Layout optimization of large-scale oil–gas gathering system based on combined optimization strategy

2019 ◽  
Vol 332 ◽  
pp. 159-183 ◽  
Author(s):  
Yang Liu ◽  
Shuangqing Chen ◽  
Bing Guan ◽  
Ping Xu
Keyword(s):  
Large Scale ◽  
Layout Optimization ◽  
Optimization Strategy ◽  
Oil Gas

scholarly journals Suitability Evaluation and Layout Optimization of the Spatial Distribution of Rural Residential Areas

2020 ◽  
Vol 12 (6) ◽  
pp. 2409 ◽  
Author(s):  
Pengfei Guo ◽  
Fangfang Zhang ◽  
Haiying Wang ◽  
Fen Qin
Keyword(s):  
Spatial Distribution ◽  
Large Scale ◽  
Layout Optimization ◽  
Optimization Strategy ◽  
Rural Settlements ◽  
Residential Areas ◽  
Study Results ◽  
Administrative Area ◽  
Elevation Model ◽  
Resistance Value

A reasonable layout optimization strategy of rural residential areas can improve the quality of life of rural residents and promote rural revitalization. Evaluating the suitability of rural residential areas is the basis of layout optimization. Based on 1:100,000 land cover data and a digital elevation model (30 m) for the Henan Province, China, we used the minimum cumulative resistance model to evaluate the spatial distribution suitability of rural settlements in the Zhengzhou administrative area (abbreviated: Zhengzhou). Then, we used a weighted Voronoi diagram to determine the scope of influence of central villages and determined the direction of relocation for the “combined migration” rural residential areas. The study results support the following conclusions: (1) the comprehensive resistance value of rural residential areas in the Northeastern part of Zhengzhou is low and the suitability is high. However, the comprehensive resistance value of the Southwestern part is high and the suitability is low. (2) The study area can be divided into highly suitable areas, suitable areas, generally suitable areas, unsuitable areas, and extremely unsuitable areas. Unsuitable areas and extremely unsuitable areas accounted for 33.66% of the total area and included 662 rural residential areas. (3) The rural residential areas were divided into four types of optimization: urbanization, key development, controlled development, and combined migration. Based on an analysis of the characteristics of each type of rural residential area, we proposed corresponding optimization strategies. The results remedy the lack of layout optimization strategies for large-scale rural residential areas and can provide support for the optimization of the layout of rural residential areas in Zhengzhou. Furthermore, the research techniques may apply to other regions.

scholarly journals Two-stage multi-tasking transform framework for large-scale many-objective optimization problems

2021 ◽  
Author(s):  
Lu Chen ◽  
Handing Wang ◽  
Wenping Ma
Keyword(s):  
Large Scale ◽  
Original Problem ◽  
Optimization Problems ◽  
Reference Points ◽  
Optimization Strategy ◽  
Two Stage ◽  
Decision Space ◽  
Second Stage ◽  
Decision Variables ◽  
Objective Space

AbstractReal-world optimization applications in complex systems always contain multiple factors to be optimized, which can be formulated as multi-objective optimization problems. These problems have been solved by many evolutionary algorithms like MOEA/D, NSGA-III, and KnEA. However, when the numbers of decision variables and objectives increase, the computation costs of those mentioned algorithms will be unaffordable. To reduce such high computation cost on large-scale many-objective optimization problems, we proposed a two-stage framework. The first stage of the proposed algorithm combines with a multi-tasking optimization strategy and a bi-directional search strategy, where the original problem is reformulated as a multi-tasking optimization problem in the decision space to enhance the convergence. To improve the diversity, in the second stage, the proposed algorithm applies multi-tasking optimization to a number of sub-problems based on reference points in the objective space. In this paper, to show the effectiveness of the proposed algorithm, we test the algorithm on the DTLZ and LSMOP problems and compare it with existing algorithms, and it outperforms other compared algorithms in most cases and shows disadvantage on both convergence and diversity.

scholarly journals HOLISTIC PARAMETRIC RECONSTRUCTION OF BUILDING MODELS FROM POINT CLOUDS

