Search Procedure Research Articles

A stochastic gradient method with variance control and variable learning rate for Deep Learning

In this paper we study a stochastic gradient algorithm which rules the increase of the mini-batch size in a predefined fashion and automatically adjusts the learning rate by means of a monotone or non-monotone line search procedure. The mini-batch size is incremented at a suitable a priori rate throughout the iterative process in order that the variance of the stochastic gradients is progressively reduced. The a priori rate is not subject to restrictive assumptions, allowing for the possibility of a slow increase in the mini-batch size. On the other hand, the learning rate can vary non-monotonically throughout the iterations, as long as it is appropriately bounded. Convergence results for the proposed method are provided for both convex and non-convex objective functions. Moreover it can be proved that the algorithm enjoys a global linear rate of convergence on strongly convex functions. The low per-iteration cost, the limited memory requirements and the robustness against the hyperparameters setting make the suggested approach well-suited for implementation within the deep learning framework, also for GPGPU-equipped architectures. Numerical results on training deep neural networks for multi-class image classification show a promising behaviour of the proposed scheme with respect to similar state of the art competitors.

An improved hybrid genetic algorithm using the affine combination-based reproduction

Genetic algorithms (GAs) are a type of search procedure that emulates the process of natural selection and genetics to solve complex optimization problems. The effectiveness of GAs in finding global solutions has led to their widespread applications in solving diverse optimization problems. However, their search performance and convergence not only depend highly on the operators used but are also sensitive to the choice of control parameters. This article presents an improved real-coded GA, called hybrid genetic algorithm (HGA), that employs affine combination-based reproduction and non-uniform mutation. The reproduction is a formula-based operator that helps to improve the convergence and introduce some degree of genetic diversity in the HGA. The non-uniform mutation helps to further maintain diversity within the population and prevent premature convergence to suboptimal solutions. The performance of the HGA can be affected by the parameters of its operators. The optimal settings of these parameters will be obtained to find the best performance through a set of simulation on 36 benchmark functions with diverse properties. Through the evaluation on a set of benchmark functions, it was found that the HGA outperforms the MATLAB ga and particleswarm (PSO) functions in terms of the offline performance.

Adaptive multibeam hopping in geo satellite networks with non-uniformly distributed ground users

This paper designs a novel low-complexity user-cluster grouping algorithm for adaptive beam hopping in geostationary satellite networks equipped with multibeam phased-array antennas. Each beam serves a cluster of users, and the challenge is to design a beam-hopping pattern where no beams are simultaneously serving nearby user clusters. We develop a line search procedure to identify near-optimum groupings for heterogeneous traffic demands. We provide a necessary condition to determine the boundaries of the line search space. Our approach employs exclusion regions around critical user clusters in congested areas, iterates a sequential congestion-based grouping algorithm, and applies a group-member-swapping procedure. It provides max-min fairness for ground users. Extensive numerical studies have shown that our user grouping algorithm produces near-optimum beam-hopping schedules with low outage probability. It achieves an improvement of up to 13dB in the worst-case signal-to-interference and noise ratio, and doubles the zero-outage data rate, compared to benchmark approaches.

Implementation of 5G New Radio Secondary Synchronization on FPGA

: 5th Generation New Radio (NR) cellular technology operates at low latency, and higher bandwidths, and makes use of a large number of antennas. NR communication is compatible with a wide range of machine- and human-centered applications. The cell search procedure plays a crucial role in establishing a synchronized connection with the base station (gNodeB) when the user equipment (UE) is enabled, or in the on state. The procedure for cell search finds the physical cell identity of the base station with primary synchronization and secondary synchronization for 3rd Generation Partnership Project (3GPP) 5G NR standards. This paper focuses on identifying the secondary synchronization of Physical Cell Identity (PCI) in the 3rd Generation Partnership Project (3GPP) 5G NR system. Field Programmable Gate Arrays are reprogrammable, flexible, reconfigurable computing circuits and simple to design at very peak frequencies. This suggested architecture employs Secondary Synchronization Signal detection and aims for low power consumption and high speed. The secondary synchronization block has been designed and executed on the Xilinx Zynq FPGA board. Index Terms: 5G New Radio, FPGA, Cell search, Physical Cell Identity, Primary and Secondary synchronization (SS).

