Neighborhood Size Research Articles

Improving neighborhood exploration into MOEA/D framework to solve a bi‐objective routing problem

AbstractLocal search (LS) algorithms are efficient metaheuristics to solve combinatorial problems. The performance of LS highly depends on the neighborhood exploration of solutions. Many methods have been developed over the years to improve the efficiency of LS on different problems of operations research. In particular, the exploration strategy of the neighborhood and the exclusion of irrelevant neighboring solutions are design mechanisms that have to be carefully considered when tackling NP‐hard optimization problems. An MOEA/D framework including an LS‐based mutation and knowledge discovery mechanisms is the core algorithm used to solve a bi‐objective vehicle routing problem with time windows (bVRPTW) where the total traveling cost and the total waiting time of drivers have to be minimized. We enhance the classical LS exploration strategy of the neighborhood from the literature of scheduling and propose new metrics based on customer distances and waiting times to reduce the neighborhood size. We conduct a deep analysis of the parameters to give a fine tuning of the MOEA/D framework adapted to the LS variants and to the bVRPTW. Experiments show that the proposed neighborhood strategies lead to better performance on both Solomon's and Gehring and Homberger's benchmarks.

A neighborhood search integer programming approach for wind farm layout optimization

Abstract. Two models and a heuristic algorithm to address the wind farm layout optimization problem are presented. The models are linear integer programming formulations where candidate locations of wind turbines are described by binary variables. One formulation considers an approximation of the power curve by means of a stepwise constant function. The other model is based on a power-curve-free model where minimization of a measure closely related to total wind speed deficit is optimized. A special-purpose neighborhood search heuristic wraps these formulations with increasing tractability and effectiveness compared to the full model that is not contained in the heuristic. The heuristic iteratively searches for neighborhoods around the incumbent using a branch-and-cut algorithm. The number of candidate locations and neighborhood sizes are adjusted adaptively. Numerical results on a set of publicly available benchmark problems indicate that a proxy for total wind speed deficit as an objective is a functional approach, since high-quality solutions of the metric of annual energy production are obtained when using the latter function as an substitute objective. Furthermore, the proposed heuristic is able to provide good results compared to a large set of distinctive approaches that consider the turbine positions as continuous variables.

Estimation of change with partially overlapping and spatially balanced samples

AbstractSpatially balanced samples are samples that are well‐spread in some available auxiliary variables. Selecting such samples has been proven to be very efficient in estimation of the current state (total or mean) of target variables related to the auxiliary variables. As time goes, or when new auxiliary variables become available, such samples need to be updated to stay well‐spread and produce good estimates of the current state. In such an update, we want to keep some overlap between successive samples to improve the estimation of change. With this approach, we end up with partially overlapping and spatially balanced samples. To estimate the variance of an estimator of change, we need to be able to estimate the covariance between successive estimators of the current state. We introduce an approximate estimator of such covariance based on local means. By simulation studies, we show that the proposed estimator can reduce the bias compared to a commonly used estimator. Also, the new estimator tends to become less biased when reducing the local neighborhood size.

Econometric inference on a large Bayesian game with heterogeneous beliefs

Econometric models of strategic interactions among people or firms have received a great deal of attention in the literature. Less attention has been paid to the role of the underlying assumptions about the way agents form beliefs about other agents. We focus on a single large Bayesian game with idiosyncratic strategic neighborhoods and develop an approach of empirical modeling that relaxes the assumption of rational expectations and allows the players to form beliefs differently. By drawing on the main intuition of Kalai (2004), we introduce the notion of hindsight regret, which measures each player’s ex-post value of other players’ type information, and obtain the belief-free bound for the hindsight regret. Using this bound, we derive testable implications and develop a bootstrap inference procedure for the structural parameters. Our inference method is uniformly valid regardless of the size of strategic neighborhoods and tends to exhibit high power when the neighborhoods are large. We demonstrate the finite sample performance of the method through Monte Carlo simulations.