2020 ◽  
Vol XLIII-B2-2020 ◽  
pp. 689-695
Author(s):  
Z. Li ◽  
W. Zhang ◽  
J. Shan
Keyword(s):  
Large Scale ◽  
Traditional Approach ◽  
Holistic Approach ◽  
Point Clouds ◽  
Airborne Lidar ◽  
Reconstruction Method ◽  
Optimization Strategy ◽  
Building Roof ◽  
Building Model ◽  
Building Models

Abstract. Building models are conventionally reconstructed by building roof points via planar segmentation and then using a topology graph to group the planes together. Roof edges and vertices are then mathematically represented by intersecting segmented planes. Technically, such solution is based on sequential local fitting, i.e., the entire data of one building are not simultaneously participating in determining the building model. As a consequence, the solution is lack of topological integrity and geometric rigor. Fundamentally different from this traditional approach, we propose a holistic parametric reconstruction method which means taking into consideration the entire point clouds of one building simultaneously. In our work, building models are reconstructed from predefined parametric (roof) primitives. We first use a well-designed deep neural network to segment and identify primitives in the given building point clouds. A holistic optimization strategy is then introduced to simultaneously determine the parameters of a segmented primitive. In the last step, the optimal parameters are used to generate a watertight building model in CityGML format. The airborne LiDAR dataset RoofN3D with predefined roof types is used for our test. It is shown that PointNet++ applied to the entire dataset can achieve an accuracy of 83% for primitive classification. For a subset of 910 buildings in RoofN3D, the holistic approach is then used to determine the parameters of primitives and reconstruct the buildings. The achieved overall quality of reconstruction is 0.08 meters for point-surface-distance or 0.7 times RMSE of the input LiDAR points. This study demonstrates the efficiency and capability of the proposed approach and its potential to handle large scale urban point clouds.

Collector System Layout Optimization Framework for Large-Scale Offshore Wind Farms

2016 ◽  
Vol 7 (4) ◽  
pp. 1398-1407 ◽  
Author(s):  
Yingying Chen ◽  
Zhao Yang Dong ◽  
Ke Meng ◽  
Feng ji Luo ◽  
Zhao Xu ◽  
...  
Keyword(s):  
Large Scale ◽  
Wind Farms ◽  
Layout Optimization ◽  
Offshore Wind ◽  
Offshore Wind Farms ◽  
Optimization Framework ◽  
Collector System ◽  
System Layout

scholarly journals A Joint Optimization Strategy of Coverage Planning and Energy Scheduling for Wireless Rechargeable Sensor Networks

Processes ◽  
2020 ◽  
Vol 8 (10) ◽  
pp. 1324
Author(s):  
Cheng Gong ◽  
Chao Guo ◽  
Haitao Xu ◽  
Chengcheng Zhou ◽  
Xiaotao Yuan
Keyword(s):  
Sensor Networks ◽  
Communication Networks ◽  
Energy Supply ◽  
Large Scale ◽  
Joint Optimization ◽  
Second Phase ◽  
Optimization Strategy ◽  
Two Phase ◽  
Game Equilibrium ◽  
Wireless Rechargeable Sensor Networks

Wireless Sensor Networks (WSNs) have the characteristics of large-scale deployment, flexible networking, and many applications. They are important parts of wireless communication networks. However, due to limited energy supply, the development of WSNs is greatly restricted. Wireless rechargeable sensor networks (WRSNs) transform the distributed energy around the environment into usable electricity through energy collection technology. In this work, a two-phase scheme is proposed to improve the energy management efficiency for WRSNs. In the first phase, we designed an annulus virtual force based particle swarm optimization (AVFPSO) algorithm for area coverage. It adopts the multi-parameter joint optimization method to improve the efficiency of the algorithm. In the second phase, a queuing game-based energy supply (QGES) algorithm was designed. It converts energy supply and consumption into network service. By solving the game equilibrium of the model, the optimal energy distribution strategy can be obtained. The simulation results show that our scheme improves the efficiency of coverage and energy supply, and then extends the lifetime of WSN.

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