Cross-link interference in the 5G cell search procedure: Observation and field experiments for mitigation

This paper presents field experiment results that illustrate the impact of cross-link interference on the cell search procedure in a 5G time division duplex system. We initially show how cross-link interference affects to the cell search procedure from an analytic aspect. An example of an experimental result provided in this paper shows how the user equipment wrongly estimates the symbol timing offset due to cross-link interference. Furthermore, we propose a modified cell search algorithm which is robust against the strong influence of cross-link interference. The proposed modification efficiently suppresses the effects of cross-link interference by applying selective energy normalization to the correlation process. The experimental results show that the modified algorithm can accurately estimate the symbol timing offset with a negligible increment in computation time.

An effective multi-level memetic search with neighborhood reduction for the clustered team orienteering problem

The Clustered Team Orienteering Problem (CluTOP) extends the classic Clustered Orienteering Problem by considering the use of multiple vehicles. The problem is known to be NP-hard and can be used to formulate many real-life applications. This work presents a highly effective multi-level memetic search for CluTOP that combines a backbone-based edge assembly crossover to generate promising offspring solutions with an effective bilevel synergistic local search procedure at both cluster and customer levels to improve offspring solutions. Other novel features of the proposed approach include a joint use of three specific hash functions to identify the tabu status of candidate solutions at the cluster level, a multi-neighborhood search with inter-route and intra-route optimization at the customer level, a pre-processing neighborhood reduction strategy to avoid examining non-promising candidate solutions, and a strategy for controlled exploration of infeasible solutions. Extensive experimental results on 1848 benchmark instances convincingly demonstrate high competitiveness of the approach in terms of both solution quality and computational time, compared to the state-of-the-art heuristics from the literature. In particular, the proposed algorithm improves upon the existing best-known solutions for 294 instances, while matching the previous best-known results for all but 3 of the remaining instances. To gain further insights into the algorithm’s performance, additional experiments are conducted to analyze its main components.

A descriptive and prescriptive analysis of rail service subsidies in the China–Europe freight transportation market

This study delivers a descriptive and prescriptive analysis of rail service subsidies for China Railway Express (CRE) in the China-Europe freight transportation market. The analysis is conducted by advanced mathematical modeling and programming methods. Specifically, we implemented a multicommodity multimodal freight transportation network equilibrium model that can be used for predicting the commodity-specific mode-route cargo flow pattern and hence for assessing the effectiveness and limitations of the current CRE subsidy scheme. To properly quantify the impact of subsidies on individual shippers’ decision making, the model explicitly characterizes individual shippers’ mode-route choice behavior and takes into account shipping cost, transit time, capacity-induced congestion surcharge, and unobserved transportation impedances as shippers’ disutility. The solution of the network equilibrium model resorts to a disaggregate simplicial decomposition algorithm within the well-known Lagrangian relaxation framework. A bi-level network-based subsidy optimization model is constructed, in which the upper level aims at minimizing the sum of revenue loss and congestion charge, and the lower level is the aforementioned freight transportation network equilibrium model. A tabu search procedure is proposed and implemented to derive the solution of the bi-level model. The above models and algorithms are then applied to the China-Europe containerized freight transportation network, which comprises all China-Europe liner shipping lines, all CRE service lines, and the highway networks in China and Europe. The evaluation and optimization results show that the current subsidy scheme creates an imbalanced capacity utilization pattern across CRE service lines while an optimized line-specific subsidy solution can yield noteworthy improvements in the service utilization and economic efficiency of CRE.

