Coverage Problem Research Articles

An exact algorithm for maximum electric vehicle flow coverage problem with heterogeneous chargers, nonlinear charging time and route deviations

Typical electric vehicles (EVs) should be recharged multiple times to reach a long distance due to the limited travel ranges. The EV flow covering problem determines the locations of charging stations to maximize the “covered” OD flows, respecting a given budget for the new charging stations. This paper proposes an exact algorithm for solving the problem by considering the heterogeneous EV chargers, nonlinear charging time, and route deviations from the shortest distance paths. We develop an exact Benders decomposition-based algorithm, in which the Benders subproblem utilizes a nested cut-generation via a customized labeling algorithm. Two methods for solving the Benders subproblem are developed, which are combined to accelerate the Benders cut generation. In addition, we present a heuristic algorithm based on the proposed exact algorithm for the larger-sized problems. The computational results show that the proposed algorithm could solve real-life problems. Especially, the heuristic algorithm could solve problems up to 339 nodes and 500 km of the EV’s travel range. The extensive computational study shows that addressing the multiple charger types, the charging time, and the deviation routes can be crucial to provide realistic charging infrastructure for EVs.

Алгоритм оценки деформации плит перекрытий по пролетно-опорным схемам здания

Planning the supporting structures of a building is a problem of an extremely wide choice. At the same time, the estimation of one option by the finite element method takes too much time to apply automated and decision support tools. Therefore, it is proposed to identify a quickly calculated estimation of the supports plan which would allow comparing two support plans correctly in terms of potential deformations. To achieve this, the existing heuristic approaches modeling the functional relationship between the plan of supports and the resulting deformations were analyzed. In this paper spans are taken as the basis for the estimation. Further, the problem of spans determining is formulated which is reduced to the problem of the geometric coverage of multicoherent orthogonal polygon. The objective function is the maximization of the correlation between the detected estimate and the actual calculated values of deformations. To determine the spans on the floor area, a new algorithm based on the sweep line algorithm is proposed which consists of three steps: partitioning the floor area, determining the proximity of partitioned sections and assembling sections into spans. In order to determine the main estimate for a span-support topology, a single span estimation is derived based on three parameters: span area, aspect ratio and topology of supports that bound the span. Since the last parameter is qualitative, an additional analysis of quantitative parameters that can correctly describe it was carried out. The analysis criterion was the correlation with the calculated deformations in the span. The parameter of the maximum perimeter distance between the supports was recognized as the best parameter. The best function for estimating the spans was selected. In addition, various approaches to assembling spans from partition sections at the last step of the algorithm were analyzed.

Kernelization for feedback vertex set via elimination distance to a forest

We study efficient preprocessing for the undirected Feedback Vertex Set problem, a fundamental problem in graph theory which asks for a minimum-sized vertex set whose removal yields an acyclic graph. More precisely, we aim to determine for which parameterizations this problem admits a polynomial kernel. While a characterization is known for the related Vertex Cover problem based on the recently introduced notion of bridge-depth, it remained an open problem whether this could be generalized to Feedback Vertex Set. The answer turns out to be negative; the existence of polynomial kernels for structural parameterizations for Feedback Vertex Set is governed by the elimination distance to a forest. Under the standard assumption NP⁄⊆coNP/poly, we prove that for any minor-closed graph class G, Feedback Vertex Set parameterized by the size of a modulator to G has a polynomial kernel if and only if G has bounded elimination distance to a forest. This captures and generalizes all existing kernels for structural parameterizations of the Feedback Vertex Set problem. [Display omitted]

ANALYSIS OF MULTIDIMENSIONAL POVERTY AMONG RURAL FARMING HOUSEHOLDS IN TARABA STATE, NIGERIA

The study analyzed the training needs of extension agents on information and communication technologies (ICTs) in Kaduna State Agricultural Development Programme (KADP), Nigeria. Seventy-five extension agents were randomly selected for the study. Data were collected using structured questionnaires and analyzed using frequencies, percentages, mean and multinomial regression. The results revealed that the majority of the extension agents had a great need for training in the use of ICT facilities such as multimedia (85%), remote sensing equipment (83.3), internet (83.3%), video camera (81.7%), audio recorder/player (76.7%), computer (73.3%) and audio-visual aids (66.7%). majority (83%) of the respondents had participated in one extension programme or the other while 20 % of the extension agents have never participated into any agricultural extension programme. Factors that influence the training needs of extension agents included training (-0.349 and 0.312), education level (-0.322 and 0.205), job experience (0.142) and age (-0.528) of the extension agents. The major constraints associated with the use of ICT tools by extension agents include lack of skills and knowledge, poor finance, the problem of internet coverage and connectivity, high cost of ICT tools, poor power supply and unavailability of ICT tools. The establishment of ICT units equipped with modern ICT facilities for extension services in all the KADA zones should be carried out. Training courses on the application ICT facilities in extension services should be planned and conducted for all extension agents in KADP for effective extension service delivery.

