Efficient Heuristic Algorithm Research Articles

Solving the k-dominating set problem on very large-scale networks

The well-known minimum dominating set problem (MDSP) aims to construct the minimum-size subset of vertices in a graph such that every other vertex has at least one neighbor in the subset. In this article, we study a general version of the problem that extends the neighborhood relationship: two vertices are called neighbors of each other if there exists a path through no more than k edges between them. The problem called “minimum k-dominating set problem” (MkDSP) becomes the classical dominating set problem if k is 1 and has important applications in monitoring large-scale social networks. We propose an efficient heuristic algorithm that can handle real-world instances with up to 17 million vertices and 33 million edges. This is the first time such large graphs are solved for the minimum k-dominating set problem.

Minimum-Latency FEC Design With Delayed Feedback: Mathematical Modeling and Efficient Algorithms

In this paper, we consider the packet-level forward error correction (FEC) code design, without feedback or with delayed feedback, for achieving the minimum end-to-end latency, i.e., the latency between the time when packet is generated at the source and its \emph{in-order delivery} to the application layer of the destination. We first show that the minimum-latency FEC design problem can be modeled as a partially observable Markov decision process (POMDP), and hence the optimal code construction can be obtained by solving the corresponding POMDP. However, solving the POMDP optimally is in general difficult unless the size is very small. To this end, we propose an efficient heuristic algorithm, namely the majority vote policy, for obtaining a high quality approximate solution. We also derive the tight lower and upper bounds of the optimal state values of this POMDP, based on which a more sophisticated D-step search algorithm is implemented for obtaining near-optimal solutions. The simulation results show that the proposed code designs via solving the POMDP, either with the majority vote policy or the D-step search algorithm, strictly outperform the existing schemes, in both cases, without or with only delayed feedback.

Accurate Tracking, Collision Detection, and Optimal Scheduling of Airport Ground Support Equipment

In order to lower the ramp risk and improve the aircraft ground handling efficiency, we aim to: 1) track ground support equipment (GSE) in a real-time and high-accuracy manner so that we can not only conveniently obtain the positions and velocities of them but also reliably report latent collisions among aircraft and GSE. As a result, corresponding ramp risks could be detected and handled in advance and 2) schedule the GSE in an optimal manner based on the real-time data gathered in advance to make efficient use of GSE so that we can smoothly serve the annually increasing air traffic while controlling the ramp area congestion and GSE overheads. In detail, first, we develop a real-time and high-accuracy tracking device consisting of one real-time kinematic (RTK) unit and heading unit(s), for GSE including not only those which have only one carriage, such as tractors, shutters, and so forth but also baggage transit trains that contain one tug plus multiple dollies. The tracking accuracy for GSE could be limited within centimeters so that the monitor, avoidance, and fixation of unaware ramp risks become possible. Second, for optimal scheduling of GSE, a mixed-integer linear programming model and an efficient heuristic algorithm are proposed to minimize the total cost of equipment’s rental and travel consumption while respecting the constraints, such as flights timetables, GSE moving speeds limit, the total number of GSE available in stock, the maximum number of dollies allowed to attach to each baggage transit train, and so on.

On the Optimization of User Association and Resource Allocation in HetNets With mm-Wave Base Stations

This article investigates the problem of joint user association and resource allocation, defined by the number of allocated time-slots, in hybrid heterogeneous networks with the coexistence of sub-6-GHz base stations and millimeter wave (mm-Wave) base stations. To do so, we formulate a joint optimization problem to improve the efficiency of resource utilization by maximizing the number of associated users and minimizing the number of allocated time-slots. The optimization problem is formulated as a binary integer linear program and is proved to be NP-hard. Accordingly, we propose two efficient heuristic algorithms to solve it. The first one is centralized and relies on complete information, whereas the second one is distributed and is based on a reinforcement learning approach. The proposed distributed learning algorithm aims to find the best association for each user based on its past experience, automatically and independently from others. Simulation results show that the performances of both proposed algorithms are close-to-optimal with an important reduction in computational complexity.