A flexible variable neighbourhood search algorithm for different variants of the Electric Vehicle Routing Problem

This manuscript presents a flexible variable neighbourhood search (VNS)-based (Flexi-VNS) algorithm to tackle both the classical Electrical Vehicle Routing Problem (EVRP) and the Battery Swap Station Location-routing Problem with Capacitated Electric Vehicles (BSS-EV-LRP). The latter has been addressed in just a few studies. It incorporates into the EVRP the battery swapping stations (BSS) location requirement to be jointly taken with decisions on the routing of electric vehicles. The Flexi-VNS algorithm includes a Randomised Variable Neighbourhood Descent (RVND) method as the local search procedure, which incorporates a new intra-RVND procedure called every time a solution is modified and applied only to those routes that have been modified. We assess the performance of Flexi-VNS on EVRP and BSS-EV-LRP benchmark instances and compare it with some algorithms in the literature. The statistical test did not guarantee the existence of a statistical difference between the proposed algorithm and the best algorithm from the literature for each problem, considering a 95% confidence level. However, despite this, computational results showed the Flexi-VNS efficiency. It improved several of the best-known solutions and reduced the number of constructed battery swap stations. More specifically, Flexi-VNS was able to equal or improve the BKS values of 15 out of a total of 20 instances with available results in the literature for BSS-EV-LRP and 47 out of 52 instances available for the EVRP, totalling 75% of the BSS-EV-LRP instances and 90.38% of the EVRP instances. Furthermore, compared to the exact algorithms, the optimal solutions were found using only a fraction of the computational times reported. Additionally, Flexi-VNS was the first to provide solutions for some BSS-EV-LRP instances.

A competitive heuristic algorithm for vehicle routing problems with drones

We propose a heuristic algorithm capable of handling multiple variants of the vehicle routing problem with drones (VRPD). Assuming that the drone may be launched from a node and recovered at another, these variants are characterized by three axes, (1) minimizing the transportation cost or minimizing the makespan, (2) the drone is either allowed or not allowed to land while awaiting recovery, and (3) single or multiple trucks each equipped with a drone. In our algorithm, we represent a VRPD solution as a set of customer sequences and evaluate it via local search procedures solving for each sequence a problem that we refer to as the fixed route drone dispatch problem (FRDDP). Given a sequence of customers to be served by a single truck and its drone, the FRDDP selects a subset of customers to be served by the drone and determines drone launch and recovery nodes, while ensuring that each such customer is positioned between two nodes in the initial sequence. We introduce a heuristic dynamic program (HDP) to solve the FRDDP with reduced computational complexity compared to an exact solution algorithm for the problem. We reinforce our algorithm by developing filtering strategies based on the HDP. We benchmark the performance of our algorithm on nine benchmark sets pertaining to four VRPD variants resulting in 932 instances. Our algorithm computes 651 of 680 optimal solutions and identifies 189 new best-known solutions.

A Q-learning based iterated local search algorithm for human-UAV cooperation in restoring transmission network

The power transmission network is easily to be destroyed when natural or man-made disasters occur. Restoration of power supply under disaster environments faces difficulties since a large-scale network typically contains many uninspected faulty nodes. Utilizing unmanned aerial vehicles (UAVs) to inspect these unknown faulty nodes can significantly improve the efficiency for subsequent restoration work performed by human-teams. Nevertheless, efficient cooperation of UAV and human-team is a complicated work due to the complexity of network structure and correlation between UAV scheduling and human-team scheduling. In this paper, a mathematical model is established to describe the considered problem aiming at maximizing the restored power supply in a limited response time. Then a Q-learning based iterated local search (Q ILS) algorithm is proposed to formulate the collaborative scheduling problem. Firstly, an initialization method is designed to assign UAVs for inspecting unknown faulty nodes and human-teams for repairing faulty nodes, which ensures each unknown faulty node is inspected before maintenance. Secondly, searching operators including perturbation and local search procedures are designed to ensure exploration and exploitation capability. Thirdly, Q-learning method is utilized as a learning engine to guide the direction of solution evolution. Moreover, the parameters of Q ILS are calibrated by multi-factor analysis of variance method to determine proper values. The computational simulations and comparison experiments validate the superiority of proposed algorithm.

Physical Activity for Anxiety for Autistic People: A Systematic Review.