Mathematical model and adaptive simulated annealing algorithm for mixed-model assembly job-shop scheduling with lot streaming

Mixed-model assembly and lot streaming are two key technologies to improve efficiency and reduce setups in a multi-variety and small-batch discrete manufacturing environment. Yet few studies consider the two technologies in assembly job-shop scheduling problems. Thus, this paper investigates the mixed-model assembly job-shop scheduling problem with lot streaming, which contains two closely coordinated stages with differentiated production lot size: the processing stage with lot streaming and the mixed-model assembly stage with single-piece flow. To tackle this problem, a mathematical model including new constraints i.e. production sequence constraints within/among sublots, sequence-dependent setup constraints between sublots and mixed-model assembly constraints, is developed to establish the temporal and spatial links between the processing and assembly stages. Besides, an adaptive simulated annealing algorithm is proposed. In this algorithm, a sublot sequence rule considering sublot and assembly features is discovered via gene expression programming to promote the performance of initial solutions; a Q-learning-based temperature control mechanism is designed to adaptively adjust the acceptance probability of inferior solutions and accordingly avoid local optima; an adaptive selection mechanism of neighborhood operators is designed to increase the chance of finding potential solutions; an adaptive adjustment mechanism of neighborhood size is put forward to balance computing resources and the coverage of neighborhood space. Experimental results show that both the discovered rule and the adaptive mechanisms are effective, and the developed algorithm is more adapt to fix the studied problem than other state-of-the-art algorithms.

Using logistic regression-cellular automata to project future sites for commercial wind energy development

Demands to decarbonize electricity production and improve energy security will continue to drive wind energy development. A Logistic Regression-Cellular Automata (LRCA) model is presented here to project timing and location of this future development and thus aid efforts to meet energy demands. The model's logistic regression equation is trained and tested using aggregated data from key predictors to correctly classify hexagonal grid cells covering the Conterminous United States (CONUS) as currently containing wind farms. The cellular automata component iteratively applies this equation, plus constraints and neighborhood effects, to project grid cells suited for future wind energy development out to the year 2050, with the model's sensitivity to constraint, neighborhood, and scenario definitions also examined. Projected wind farms are concentrated in high wind speed regions currently populated by wind farms (e.g., Central Plains, Midwest). State-level scale projections reveal local influences on future development, such as critical species habitats and infrastructure. Projected wind farm locations are trustworthy since the model correctly classifies over 85% of current grid cell states at CONUS and state-level scales. Current clusters of wind energy development across the CONUS will continue to expand in these projections, with these clusters growing earlier and faster given a larger neighborhood size and looser constraints. Model projections are less sensitive to scenario definitions, with modifications to predictors affecting when existing wind farm clusters expand rather than where new clusters form. Replacement of the wind farm location dataset would allow this model to project other decentralized land use changes, particularly solar energy development.

Effects of House Flips on Local Housing Transactions

This study explores the possible impacts of short-term house trades, often called “house flips,” on local housing markets. Using nationally representative data across multiple U.S. housing markets during 2000–2013, we test the Spillover Hypothesis and the alternative Competition Hypothesis for the influences of flips on the neighborhood non-flip house transactions. We find that the local flip tendency, our measurement for local flip activity, is negatively associated with the holding durations of neighborhood houses experiencing non-flip resales, and positively associated with their resale prices, after we control for market conditions that affect both flip and non-flip transactions as well as other potential determinants for holding duration and price. The effects are generally more prominent when the local flip tendency is measured for a longer run, supporting the Spillover Hypothesis. These effects are also persistent regardless of whether the local flip tendency is defined as the proportion of flips in the local resales by transaction number or by transaction volume, or whether it is measured continuously or discretely. The results also hold when we endogenize the local flip tendency, or when we change the neighborhood size. These findings demonstrate the substantial influences of flip activities to the local housing market turnover and price movements.