Mechanism design for on-demand first-mile ridesharing

Ridesharing emerges as a viable way to bridge the first-mile accessibility gap to public transit. There is particularly a high demand for the first-mile ridesharing service in transit-intensive metropolitan areas. This paper studies the mechanism design problem in order to promote passengers’ participation in the on-demand first-mile ridesharing accounting for passengers’ mobility preferences, including passengers’ requirements on arrival deadlines, maximum willing-to-pay prices, and detour tolerances. A rolling horizon planning approach is used to process spontaneous on-demand passenger requests. We propose a novel mechanism, namely “Mobility-Preference-Based Mechanism with Baseline Price Control” (MPMBPC), which adapts the traditional Vickrey–Clarke–Groves (VCG) mechanism and incorporates a baseline price control component. MPMBPC is proved to satisfy several important mechanism design properties, including “individual rationality”, “incentive compatibility”, “price controllability”, and “detour discounting reasonability”. In comparison with the traditional general-purpose VCG mechanism, MPMBPC can avoid unreasonably low prices and prevent carriers’ deficits. A computationally efficient heuristic algorithm called Solution Pooling Approach (SPA) is developed to solve large-scale ridesharing mechanism design problems. Numerical examples are developed to demonstrate that SPA can solve large-scale ridesharing mechanism design problems in a computationally efficient way, with satisfactory solution qualities.

A Fast Approach for Reoptimization of Railway Train Platforming in Case of Train Delays

Train platforming is critical for ensuring the safety and efficiency of train operations within the stations, especially when unexpected train delays occur. This paper studies the problem of reoptimization of train platforming in case of train delays, where the train station is modeled using the discretization of the platform track time-space resources. To solve the reoptimization problem, we propose a mixed integer linear programming (MILP) model, which minimizes the weighted sum of total train delays and the platform track assignment costs, subject to constraints defined by operational requirements. Moreover, we design an efficient heuristic algorithm to solve the MILP model such that it can speed up the reoptimization process with good solution precision. Furthermore, a real-world case is taken as an example to show the efficiency and effectiveness of the proposed model and algorithm. The computational results show that the MILP model established in this paper can describe the reoptimization of train platforming accurately, and it can be solved quickly by the proposed heuristic algorithm. In addition, the model and algorithm developed in this paper can provide an effective computer-aided decision-making tool for the train dispatchers in case of train delays.

Optimizing the Learning Performance in Mobile Augmented Reality Systems With CNN

It is an essential goal for future wireless networks to provide better artificial intelligent services. In this paper, we investigate the joint communication and computation resource optimization in the mobile edge learning system to support augmented reality applications, where the convolutional neural networks (CNNs) are deployed at the edge server. For such a system, we first develop a delay model to characterize the relation between the computation latency and the input image size of general CNN models. Then, we formulate a mixed integer nonlinear optimization problem to maximize the system computation capacity under the constraints of learning accuracy, end-to-end latency, and energy consumption. To solve this problem, we first investigate maximizing the system learning accuracy under the communication and computation resource constraints. The optimal resource allocation policy can be achieved by a low-complexity search algorithm. We further prove that the original problem is NP-hard and propose an efficient heuristic algorithm with a newly-developed offloading priority function. An upper bound for the proposed algorithm is also derived. Finally, test results validate the applicability of the delay model and demonstrate the performance improvement of the proposed algorithm as compared with the existing algorithms.

Cost-Efficient VNF Placement and Scheduling in Public Cloud Networks

Following successful adoption of cloud computing, many service providers (SPs) are now using high-performance Virtual Machines (VMs) located in large datacenters owned by public cloud infrastructure providers to deploy their virtual network functions (VNFs). Since using these VMs has a cost depending on utilization time, a complex problem of VNF placement and scheduling (VPS) must be addressed to achieve satisfactory network performance (e.g., latency) while minimizing the cost paid to lease VMs. In this study, a cost-efficient VPS scheme (CE-VPS) is proposed to address the VPS problem in public cloud networks considering dynamic requests of ordered sequences of VNFs. Our CE-VPS scheme goes beyond existing solutions as it models some important practical aspects such as an additional latency incurred by booting a VM and installing a VNF instance. Also, CE-VPS considers that VNFs can be multi-threaded or single-threaded, and that their throughput as a function of allocated computing resources must be modeled differently. CE-VPS is formulated as a mixed inter linear program (MILP) and also as an efficient heuristic algorithm. CE-VPS achieves lower cost and latency than conventional Best-Availability and Cost-Efficient Proactive VNF Placement schemes, and a better trade-off between resource consumption and latency performance than a conventional Low-Latency scheme.