Clinical anxiety is a common comorbidity in autistic people. Due to the prevalence of anxiety in the autism population and the adverse effects it causes, there is a critical need to develop effective interventions which address anxiety symptoms for autistic people. Therefore, the purpose of this systematic review was to examine the effectiveness of the use of physical activity as an intervention to reduce anxiety in autistic people. Three databases PubMed, PsychInfo, and Cochrane RCTs, were searched utilizing key terms. PRISMA systematic search procedures identified 44 studies meeting predetermined inclusion criteria. Participant characteristics, the type of physical activity performed, the nature of the physical activity program/delivery, anxiety-related outcomes, and research methodology was evaluated for each study. Each paper included was appraised and scored for risk of bias using Cochrane Handbook for Systematic Reviews of Interventions risk of bias tool. Titles and abstracts of 44 articles were reviewed and 8 articles met inclusion criteria which evaluated interventions. Evidence from 8 studies suggests that yoga, a community-based football program, an app-assisted walking program, group exercise programs, and horseback riding interventions reduced anxiety for autistic people. The studies included in this systematic review provide strong-to-moderate evidence that physical activity can reduce anxiety for autistic children and adults. However, additional research is needed to identify which mode of physical activity is most beneficial for anxiety reduction. Further, future research should evaluate frequency, duration, and intensity and their effects on anxiety for autistic people.

The home health care routing with heterogeneous electric vehicles and synchronization

AbstractThis paper studies the problem of heterogeneous electric vehicles, fast chargers, and synchronized jobs that have time windows in home healthcare routing and scheduling. We consider a problem that aims to establish daily routes and schedules for healthcare nurses to provide a variety of services to patients located in a scattered area. Each nurse should be assigned to an electric vehicle (EV) from a heterogeneous fleet of EVs to perform the assigned jobs within working hours. We consider three different types of EVs in terms of battery capacity and energy consumption. We aim to minimize the total cost of energy consumption, fixed nurse cost, and costs arising from the patients that cannot be served within the working day. We model the problem as a mixed integer programming formulation. We develop a hybrid metaheuristic based on a greedy random adaptive search procedure heuristic, to generate good quality initial solutions quickly, and an adaptive variable neighborhood search algorithm to generate high quality solutions in reasonable time. The hybrid metaheuristic employs a set of new advanced efficient procedures designed to handle the complex structure of the problem. Through extensive computational experiments, the performance of the mathematical model and the hybrid metaheuristic are evaluated. We conduct analyses on the robustness of the metaheuristic and the performance contribution of employing adaptive probabilities. We analyze the impact of problem parameters such as competency requirements, job duration, and synchronized jobs.

Towards efficient control synthesis for nonlinear wave energy conversion systems: impedance-matching meets the spectral-domain

Existing studies within the literature that focus on designing parametric energy-maximizing controllers for Wave Energy Converter (WEC) systems predominantly rely on the impedance-matching (IM) principle, originally developed for linear time-invariant systems. Alternatively, iterative optimization routines are commonly employed for nonlinear WECs. However, these approaches often face a trade-off between effectiveness in maximizing energy extraction and computational efficiency. To address this limitation, this study proposes a computationally efficient controller tuning method for analogous synthesis in the case of nonlinear WECs. The proposed approach combines a statistical linearization technique known as spectral-domain modeling with the IM principle, to synthesize a Proportional–Integrative (PI) controller for a nonlinear WEC. Furthermore, a comparison is performed with two other synthesis methods: one based on a standard (i.e. linear) frequency-domain representation of the WEC that incorporates the IM principle, and the other employing a gradient-free optimization routine applied to the nonlinear time-domain model of the WEC for PI parameter tuning through exhaustive numerical search. A discussion on the effectiveness of each tuning method in maximizing energy absorption is provided, including an appraisal of their associated computational time requirements. Numerical analyses demonstrate that the proposed method, which integrates spectral-domain modeling and IM, can achieve (almost) optimal PI controller design for a nonlinear WEC. Furthermore, this study addresses the inaccuracies inherent in the frequency-domain approach and significantly reduces the computational time compared to the exhaustive search procedure. The findings of this research represent a significant advancement towards the development of simple, effective, and efficient IM-based techniques for synthesis of controllers in nonlinear WEC systems