Inpainting in Discrete Sobolev Spaces: Structural Information for Uncertainty Reduction

In this article, we introduce a new mathematical functional whose minimization determines the quality of the solution for the exemplar-based inpainting-by-patch problem. The new functional expression includes finite difference terms in a similar fashion to what happens in the theoretical Sobolev spaces: its use reduces the uncertainty in the choice of the most suitable values for each point to inpaint. Moreover, we introduce a probabilistic model by which we prove that the usual principal directions, generally employed for continuous problems, are not enough to achieve consistent reconstructions in the discrete inpainting asset. Finally, we formalize a new priority index and new rules for its dynamic update. The quality of the reconstructions, achieved using a reduced neighborhood size of more than 95% with respect to the current state-of-the-art algorithms based on the same inpainting approach, further provides the experimental validation of the method.

RAN-GNNs: Breaking the Capacity Limits of Graph Neural Networks.

Graph neural networks (GNNs) have become a staple in problems addressing learning and analysis of data defined over graphs. However, several results suggest an inherent difficulty in extracting better performance by increasing the number of layers. Recent works attribute this to a phenomenon peculiar to the extraction of node features in graph-based tasks, i.e., the need to consider multiple neighborhood sizes at the same time and adaptively tune them. In this article, we investigate the recently proposed randomly wired architectures in the context of GNNs. Instead of building deeper networks by stacking many layers, we prove that employing a randomly wired architecture can be a more effective way to increase the capacity of the network and obtain richer representations. We show that such architectures behave like an ensemble of paths, which are able to merge contributions from receptive fields of varied size. Moreover, these receptive fields can also be modulated to be wider or narrower through the trainable weights over the paths. We also provide extensive experimental evidence of the superior performance of randomly wired architectures over multiple tasks and five graph convolution definitions, using recent benchmarking frameworks that address the reliability of previous testing methodologies.

Satisfaction with urban trees associates with tree canopy cover and tree visibility around the home

Many world cities want to expand the number of urban trees. How this expansion occurs should consider what people expect from trees based on how they experience and perceive these trees. Therefore, we need a better understanding of how people perceptually respond to urban tree abundance. This research examined whether people’s satisfaction with urban trees and satisfaction with the management of those trees were related to objective measures of greenery such as the Normalized Difference Vegetation Index (NDVI), percent tree canopy cover, and the Viewshed Greenness Visibility Index (VGVI) for trees. We used a demographically and geographically representative survey of 223 residents in Toronto, Canada, and calculated NDVI, canopy cover, and VGVI at three neighbourhood sizes. We analysed the data using generalized linear regression. We found that canopy cover and VGVI had a positive association with satisfaction with urban trees. The associations were comparatively stronger at larger neighbourhood scales than at smaller scales. There were no statistically significant associations with NDVI or satisfaction with the management of urban trees.

A parallel particle swarm optimization framework based on a fork-join thread pool using a work-stealing mechanism

Particle Swarm Optimization (PSO) is one of the most popular optimization algorithms that has been adopted in various fields, including design, scheduling, and biochemistry. However, the algorithm is time-consuming when facing high-dimensional or multi-objective optimizing problems. Parallel PSO is thus proposed to improve its computing efficiency, and many studies have been conducted. However, the low-level system design is seldom considered in parallel programming, which may have a nonnegligible impact on computing efficiency, creating a gap between low-level optimization and high-level algorithm design. Therefore, this paper proposes a Parallel Asynchronous PSO (PAPSO) framework based on thread pools utilizing multicore processors and adopts a cross-level approach to bridge the gap. A series of experiments are designed and conducted to examine how the aforementioned method can improve the parallel execution efficiency of PSO compared with the OpenMP framework and nonparallel PSO. Results indicate that PAPSO can significantly improve PSO computing efficiency by reducing the elapsed time, approaching approximately 20% optimization compared with OpenMP. Additionally, it achieves an approximately linear speedup of up to 4.5 threads. In addition, the particle communication experiment shows that the nonblocking communication protocol is effective for maintaining the computing elapsed time in the same level facing different neighborhood sizes. Finally, the work-stealing mechanism achieves an average of 16% improvement for general computing scenarios and maintain up to 16% improvement for imbalanced workload scenarios. Generally, the major contribution of this paper is that we innovate the thread pooling management concept with the fork-join model in parallelizing PSO to make sufficient use of multiple core CPUs for parallel computing through multithread programming.