ASSESSMENT OF TRAINING NEEDS AMONG AGRICULTURAL EXTENSION AGENTS ON INFORMATION AND COMMUNICATION TECHNOLOGIES IN KADUNA STATE AGRICULTURAL DEVELOPMENT PROGRAMME, NIGERIA

The study analyzed the training needs of extension agents on information and communication technologies (ICTs) in Kaduna State Agricultural Development Programme (KADP), Nigeria. Seventy-five extension agents were randomly selected for the study. Data were collected using structured questionnaires and analyzed using frequencies, percentages, mean and multinomial regression. The results revealed that the majority of the extension agents had a great need for training in the use of ICT facilities such as multimedia (85%), remote sensing equipment (83.3), internet (83.3%), video camera (81.7%), audio recorder/player (76.7%), computer (73.3%) and audio-visual aids (66.7%). majority (83%) of the respondents had participated in one extension programme or the other while 20 % of the extension agents have never participated into any agricultural extension programme. Factors that influence the training needs of extension agents included training (-0.349 and 0.312), education level (-0.322 and 0.205), job experience (0.142) and age (-0.528) of the extension agents. The major constraints associated with the use of ICT tools by extension agents include lack of skills and knowledge, poor finance, the problem of internet coverage and connectivity, high cost of ICT tools, poor power supply and unavailability of ICT tools. The establishment of ICT units equipped with modern ICT facilities for extension services in all the KADA zones should be carried out. Training courses on the application ICT facilities in extension services should be planned and conducted for all extension agents in KADP for effective extension service delivery.

Research on Morphological Innovation Design of Industrial Products Based on Combinatorial Genetic Algorithm

Abstract This paper applies the combinatorial genetic algorithm to the design of industrial product forms to achieve the effect of industrial product form design innovation. The standard genetic algorithm is applied to industrial product design, and the optimal order of genetic optimization is constructed through continuous population genetics, combined with the minimum value of the objective function to construct the constraints of the innovative design of industrial products. Using the method of combinatorial optimization to integrate different elements in industrial product design, a coding method based on rows and columns is proposed for the set coverage problem of design elements. Analyzing the user’s preference for industrial product form design allows us to explore the demand direction of industrial product innovation design and formulate a suitable innovation design scheme. The results show that the consensus satisfaction of hair dryer form design in genetic optimization 2nd and 9th are higher than 0.8. When users are stimulated by like and dislike sample pictures, there is no significant difference in the average response time, p=0.337>0.05, and the difference in the average response time of the users is more significant p=0.020<0.05 when they are confronted with the preferred design shape.

A Survey on Swarm Robotics for Area Coverage Problem

The area coverage problem solution is one of the vital research areas which can benefit from swarm robotics. The greatest challenge to the swarm robotics system is to complete the task of covering an area effectively. Many domains where area coverage is essential include exploration, surveillance, mapping, foraging, and several other applications. This paper introduces a survey of swarm robotics in area coverage research papers from 2015 to 2022 regarding the algorithms and methods used, hardware, and applications in this domain. Different types of algorithms and hardware were dealt with and analysed; according to the analysis, the characteristics and advantages of each of them were identified, and we determined their suitability for different applications in covering the area for many goals. This study demonstrates that naturally inspired algorithms have the most significant role in swarm robotics for area coverage compared to other techniques. In addition, modern hardware has more capabilities suitable for supporting swarm robotics to cover an area, even if the environment is complex and contains static or dynamic obstacles.

The mechanism analysis on the farmers’ motivation of using the quality traceability system based on TAM-ECM model

The quality traceability system for agricultural product plays an important role in tracing the production history or flow of agricultural products in China. At present, the quality traceability system is facing the problem of limited coverage and promotion. Understanding the continuous usage behavior of users can help solve this problem. This study constructed a model integrating TAM-ECM to analyze the mechanisms affecting the continuous usage intention and behavior of users of the quality traceability system, using PLS analysis method and survey data from 197 users of the GanNan navel orange quality traceability system with a usage time of up to two years. The results show that both satisfaction and usage habits directly promote the continuous usage of the quality traceability system. Lowering transaction costs significantly improves perceived value and satisfaction, and has a greater direct impact on satisfaction. Expectation confirmation level has a positive effect on perceived value, and lowering transaction costs also enhances perceived value. Perceived value、transaction cost, and satisfaction are important factors driving the continuous usage of the quality traceability system by users. The research findings provide a reference basis for the government to develop policies to attract users to use the quality traceability system for agricultural product.

