medium ◽  
Optimization Routine

This paper deals with a reconstruction of random media via multi-objective optimization. Two statistical descriptors, namely a two-point probability function and a two-point lineal path function, are repetitively evaluated for the original medium and the reconstructed image to appreciate the improvement in the optimization progress. Because of doubts of the weights setting in the weighted-sum method, purely multi-objective optimization routine Non-dominated Sorting Genetic Algorithm~II is utilized. Three operators are compared for creating new offspring populations that satisfy a prescribed volume fraction constraint. The main contribution is in the testing of the proposed methodology on several benchmark images.

Application of the Headstock of CNC Boring Machine for Tractor Engine Cylinder Block Based on Multi-Objective Genetic Algorithm

2021 ◽  
Vol 37 (2) ◽  
pp. 343-349
Author(s):  
Yahui   Wang ◽  
Ling   Shi ◽  
Yiqi   Dang ◽  
Shengkai   Sun ◽  
Huipeng   Zhang
Keyword(s):  
Genetic Algorithm ◽  
Numerical Control ◽  
Gear Train ◽  
List Type ◽  
Weighted Sum ◽  
Cnc Machine ◽  
Nsga Ii ◽  
Multi Objective ◽  
Weighted Sum Method ◽  
Tooth Width

HighlightsThe headstock of the single-sided horizontal CNC boring machine specializing in processing tractor 6-cylinder engine cylinders is optimized.The constraint conditions such as tooth width and modulus are constructed. The model is optimized by the NSGA algorithm, and the optimization results are good.The optimization results of the NSGA algorithm are compared with the results of the weighted sum method and the GA, which highlights the superiority of the NSGA algorithm.ABSTRACT. The tractor is one of the most frequently used equipment in agricultural production, and its mass production is the general trend. With the continuous advancement of the global industrialization process, the importance of Computer Numerical Control (CNC) machine in the entire industrial production has become more and more prominent, and the application of CNC machine in tractor manufacturing has greatly improved production efficiency. This article takes the headstock of a single-sided horizontal CNC boring machine dedicated to processing tractor 6-cylinder engine cylinders as the research objective, takes the key parameters of the gear train in the headstock as the optimization design variables, constructs constraints, such as modulus, tooth width, etc., establishes a multi-objective optimization mathematical model, uses the Non-dominated Sorting Genetic Algorithm II (NSGA-II) to process the model and obtains the Pareto solution set through multiple iterations. The optimization results show that the volume, center distance and the reciprocal of coincidence degree of the main shaft 1 transmission group are reduced in varying degrees. Finally, it is compared with the weighted sum method and genetic algorithm (GA) to highlight the superiority of NSGA-II. Keywords: Headstock, Multi-optimization, Non-dominated Sorting Genetic Algorithm II, Tractor cylinder.

scholarly journals An Integrated Multi-Criteria and Multi-Objective Optimization Approach for Establishing the Transport Plan of Intercity Trains

2020 ◽  
Vol 12 (2) ◽  
pp. 687 ◽  
Author(s):  
Svetla Stoilova
Keyword(s):  
Objective Function ◽  
Optimal Transport ◽  
Hybrid Methods ◽  
Optimization Approach ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Weighted Sum Method ◽  
Transport Plan ◽  
The Impact ◽  
Constraint Method

The development of the transport plan must take into account various criteria impacting the transport process. The main objective of the study is to propose an integrated approach to determine the transport plan of passenger trains. The methodology consists of five steps. In the first step, the criteria for optimization of the transport plan were defined. In the second step, variants of the transport plan were formulated. In the third step, the weights of the criteria are determined by applying the step-wise weight assessment ratio analysis method (SWARA) multi-criteria method. The multi-objective optimization was conducted in the fourth step. The following multi-objective optimization approaches were used and compared: weighted sum method (WSM), compromise programming method (CP), and the epsilon–constraint method (EC). The study proposes a modified epsilon–constraint method (MEC) by applying normalization of each objective function according to the maximal value of the solution by individual optimization for each objective function, and hybrid methods: hybrid WSM and EC, hybrid WSM and MEC, hybrid CP and EC, and Hybrid CP and MEC. The impact of the variation of passenger flows on the choice of an optimal transport plan was studied in the fifth step. The Laplace’s criterion, Hurwitz’s criterion, and Savage’s criterion were applied to come to a decision. The approbation of the methodology was demonstrated through the case study of Bulgaria’s railway network. Suitable variant of transport plan is proposed.

scholarly journals Goal-Programming-Based Multi-Objective Optimization in Off-Grid Microgrids

2020 ◽  
Vol 12 (19) ◽  
pp. 8119
Author(s):  
Akhtar Hussain ◽  
Hak-Man Kim
Keyword(s):  
Energy Storage ◽  
Goal Programming ◽  
Optimization Problems ◽  
Renewable Energy Sources ◽  
Storage System ◽  
Weighted Sum ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Weighted Sum Method ◽  
The Impact

Renewable-based off-grid microgrids are considered as a potential solution for providing electricity to rural and remote communities in an environment-friendly manner. In such systems, energy storage is commonly utilized to cope with the intermittent nature of renewable energy sources. However, frequent usage may result in the fast degradation of energy storage elements. Therefore, a goal-programming-based multi-objective optimization problem has been developed in this study, which considers both the energy storage system (battery and electric vehicle) degradation and the curtailment of loads and renewables. Initially, goals are set for each of the parameters and the objective of the developed model is to minimize the deviations from those set goals. Degradation of battery and electric vehicles is quantified using deep discharging, overcharging, and cycling frequency during the operation horizon. The developed model is solved using two of the well-known approaches used for solving multi-optimization problems, the weighted-sum approach and the priority approach. Five cases are simulated for each of the methods by varying weight/priority of different objectives. Besides this, the impact of weight and priority values selected by policymakers is also analyzed. Simulation results have shown the superiority of the weighted-sum method over the priority method in solving the formulated problem.

scholarly journals Multi-Objective Factors Optimization in Fused Deposition Modelling with Particle Swarm Optimization and Differential Evolution

2021 ◽  
Author(s):  
Mohamed Arezki Mellal ◽  
Chahinaze Laifaoui ◽  
Fahima Ghezal ◽  
Edward J. Williams
Keyword(s):  
Particle Swarm Optimization ◽  
Differential Evolution ◽  
Particle Swarm ◽  
Fused Deposition Modelling ◽  
Weighted Sum ◽  
Swarm Optimization ◽  
Multi Objective ◽  
Fused Deposition ◽  
Weighted Sum Method ◽  
Nature Inspired Computing

Abstract The design of any system contemplates the elaboration of a prototype of the entire system or some parts, before the manufacturing phase. Nowadays, rapid prototyping (RP) is widely used by the designers. This paper addresses the multi-objective factors optimization of the fused deposition modelling (FDM) technology. The problem is converted into a single one using the weighted-sum method and then solved by resorting to two nature-inspired computing techniques, namely particle swarm optimization (PSO) and differential evolution (DE). The results obtained are compared.

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