Model and heuristics for the multi-manned assembly line worker integration and balancing problem

This paper examines the balancing of assembly lines with multi-manned stations and a heterogeneous workforce. Both topics received considerable attention in the literature, but not in an integrated fashion. Combining these two characteristics gives rise to a highly combinatorial Multi-manned Assembly Line Worker Integration and Balancing Problem. When considering multi-manned stations, the already coupled decisions on assigning tasks to heterogeneous workers and workers to stations must be further linked with task scheduling assessments. We propose a Mixed-Integer Linear Programming model and develop two heuristic solution procedures, which tackle the problem with a hierarchical decomposition approach. Computational tests on a large dataset indicate that the proposed method can obtain good primal bounds in short computational times. We demonstrate that these results can be applied to the monolithic model either as a warm start or in a proximity search procedure to obtain synergistic gains with statistically significant differences. From a managerial perspective, we show that multi-manned stations can reduce the assembly line's length even in the presence of a heterogeneous workforce, which is crucial for many industries manufacturing large-size products.

Meta-analysis: The effect of problem-based learning on higher-order thinking skills of Indonesian students

Higher order thinking skills are one of the competencies developed in learning, especially in mathematics. One of the effective learning models to improve higher order thinking skills is Problem-Based Learning (PBL). In this study, a meta-analysis method was used, which involved analyzing various articles that had been collected. Search procedures through Google Scholar and Garuda Portal and found 99 articles that discuss the effect of PBL implementation in Indonesia on students' higher order thinking skills. Of these 99 articles, only 11 articles met the inclusion criteria and were then analyzed using the meta-analysis method. This meta-analysis method provides results in the form of a combined effect size that describes the extent of the influence of PBL implementation on students' higher order thinking skills. Based on the interpretation of the combined effect size, it can be concluded that overall, the application of PBL has a moderate effect on students' higher order thinking skills. In other words, PBL has a positive impact in improving students' ability to think critically, analytically, creatively, and think reflectively. In addition, this study also considered study characteristics, such as education level and year of study. The statistical analysis showed the effect of PBL implementation in improving students' ability to think critically, analytically, creatively, and reflectively.

Analyzing Handball Techniques Using A Biomechanical Approach: A Systematic Literature Review

Objectives. The study aimed to examine the use of biomechanical analysis in handball technique. Materials and methods. This review study followed the PRISMA standards for systematic reviews and meta-analyses.The study had to be published within the period of 2018 to 2023. The search procedure involved using the keywords (1)handball and (2) biomechanics. Scopus search engine was used in the study. Results. The search results on the database yielded 115 articles, which were adjusted according to the criteria into 11 articles. Conclusion. Biomechanical analysis that can be applied to handball techniques includes such aspects: distance travelled, speed, change of direction, joint angle, postural stability, movement pattern, and injury localization.

Integrated optimization of traffic signal timings and vehicle trajectories considering mandatory lane-changing at isolated intersections

Intersections are the predominant bottlenecks in urban areas due to the intricate conflicts among vehicles with different movements. With the advent of connected and automated vehicles (CAVs), it is more effective to reorganize urban traffic in a coordinated manner, and integrate the planning of both the traffic signal timings and vehicle trajectories. Most existing studies in this field assume that all vehicles are in their intended lanes initially, and only optimize the trajectories in the longitudinal dimension for simplicity, which is ideal and unrealistic. Therefore, this paper considers mandatory lane-changing for the integrated optimization of traffic signal timings and vehicle trajectories at isolated intersections. The entire planning horizon is segmented into a series of time-slots, based on which we select phases and plan the trajectories. First, a single-layer mixed integer programming (MIP) model is formulated to obtain global optimal solutions. Nevertheless, for large-scale cases, the MIP model is not applicable due to its computational complexities. Thus, we regulate vehicles into multi-lane platoons that correspond to green phases to reduce the solution space, and propose a beam-search-based platoon formation planning (BPFP) framework for efficient optimization. A beam search procedure is proposed to select phases for each time-slot, which incorporates a vehicle assignment algorithm to design the platoon formation as a subroutine. The two-dimensional vehicle trajectories are subsequently planned according to the platoon formation profiles. Numerical experiments illustrate that BPFP is capable of acquiring near-optimal solutions and considerably outperforms the baselines, while the computation time is within seconds even in over-saturated scenarios.