Hardness and approximation of submodular minimum linear ordering problems

AbstractThe minimum linear ordering problem (MLOP) generalizes well-known combinatorial optimization problems such as minimum linear arrangement and minimum sum set cover. MLOP seeks to minimize an aggregated cost $$f(\cdot )$$ f ( · ) due to an ordering $$\sigma $$ σ of the items (say [n]), i.e., $$\min _{\sigma } \sum _{i\in [n]} f(E_{i,\sigma })$$ min σ ∑ i ∈ [ n ] f ( E i , σ ) , where $$E_{i,\sigma }$$ E i , σ is the set of items mapped by $$\sigma $$ σ to indices [i]. Despite an extensive literature on MLOP variants and approximations for these, it was unclear whether the graphic matroid MLOP was NP-hard. We settle this question through non-trivial reductions from mininimum latency vertex cover and minimum sum vertex cover problems. We further propose a new combinatorial algorithm for approximating monotone submodular MLOP, using the theory of principal partitions. This is in contrast to the rounding algorithm by Iwata et al. (in: APPROX, 2012), using Lovász extension of submodular functions. We show a $$(2-\frac{1+\ell _{f}}{1+|E|})$$ ( 2 - 1 + ℓ f 1 + | E | ) -approximation for monotone submodular MLOP where $$\ell _{f}=\frac{f(E)}{\max _{x\in E}f(\{x\})}$$ ℓ f = f ( E ) max x ∈ E f ( { x } ) satisfies $$1 \le \ell _f \le |E|$$ 1 ≤ ℓ f ≤ | E | . Our theory provides new approximation bounds for special cases of the problem, in particular a $$(2-\frac{1+r(E)}{1+|E|})$$ ( 2 - 1 + r ( E ) 1 + | E | ) -approximation for the matroid MLOP, where $$f = r$$ f = r is the rank function of a matroid. We further show that minimum latency vertex cover is $$\frac{4}{3}$$ 4 3 -approximable, by which we also lower bound the integrality gap of its natural LP relaxation, which might be of independent interest.

Communication-Topology-preserving Motion Planning: Enabling Static Routing in UAV Networks

Unmanned Aerial Vehicle (UAV) swarm offers extended coverage and is a vital solution for many applications. A key issue in UAV swarm control is to cover all targets while maintaining connectivity among UAVs, referred to as a multi-target coverage problem. With existing dynamic routing protocols, the flying ad hoc network suffers outdated and incorrect route information due to frequent topology changes. This might lead to failures of time-critical tasks. One mitigation solution is to keep the physical topology unchanged, thus maintaining a fixed communication topology and enabling static routing. However, keeping physical topology unchanged may sacrifice the coverage. In this article, we propose to maintain a fixed communication topology among UAVs, which allows certain changes in physical topology, so that to maximize the coverage. We develop a distributed motion planning algorithm for the online multi-target coverage problem with the constraint of keeping communication topology intact. As the communication topology needs to be timely updated when UAVs leave or arrive at the swarm, we further design a topology-management protocol. Experimental results from the ns-3 simulator show that under our algorithms, UAV swarms of different sizes achieve significantly improved delay and loss ratio, efficient coverage, and rapid topology update.

Design and development of a fuzzy explainable expert system for a diagnostic robot of COVID-19

<p><span lang="EN-US">Expert systems have been widely used in medicine to diagnose different diseases. However, these rule-based systems only explain why and how their outcomes are reached. The rules leading to those outcomes are also expressed in a machine language and confronted with the familiar problems of coverage and specificity. This fact prevents procuring expert systems with fully human-understandable explanations. Furthermore, early diagnosis involves a high degree of uncertainty and vagueness which constitutes another challenge to overcome in this study. This paper aims to design and develop a fuzzy explainable expert system for coronavirus disease-2019 (COVID-19) diagnosis that could be incorporated into medical robots. The proposed medical robotic application deduces the likelihood level of contracting COVID-19 from the entered symptoms, the personal information, and the patient's activities. The proposal integrates fuzzy logic to deal with uncertainty and vagueness in diagnosis. Besides, it adopts a hybrid explainable artificial intelligence (XAI) technique to provide different explanation forms. In particular, the textual explanations are generated as rules expressed in a natural language while avoiding coverage and specificity problems. Therefore, the proposal could help overwhelmed hospitals during the epidemic propagation and avoid contamination using a solution with a high level of explicability.</span></p>

Heterogeneous decision-making dynamics of threshold-switching agents on complex networks.