Determination of Surface Roughness of the Land Texture Using Remote Sensing Data and Geo-Informatic Techniques: A Case Study in Kurdistan Region-Iraq

Surface roughness (SR) is a significant geomorphic factor that has been effectively applied to determine material characteristics, present and past processes, and the period passed by since development in the environmental and earth sciences. Thus, this type of study is essential for analyzing SR and its importance. The research efforts to display a coherent evaluation of techniques and remote sensing data. The digital elevation models (DEMs) data were taken into account for quantifying the SR of landscape in different scales in selected areas that belong to the Kurdistan region, north of Iraq. The various types of DEMs {ALOS PALSAR (12.5 m) and SRTM in (30 m) and (90 m) resolution} as remotely sensed data integrated with GIS techniques were used to calculate SR regarding different neighborhood sizes {51 × 51 and 100 × 100 cells moving window size (MWZ)} in the both selected areas. The first study area is a mountainous terrain characterized by a number of anticlines and synclines that are located in High Folded Zones (HFZ), while the second study area is virtually a plain that is located in Low Folded Zones (LFZ). It found that SR calculation in wide and mountainous areas can be efficiently analyzed in more detail by using coarse-resolution DEMs from large MWZ, such as 100 × 100 cells, whereas high-resolution DEMs can be considered for studying SR in small, urban, and plain areas to reach detail information regarding smaller MWZ. The results show that the E and W segments of Safeen and the western segment of Shakrok anticlines have the highest SR values, which indicate high incision at elevated and extremely eroded parts of area one, although Bani Bawi, Pirmam, and Khatibian anticlines have moderate to low SR values due poorly incised. On the other hand, the areas within LFZ are mainly flat and have the lowest values of SR, which indicate a very poorly dissected landscape except for the northwest part of the area two relatively has a higher SR value.

Real-time detection of street tree crowns using mobile laser scanning based on pointwise classification

For targeted spraying in urban streets using mobile laser scanning (MLS), it is crucial to continuously detect tree crowns in real time from MLS data collected online. Crown detection based on a pointwise classification method, which predicts whether a point belongs to a crown using a binary classifier composed of a set of local features extracted from the neighbourhood of the point, is suitable for online data processing. However, the computational complexity of pointwise detection methods needs to be reduced to meet the real-time requirements of online targeted spraying. This paper proposes a real-time detection method for street tree crowns using MLS based on pointwise classification. A cubic neighbourhood is adopted from which a set of statistical features are extracted. A coarse-to-fine neighbourhood search method based on an MLS data grid storage structure is proposed to quickly query the neighbours. The classification performance and computational time of single features are evaluated. Then, the features are ranked using the technique for order preference by similarity to the ideal solution (TOPSIS). Finally, a subset of features that are easy to calculate and have high discrimination are selected and fused into a crown detector using a random forest algorithm. During online detection, the point cloud is sampled in terms of the vehicle movement and LiDAR measurement before being detected. An MLS point cloud collected from a 451 m urban street is used in the experiments. When the neighbourhood size is set to 0.3 m, the detection time per scanline is 24.84 ms on the unsampled test set with an F1 score of 0.9927. When the test set is sampled with a total sampling rate of 40, the detection time is reduced to 1% with an F1 score of 0.9906. The experimental results show that the proposed method can provide real-time and accurate detection of street tree crowns for online targeted spraying.

Optimum k-Nearest Neighbors for Heading Synchronization on a Swarm of UAVs under a Time-Evolving Communication Network.