Item Assignment Problem in a Robotic Mobile Fulfillment System

A robotic mobile fulfillment system (RMFS) performs the order fulfillment process by bringing inventory to workers at pick-pack-and-ship warehouses. In the RMFS, robots lift and carry shelving units, called inventory pods, from storage locations to picking stations where workers pick items off the pods and put them into shipping cartons. The robots then return the pods to the storage area and transport other pods. In this article, we consider an item assignment problem in the RMFS in order to maximize the sum of similarity values of items in each pod. We especially focus on a reoptimization heuristic to address the situation where the similarity values are altered so that a good assignment solution can be obtained quickly with the changed similarity values. A constructive heuristic algorithm for the item assignment problem is developed, and then, a reoptimization heuristic is proposed based on the constructive heuristic algorithm. Then, computational results for several instances of the problem with 10–500 items are presented. We further analyze the case for which an item type can be placed into two pods. Note to Practitioners —This article proposes an efficient heuristic algorithm for assigning items to pods in a robotic mobile fulfillment system (RMFS) so that items ordered together frequently are put into the same pod. Computational results with 10–500 items show that the gaps from upper bounds are very small on average. For cases where the similarity values between items change or their estimation is not accurate due to the fluctuations in demand, a reoptimization heuristic algorithm that alters the original assignment is developed. The experimental results show that the reoptimization algorithm is robust when perturbation levels are approximately 40%–50% of the original similarity values with much less computation times. We believe that this research work can be very helpful for operating the RMFS efficiently.

An energy-efficient algorithm for virtual machine placement optimization in cloud data centers

Cloud providers offer computing services based on user demands using the Infrastructure as a Service (IaaS) service model. In a cloud data center, it is possible that multiple Virtual Machines (VMs) run on a Physical Machine (PM) using virtualization technology. Virtual Machine Placement (VMP) problem is the mapping of virtual machines across multiple physical ones. This process plays a vital role in defining energy consumption and resource usage efficiency in the cloud data center infrastructure. However, providing an efficient solution is not trivial due to difficulties such as machine heterogeneity, multi-dimensional resources, and large scale cloud data centers. In this paper, we propose an efficient heuristic algorithm that focuses on power consumption and resource wastage optimization to solve the aforementioned problem. The proposed algorithm, called MinPR, minimizes the total power consumption by reducing the number of active physical machines and prioritizing the power-efficient ones. Also, it reduces resource wastage by maximizing and balancing resource utilization among physical machines. To achieve these goals, we propose a new Resource Usage Factor model that manages virtual machine placement on physical machines using reward and penalty mechanisms. Simulations based on cloud user-customized VMs and Amazon EC2 Instances workloads illustrate that the proposed algorithm outperforms existing approaches. In particular, the proposed algorithm reduces total energy consumption by up to 15% for cloud user-customized VMs and by up to 10% for Amazon EC2 Instances.

Efficient Heuristic Algorithms for Single-Vehicle Task Planning With Precedence Constraints

This article investigates the task planning problem where one vehicle needs to visit a set of target locations while respecting the precedence constraints that specify the sequence orders to visit the targets. The objective is to minimize the vehicle's total travel distance to visit all the targets while satisfying all the precedence constraints. We show that the optimization problem is NP-hard, and consequently, to measure the proximity of a suboptimal solution from the optimal, a lower bound on the optimal solution is constructed based on the graph theory. Then, inspired by the existing topological sorting techniques, a new topological sorting strategy is proposed; in addition, facilitated by the sorting, we propose several heuristic algorithms to solve the task planning problem. The numerical experiments show that the designed algorithms can quickly lead to satisfying solutions and have better performance in comparison with popular genetic algorithms.

Targeted influence maximization under a multifactor-based information propagation model

Information propagation modeling and influence maximization are two important research problems in viral marketing. When marketing information is given, how can the seed nodes be efficiently identified to maximize the spread of the information through the network? To answer this question, we consider multiple factors in information propagation, such as information content, social influence and user authority, and propose a multifactor-based information propagation model (MFIP). Then, we utilize the first-order influence of the nodes to approximate their influence and propose an efficient heuristic algorithm named weighted degree decrease (WDD) to select the seed nodes under the MFIP model. Experimental evaluations with four real-world social network datasets demonstrate the effectiveness and efficiency of our algorithm.