In the classical two-player decision-making scenario, individuals may have different tendencies to take a certain action, given that there exists a sufficient number of neighbors adopting a particular option. This is ubiquitous in many real-life contexts including traffic congestion, crowd evacuation, and minimal vertex cover problem. Under best-response dynamics, we investigate the decision-making behaviors of heterogeneous agents on complex networks. Results of the networked games are twofold: for networks of uniform degree distribution (e.g., the lattice) and fraction of the strategy is of a linear function of the threshold setting. Moreover, the equilibrium analysis is provided and the relationship between the equilibrium dynamics and the change of the threshold value is given quantitatively. Next, if the games are played on networks with non-uniform degree distribution (e.g., random regular and scale-free networks), influence of the threshold-switching will be weakened. Robust experiments indicate that it is not the value of the average degree, but the degree distribution that influences how the strategy evolves affected by the threshold settings. Our result shows that the decision-making behaviors can be effectively manipulated by tuning the parameters in the utility function (i.e., thresholds) of some agents for more regular network structures.

The problem of media coverage of contemporary Arab formation

Deciphering the codes of contemporary plastic art requires an intellectual effort, so that communication between the artist and the media outlet can strip away the ambiguity to reach a clear reading such that the achievement can be understandably presented to the recipient. The first chapter serves to identify the problem, the scope of the study. As the issue stems from the ambiguous nature of an artist's piece for media publication, it would derive several questions: What is the level of interest of the Arab press in the field of plastic arts? How willing is the Arab press to actively contribute to the field? And are they open to enhancing the process of understanding or receiving it? The author(s) further elaborate upon the importance and aim of the research that is quantified by the objective and temporal metrics of the study along with relevant, defined terminology. The second chapter covers the two concepts of information and receiving through its design of the study's theoretical framework and the author(s) acknowledgment and citations of prior relevant works. Its third chapter details the procedures by which the researcher(s) would identify a sample populace consisting of chosen relatives and relevant works of art. The fourth chapter, in providing its analysis of the results and the drawn conclusions of the study, gave insight into how media and art can influence an audience's opinions about community issues. Such a connection would notably contribute to the decision-making process and shape the convictions of the individual..

Evolutionary Algorithm on General Cover with Theoretically Guaranteed Approximation Ratio

Theoretical studies on evolutionary algorithms have developed vigorously in recent years. Many such algorithms have theoretical guarantees in both running time and approximation ratio. Some approximation mechanism seems to be inherently embedded in many evolutionary algorithms. In this paper, we identify such a relation by proposing a unified analysis framework for a global simple multiobjective evolutionary algorithm (GSEMO) and apply it on a minimum weight general cover problem, which is general enough to subsume many important problems including the minimum submodular cover problem in which the submodular function is real-valued, and the minimum connected dominating set problem for which the potential function is nonsubmodular. We show that GSEMO yields theoretically guaranteed approximation ratios matching those achievable by a greedy algorithm in expected polynomial time when the potential function g is polynomial in the input size and the minimum gap between different g-values is a constant. History: Accepted by Erwin Pesch, Area Editor for Heuristic Search & Approximation Algorithms. Funding: This work was supported by National Natural Science Foundation of China [11771013, U20A2068]; Zhejiang Provincial Natural Science Foundation of China [LD19A010001].

Approximation Algorithms for the Min–Max Mixed Rural Postmen Cover Problem and Its Variants

Approximation Algorithms for the Min–Max Mixed Rural Postmen Cover Problem and Its Variants

Multi-Robot Cooperative Multi-Area Coverage Based on Circular Coding Genetic Algorithm

This paper studies the cooperative multi-area coverage problem with obstacles, which requires a group of robots to cover an area while avoiding collisions. This problem is very common in scenarios such as garbage removal, mine clearance, and regional information collection. The currently proposed algorithms usually have the problem of high redundancy and weak scene scalability. This paper designs a cooperative area coverage algorithm for multiple robots. First, a set of rules is proposed to divide the area to be covered into several small areas. Then, a genetic algorithm based on circular coding is designed to allocate these divided areas to several robots. Finally, the coverage path of the robot is designed using the zigzag method, so that the robot can cover the area allocated to it. Through computational experiments, it has been verified that this algorithm has efficiency advantages over state-of-the-art algorithms in certain scenarios and has scalability for different scenarios.