Heading synchronization is fundamental in flocking behaviors. If a swarm of unmanned aerial vehicles (UAVs) can exhibit this behavior, the group can establish a common navigation route. Inspired by flocks in nature, the k-nearest neighbors algorithm modifies the behavior of a group member based on the k closest teammates. This algorithm produces a time-evolving communication network, due to the continuous displacement of the drones. Nevertheless, this is a computationally expensive algorithm, especially for large groups. This paper contains a statistical analysis to determine an optimal neighborhood size for a swarm of up to 100 UAVs, that seeks heading synchronization using a simple P-like control algorithm, in order to reduce the calculations on every UAV, this is especially important if it is intended to be implemented in drones with limited capabilities, as in swarm robotics. Based on the literature of bird flocks, that establishes that the neighborhood of every bird is fixed around seven teammates, two approaches are treated in this work: (i) the analysis of the optimum percentage of neighbors from a 100-UAV swarm, that is necessary to achieve heading synchronization, and (ii) the analysis to determine if the problem is solved in swarms of different sizes, up to 100 UAVs, while maintaining seven nearest neighbors among the members of the group. Simulation results and a statistical analysis, support the idea that the simple control algorithm behaves like a flock of starlings.

COMPARATIVE STUDY OF ROAD AND URBAN OBJECT CLASSIFICATION BASED ON MOBILE LASER SCANNERS

Abstract. Recently, the rapid development of new laser technologies has led to the continuous evolution of mobile laser systems, resulting in even greater capabilities for transport infrastructure. However, the market offers numerous MLS systems with varying specifications for global navigation satellite systems (GNSS), inertial measurement units (IMU), and laser scanners, which can result in different accuracies, resolutions, and densities. In this regard, this paper aims to compare two different MLS system, integrated with different GNSS and IMU for mapping in road and urban environments. The study evaluates the performance of these sensors using different classifiers and neighborhood sizes to determine which sensor produces better results. Random forest was found to be the most suitable classifier with an overall accuracy of (91.81% for Optech and 94.38% for Riegl) in road environment and (86.39% for Optech and 84.21% for Riegl) in urban environment. In terms of MLS, Optech achieved the highest accuracy in the road environment, while Riegl obtained the highest accuracy in the urban environment. This study provides valuable insights into the most effective MLS systems and approaches for accurate mapping in road and urban infrastructure.

A new boredom-aware dual-resource constrained flexible job shop scheduling problem using a two-stage multi-objective particle swarm optimization algorithm

Dual-resource constrained flexible job shop scheduling problem has become a hot research field in recent years. However, few studies have considered workers’ boredom sensations when allocating resources and scheduling tasks, leading to inappropriate task allocation and negative effects such as efficiency reductions and absenteeism. Therefore, a new boredom-aware dual-resource constrained flexible job shop scheduling problem is investigated in this study, which considers the increase in workers’ boredom caused by repetitive job assignments and constructs an efficiency function to characterize the impact of workers’ boredom. For this problem, a bi-level lexicographic model, which takes the effective completion of all manufacturing tasks as the primary optimization objective and higher worker job satisfaction as the secondary optimization objective, is established. A two-stage multi-objective particle swarm optimization algorithm with a three-dimensional representation scheme is presented to solve this model. In this algorithm, a new position-updating mechanism and a local search strategy are devised to effectively evolve the solution, and a decomposition strategy with a self-adaptive neighborhood size and a boundary exploration mechanism are embedded to obtain a uniformly distributed Pareto front. Experimental results confirm the superiority of the presented model and algorithm.

Reduction or enhancement? Repetition effects on early brain potentials during visual word recognition are frequency dependent.

Most studies on word repetition have demonstrated that repeated stimuli yield reductions in brain activity. Despite the well-known repetition reduction effect, some literature reports repetition enhancements in electroencephalogram (EEG) activities. However, although studies of object and face recognition have consistently demonstrated both repetition reduction and enhancement effects, the results of repetition enhancement effects were not consistent in studies of visual word recognition. Therefore, the present study aimed to further investigate the repetition effect on the P200, an early event-related potential (ERP) component that indexes the coactivation of lexical candidates during visual word recognition. To achieve a high signal-to-noise ratio, EEG signals were decomposed into various modes by using the Hilbert-Huang transform. Results demonstrated a repetition enhancement effect on P200 activity in alpha-band oscillation and that lexicality and orthographic neighborhood size would influence the magnitude of the repetition enhancement effect on P200. These findings suggest that alpha activity during visual word recognition might reflect the coactivation of orthographically similar words in the early stages of lexical processing. Meantime, there were repetition reduction effects on ERP activities in theta-delta band oscillation, which might index that the lateral inhibition between lexical candidates would be omitted in repetition.