A Nozzle Path Planner for 3-D Printing Applications

Additive manufacturing technologies have been widely applied in both household and industrial applications. The fabrication time of a three-dimensional (3-D) printed object can be shortened by optimizing its segments printing order. The computational times required by existing nozzle path planning algorithms can increase rapidly with the number of printing segments. In this article, the nozzle path planning problem is formulated as an undirected rural postman problem and a computationally efficient heuristic search algorithm is proposed to find fast routes and mitigate overheads in printing processes. Both simulation and experimental results concur that the proposed algorithm can significantly speed up printing processes and outperform its counterparts in 3-D printing applications.

Collaborative passenger flow control on an oversaturated metro line: a path choice approach

Focusing on reducing the traffic congestion on an oversaturated urban metro system, this study investigates the collaborative passenger flow control problem for a metro line. By introducing the timetable-oriented space-time network representation, the problem of interest is finally formulated as an integer programming model, in which the objective function aims to minimize the total weighted passenger waiting time at different stations. Since the final decision is closely related to the space-time path-finding process, the proposed model can also be regarded as a coordinated path-finding process with train loading capacity constraints. Then, an efficient heuristic algorithm is developed to solve the proposed model. Finally, a series of numerical examples are implemented on a sample metro line and the Beijing Batong metro line. The computational results show that the proposed approach can produce high-quality passenger flow control strategies for the metro system, thereby demonstrating the effectiveness of the proposed model and algorithm.

Efficient service deployment in mobile edge computing environment

Mobile applications' requirements for compute capability grow up daily. Providing mobile service in remote cloud is one of the solutions to this issue. However, due to the long geographical distance of clouds, it is difficult to ensure the claimed performance of real-time service. As a result, mobile edge computing has become the main solution to solve this problem. The present researches are concerned with cloudlet placement, and computation offloading, while the problem about how to deploy services on cloudlets based on the user's geographical distribution is overlooked. We address this issue in our paper. With the consideration of minimising the number of services and guaranteeing access delay constraint at the same time, this problem is formulated into an optimisation problem. For the NP-hardness of the problem, an efficient heuristic algorithm is proposed to resolve it. The simulation experiment at the end of this paper evaluates the performance of this algorithm. Experiment results demonstrate that the proposed heuristic algorithm is effective.

Effective Capacity Based Power Allocation for the Coexistence of an Integrated Radar and Communication System and a Commercial Communication System

Considerable interest has been shown in the coexistence between airborne radar and commercial communication systems in recent years. In particular, the integrated radar and communication system (IRCS) is promising for the airborne platforms like Unmanned Air Vehicles (UAVs). However, due to fast varying channels caused by high mobility, it is a great challenge for the fusion center to collect detection information within a given delay threshold through the air-to-ground (A2G) communication. Based on slowly varying components of the channel, i.e, target spectrum, power spectral densities of the signal dependent clutters, path loss and shadow fading, this paper considers the problem of power minimization for an IRCS and a base station (BS) coexisting in the same frequency band. The latency bound, latency violation probability (LVP), and channel capacity for the A2G communication to the fusion center are considered based on the effective capacity (EC) theory. The detection performance for radar and the rate requirement of BS user are also taken into account. The power allocation problem is non-convex and formulated to a monotonic optimization problem. Afterward, an efficient heuristic scheduling algorithm with acceptable computational complexity is proposed to solve the formulated problem. Then, the robust power allocation with channel estimation error is considered. Simulation results demonstrate the effectiveness of the proposed heuristic algorithm from the perspectives of the total transmit power, EC, and LVP of the IRCS.

Heuristic Algorithms of Coincidence for the Estimation of Movements in Compression of Images