A greedy randomized adaptive search procedure for scheduling IoT tasks in virtualized fog–cloud computing

AbstractVirtualized fog–cloud computing (VFCC) has emerged as an optimal platform for processing the increasing number of emerging Internet of Things (IoT) applications. VFCC resources are provisioned to IoT applications in the form of virtual machines (VMs). Effectively utilizing VMs for diverse IoT tasks with varying requirements poses a significant challenge due to their heterogeneity in processing power, communication delay, and energy consumption. In addressing this challenge, in this article, we propose a system model for scheduling IoT tasks in VFCCs, considering not only individual task deadlines but also the system's overall energy consumption. Subsequently, we employ a greedy randomized adaptive search procedure (GRASP) to determine the optimal assignment of IoT tasks among VMs. GRASP, a metaheuristic‐based technique, offers appealing characteristics, including simplicity, ease of implementation, a limited number of tuning parameters, and the potential for parallel implementation. Our comprehensive experiments evaluate the effectiveness of the proposed method, comparing its performance with the most advanced algorithms. The results demonstrate that the proposed approach outperforms the existing methods in terms of deadline satisfaction ratio, average response time, energy consumption, and makespan.

Expedited re-design of multi-band passive microwave circuits using orthogonal scaling directions and gradient-based tuning

Geometry scaling of microwave circuits is an essential but challenging task. In particular, the employment of a given passive structure in a different application area often requires re-adjustment of the operating frequencies/bands while maintaining top performance. Achieving this necessitates the utilization of numerical optimization methods. Nonetheless, if the intended frequencies are distant from the ones at the starting point, local search procedures tend to fail, whereas global search algorithms are computationally expensive. As recently demonstrated, a combination of large-scale concurrent geometry parameter scaling with intermittent local tuning allows for dependable re-design of high-frequency circuits at low CPU costs. Unfortunately, the procedure is only applicable to single-band structures due to synchronized modifications of all operating bands under scaling. This article discusses a novel procedure that leverages a similar overall concept, but allows for independent control of all center frequencies. To achieve this goal, an automated decision-making procedure is developed in which a set of orthogonal scaling directions are determined based on their effect on individual circuit bands, and using auxiliary optimization sub-problems. The scaling range is then automatically computed by solving an appropriately-defined least-square design relocation problem. The methodology introduced in the work is illustrated using two planar passive devices. In both cases, wide-range operating frequency re-design has been demonstrated and favorably compared to conventional gradient-based tuning. Furthermore, the presented procedure has been shown to be computationally efficient. It is also easy to implement and integrate with a variety of gradient-based optimization procedures of a descent type.

Promotion of Muscle-Strengthening Activity Among Latina and Black/African American Women: A Review of Literature.

Latina and Black/African American (AA) women report disproportionately low levels of muscle-strengthening activities (MSA) and high rates of related chronic health conditions. Despite the health benefits of MSA, physical activity intervention research in these populations has focused mostly on increasing aerobic physical activity. The purpose of this review was to describe the current state of scientific literature on MSA interventions among Latina and Black/AA women. Two electronic databases, CINAHL and PubMed, were searched for studies published during the past 10years. Studies were included in this review if they reported promotion of MSA, included at least 50% Latina and/or Black/AA women in their samples, and used an interventional design. Search procedures identified 8 unique interventions targeting MSA in Latina (n = 3) and Black/AA women (n = 5). Results revealed there is limited published research on MSA promotion among Latina and Black/AA women, especially on theory-based interventions that address psychosocial and behavioral influences of MSA, as well as assessment of MSA outcomes in these populations. This review highlights a critical need for research on culturally tailored behavioral interventions to reduce the low MSA in Latina and Black/AA women and provides future research directions on this topic.