MPED: Quantifying Point Cloud Distortion Based on Multiscale Potential Energy Discrepancy.

In this article, we propose a new distortion quantification method for point clouds, the multiscale potential energy discrepancy (MPED). Currently, there is a lack of effective distortion quantification for a variety of point cloud perception tasks. Specifically, in human vision tasks, a distortion quantification method is used to predict human subjective scores and optimize the selection of human perception task parameters, such as dense point cloud compression and enhancement. In machine vision tasks, a distortion quantification method usually serves as loss function to guide the training of deep neural networks for unsupervised learning tasks (e.g., sparse point cloud reconstruction, completion, and upsampling). Therefore, an effective distortion quantification should be differentiable, distortion discriminable, and have low computational complexity. However, current distortion quantification cannot satisfy all three conditions. To fill this gap, we propose a new point cloud feature description method, the point potential energy (PPE), inspired by classical physics. We regard the point clouds are systems that have potential energy and the distortion can change the total potential energy. By evaluating various neighborhood sizes, the proposed MPED achieves global-local tradeoffs, capturing distortion in a multiscale fashion. We further theoretically show that classical Chamfer distance is a special case of our MPED. Extensive experiments show that the proposed MPED is superior to current methods on both human and machine perception tasks. Our code is available at https://github.com/Qi-Yangsjtu/MPED.

Neighborhood-Based Hypergraph Core Decomposition

We propose neighborhood-based core decomposition : a novel way of decomposing hypergraphs into hierarchical neighborhood-cohesive subhypergraphs. Alternative approaches to decomposing hypergraphs, e.g., reduction to clique or bipartite graphs, are not meaningful in certain applications, the later also results in inefficient decomposition; while existing degree-based hypergraph decomposition does not distinguish nodes with different neighborhood sizes. Our case studies show that the proposed decomposition is more effective than degree and clique graph-based decompositions in disease intervention and in extracting provably approximate and application-wise meaningful densest subhypergraphs. We propose three algorithms: Peel, its efficient variant E-Peel, and a novel local algorithm: Local-core with parallel implementation. Our most efficient parallel algorithm Local-core(P) decomposes hypergraph with 27M nodes and 17M hyperedges in-memory within 91 seconds by adopting various optimizations. Finally, we develop a new hypergraph-core model, the (neighborhood, degree)-core by considering both neighborhood and degree constraints, design its decomposition algorithm Local-core+Peel, and demonstrate its superiority in spreading diffusion.

Geographically weighted regression to measure the role of intra-urban drivers for urban growth modelling in Kathmandu, Central Himalayas.

The present study analyzes spatio-temporal urban growth in Kathmandu Urban Agglomeration (KUA) and its impact on ecological infrastructures between 2000 and 2020. A geographically weighted regression (GWR) approach was used to investigate intra-urban drivers of urban growth between 2000-2010 (epoch 1) and 2010-2020 (epoch 2), and to predict built-up growth for the year 2030. The study makes crucial contributions to the urban growth modelling literature by (a) investigating six different neighborhood sizes (3 × 3 to 13 × 13) for computing proportion of built-up cells and (b) eliminating/selecting covariates using correlation check and globallasso regression. The superiority of GWR model over global regression model has been evaluated using the Akaike information criterion score and the stationarity test. The chronological urban growth analysis of KUA highlights rapid growth in mountainous landscape from 54.90 to 166 km2 during 2000-2020 and is projected to increase to 224.22 km2 during 2000-2030. This has significantly altered the forest-agriculture landscape during 2000-2020 (111.2 km2) and is expected to affect a large segment of ecological (blue-green) infrastructure (138 km2) by 2030. The findings of this study may be used for policy formulation, appropriate land use planning, and sustainable urban development.