The NP-Completeness theory states that exact and efficient algorithms are unlikely to exist for the class of NP-difficult problems. One way to deal with NP hardness is to relax the optimality requirement and look for solutions instead that are close to the optimum. This is the main idea behind the approximation algorithms, which are called heuristic or metaheuristic. The problem of motion estimation is a process with a high degree of computational complexity, it requires sufficient memory space and execution time. It represents the cost of static, dynamic and video image sequence coding. The main task is to minimize the distortion rate and improve visual quality. This makes research in the field of coding, image compression and video focus on finding efficient algorithms to carry out the estimation of movement in a reasonable time. If a list of images of n elements is analyzed, there are feasible solutions. So, an exhaustive search is too slow, even for small values of the solution space. Therefore, from a practical point of view, it is crucial to have efficient and fast heuristic algorithms that avoid thorough search. In this investigation we design and implement heuristic algorithms, based on the frequency domain, which are applied on the coefficients of the discrete transform of the cosine and wavelets. Also, we propose temporal domain algorithms such as block-matching algorithms, which focus your search on the maximum coincidence of the current image with the reference one. The algorithms used during the implementation of this research work were written with the mathematical programming language MATLAB. In addition, we review the basic concepts of image processing, video, compression algorithms and motion estimation frequently used. The evaluation of the algorithms was carried out with a set of images provided by a previous acquisition system. We show the improvement of visual quality, the amount of compressed or reconstructed information and the behavior of the methods in the search for similarities between pixels or images. Finally, we contribute to the dissemination of new lines of scientific research that lead to the expansion and improvement of the study, the generation of new knowledge, since it is a young area within the Education discipline of Nicaragua.

A New Splitting Criterion for Better Interpretable Trees

A new splitting criterion for classification trees that generates better decision rules in terms of interpretability is proposed in this paper. The criterion is designed to find homogeneous rules that describe a significant number of instances with a short length. The proposed criterion considers only one side of a split to generate highly homogeneous rules and concurrently utilizes a function of sample ratios with an adjustable hyperparameter to control the coverage of rules. The distinctive feature of the proposed method is that it is applied adaptively at every split. We also introduce an efficient heuristic algorithm to determine an appropriate hyperparameter value for every split. Experimental results evaluated over 17 benchmark datasets show that the proposed criterion combined with the proposed heuristic constructs a better interpretable decision tree. It is verified through quantitative and qualitative analysis that the constructed tree produces highly interpretable rules, and its predictive performance is comparable to that of other popular criteria.

Backhaul-Aware Optimization of UAV Base Station Location and Bandwidth Allocation for Profit Maximization

Unmanned Aerial Vehicle Base Stations (UAVBSs) are envisioned to be an integral component of the next generation Wireless Communications Networks (WCNs) by dynamically moving the supply towards the demand. A significant drawback of the state-of-the-art have been designing a WCN in which the service-oriented performance measures (e.g., throughput) are optimized without considering different relevant decisions such as determining the location and allocating the resources, jointly. In this study, we address the UAVBS location and bandwidth allocation problems together to optimize the total network profit. In particular, a Mixed-Integer Non-Linear Programming (MINLP) formulation is developed, in which the location of a single UAVBS and bandwidth allocations to users are jointly determined. The objective is to maximize the total profit without exceeding the backhaul and access capacities. The profit gained from a specific user is assumed to be a piecewise-linear function of the provided data rate level, where higher data rate levels would yield higher profit. Due to high complexity of the MINLP, we propose an efficient heuristic algorithm with lower computational complexity. We show that, when the UAVBS location is determined, the resource allocation problem can be reduced to a Multidimensional Binary Knapsack Problem (MBKP), which can be solved in pseudo-polynomial time. To exploit this structure, the optimal bandwidth allocations are determined by solving several MBKPs in a search algorithm. We test the performance of our algorithm with two heuristics and with the MINLP model solved by a commercial solver. Our numerical results show that the proposed algorithm outperforms the alternative solution approaches and would be a promising tool to improve the total network profit.

Community search for multiple nodes on attribute graphs

Community search is individualized and targeted, which identifies local communities containing query nodes and meeting specific conditions only according to users. Community search is only interested in communities that satisfy the designated criteria, and has a wide range of application scenarios in the real world. Traditional community search only uses the topology information, without considering the impact of node attributes information. And many existing algorithms can only deal with a single query node. Therefore, this paper defines the attribute community search problem of multiple query nodes on attribute graphs, which utilizes the edge connectivity as the structural tightness constraint and designs an attribute function as the attribute tightness constraint. To solve this problem, we design a framework that firstly finds the maximum k edge connected-component containing query as a candidate subgraph, then iteratively deletes the node with the least contribution to the attribute function from the candidate subgraph and adjusts the subgraph according to the structural constraints. We also design the attribute Steiner distance that synthesizes the structural information and attribute information, and propose an efficient heuristic expansion algorithm based on distance. The experimental results on several datasets show our proposed methods can find the community containing all query nodes with high tightness of structure and homogeneity of attributes.