On the Parallel Parameterized Complexity of MaxSAT Variants

In the maximum satisfiability problem (max-sat) we are given a propositional formula in conjunctive normal form and have to find an assignment that satisfies as many clauses as possible. We study the parallel parameterized complexity of various versions of max-sat and provide the first constant-time algorithms parameterized either by the solution size or by the allowed excess relative to some guarantee. For the dual parameterized version where the parameter is the number of clauses we are allowed to leave unsatisfied, we present the first parallel algorithm for max-2sat (known as almost-2sat). The difficulty in solving almost-2sat in parallel comes from the fact that the iterative compression method, originally developed to prove that the problem is fixed-parameter tractable at all, is inherently sequential. We observe that a graph flow whose value is a parameter can be computed in parallel and develop a parallel algorithm for the vertex cover problem parameterized above the size of a given matching. Finally, we study the parallel complexity of max-sat parameterized by the vertex cover number, the treedepth, the feedback vertex set number, and the treewidth of the input’s incidence graph. While max-sat is fixedparameter tractable for all of these parameters, we show that they allow different degrees of possible parallelization. For all four we develop dedicated parallel algorithms that are constructive, meaning that they output an optimal assignment – in contrast to results that can be obtained by parallel meta-theorems, which often only solve the decision version.

Generalized class cover problem with axis-parallel strips

We initiate the study of a generalization of the class cover problem [Cannon and Cowen [1], Bereg et al. [2]] the generalized class cover problem, where we are allowed to misclassify some points provided we pay an associated positive penalty for every misclassified point. Two versions: single coverage and multiple coverage, of the generalized class cover problem are investigated. We study five different variants of both versions of the generalized class cover problem with axis-parallel strips and axis-parallel half-strips extending to different directions in the plane, thus extending similar work by Bereg et al. (2012) [2] on the class cover problem. We prove that the multiple coverage version of the generalize class cover problem with axis-parallel strips are in P, whereas the single coverage version is NP-hard. A factor 2 approximation algorithm is provided for the later problem. The APX-hardness result is also shown for the single coverage version. For half-strips extending to exactly one direction, both the single and multiple coverage versions can be solved in polynomial time using dynamic programming. In the case of half-strips extending to two orthogonal directions, we prove the class cover problem is NP-hard followed by APX-hard. This gives improve hardness results compare to Bereg et al. (2012) [2], where they proved the class cover problem with half-strips oriented in four different directions is NP-hard. These NP- and APX-hardness results can directly apply to both single and multiple versions. Finally, constant factor approximation algorithms are provided for half-strips extending to more than one direction.

Multi-UAV Airborne UV Area Coverage and Task Assignment Method

To address the area coverage problem in large-scale reconnaissance and detection tasks, Unmanned Aerial Vehicles(UAVs) utilize Ultraviolet(UV) light for flight guidance. Additionally, a UV light power control method is implemented to assist UAVs in adapting to various terrains. Based on the flight characteristics of quadrotor UAVs, the target area is divided into a grid-like format. This transformation helps convert the area coverage task into a multi-UAV path planning problem on a raster map. To address this, constraints are established for assigning tasks to multiple UAVs. An algorithm is proposed to assign the target area uniformly based on the initial position of the UAV, ensuring full coverage. Additionally, the algorithm considers the differences in endurance among multiple UAVs. This improved algorithm is highly suitable for real-life application scenarios. The simulation results demonstrate the power control changes in wireless UV-guided UAV flight. Moreover, the assignment results of the improved algorithm align closely with the theoretical assignment results, considering different parameters like the number of UAVs, initial position, and endurance capacity.

On the complexity of robust multi-stage problems with discrete recourse

We study the computational complexity of multi-stage robust optimization problems. Such problems are formulated with alternating min/max quantifiers and therefore naturally fall into a higher stage of the polynomial hierarchy. Despite this, almost no hardness results with respect to the polynomial hierarchy are known.In this work, we examine the hardness of robust two-stage adjustable and robust recoverable optimization with budgeted uncertainty sets. Our main technical contribution is the introduction of a technique tailored to prove Σ3p-hardness of such problems. We highlight a difference between continuous and discrete budgeted uncertainty: In the discrete case, indeed a wide range of problems becomes complete for the third stage of the polynomial hierarchy; in particular, this applies to the TSP, independent set, and vertex cover problems. However, in the continuous case this does not happen and problems remain in the first stage of the hierarchy. Finally, if we allow the uncertainty to not only affect the objective, but also multiple constraints, then this distinction disappears and even in the continuous case we encounter hardness for the third stage of the hierarchy. This shows that even robust problems which are already NP-complete can still exhibit a significant difference in computational complexity between column-wise and row-wise uncertainty.