Greedy Heuristic Method Research Articles

A scenario-based metaheuristic and optimization framework for cost-effective machine-trail network design in forestry

Designing an optimal machine trail network is a complex locational problem that requires an understanding of different machines’ operations and terrain features as well as the trade-offs between various objectives. With the overall goal to minimize the operational costs of the logging operation, this paper proposes a mathematical optimization model for the trail network design problem and a greedy heuristic method based on different randomized search scenarios aiming to find the optimal location of machine trails —with potential to reduce negative environmental impact. The network is designed so that all trees can be reached and adapted to how the machines can maneuver while considering the terrain elevation’s influence. To examine the effectiveness and practical performance of the heuristic and the optimization model, it was applied in a case study on four harvest units with different topologies and shapes. The computational experiments show that the heuristic can generate solutions that outperform the solutions corresponding to conventional, manual designs within practical time limits for operational planning. Moreover, to highlight certain features of the heuristic and the parameter settings’ effect on its performance, we present an extensive computational sensitivity analysis.

An anonymization-based privacy-preserving data collection protocol for digital health data.

Digital health data collection is vital for healthcare and medical research. But it contains sensitive information about patients, which makes it challenging. To collect health data without privacy breaches, it must be secured between the data owner and the collector. Existing data collection research studies have too stringent assumptions such as using a third-party anonymizer or a private channel amid the data owner and the collector. These studies are more susceptible to privacy attacks due to third-party involvement, which makes them less applicable for privacy-preserving healthcare data collection. This article proposes a novel privacy-preserving data collection protocol that anonymizes healthcare data without using a third-party anonymizer or a private channel for data transmission. A clustering-based k-anonymity model was adopted to efficiently prevent identity disclosure attacks, and the communication between the data owner and the collector is restricted to some elected representatives of each equivalent group of data owners. We also identified a privacy attack, known as "leader collusion", in which the elected representatives may collaborate to violate an individual's privacy. We propose solutions for such collisions and sensitive attribute protection. A greedy heuristic method is devised to efficiently handle the data owners who join or depart the anonymization process dynamically. Furthermore, we present the potential privacy attacks on the proposed protocol and theoretical analysis. Extensive experiments are conducted in real-world datasets, and the results suggest that our solution outperforms the state-of-the-art techniques in terms of privacy protection and computational complexity.

Sensor Selection with Composite Features in Identifying User-Intended Poses for Human-Prosthetic Interfaces.

A Human-Prosthetic Interface (HPI) serves to estimate and realise the limb pose intended by the human user, using the information obtained from sensors worn by the user. In recent studies, the HPI maps multi-joint limb poses (i.e. coordinated movement of the body and limbs) to the inputs of multiple sensors. This is in contrast to the conventional methods where each degree of freedom of the powered prosthesis is mapped to the input of one/a pair of sensors. In this approach, it is necessary to systematically select sensors that carry the most information for the intended set of poses, to improve system accuracy and/or minimise the number of sensors, thus the complexity, in the prosthetic system. In this paper, sensor selection process is systematically formulated to maximise the information contained in the input features for a given number of sensors. Most importantly, it accounts for composite features, which are features requiring information from multiple sensors. Such composite features exist and are important in HPIs as we seek to capture coordinated motion involving movements of multiple limb and body segments. A non-convex optimisation problem is formulated which accounts for the constraint introduced by the composite features. A projection matrix is utilised as the optimisation variable to select intended features for evaluation. The problem is solved by the proposed Sensor Selection with Composite Features (SS-CF) algorithm which adapts convex-relaxation techniques. The SS-CF is benchmarked against HPI with expert-selected sensors in the literature and against a greedy heuristic method. The outcome demonstrated the efficacy of the SS-CF algorithm.

Hybrid transportation system using trucks and drones

In recent years, the use of drones has gained significant attention as a potential solution for delivering goods and services in a variety of industries. Drones offer advantages over traditional delivery methods, including faster delivery times, increased efficiency, and reduced costs. However, their adoption has been limited by challenges related to their operation and regulation, as well as by the complexity of integrating them into existing transportation networks. To address some of these challenges, we present a novel approach to dispatching a hybrid truck and drone delivery system for serving a set of clients. Our proposal uses OR-Tools, an open-source optimization toolkit, for providing an initial truck tour, and a greedy heuristic method for introducing drone service into this route. We consider that at the starting point, the drone is on the truck. During the tour, the hybrid tandem of vehicles ensures that all the nodes of the initial tour are visited, either by the truck, or by the drone. When both vehicles arrive at final node, the time needed is computed. To evaluate the effectiveness of our proposal, we tested it on an instance of the Traveling Salesman Problem in Romania and compared our results with those based on the Concorde solver, a widely used exact algorithm for solving this problem. Our results demonstrate that our solution is stable, and performs well, indicating that our approach can be a useful tool for efficiently programming the service of hybrid delivery systems.

Joint Antenna Placement and Power Allocation for Target Detection in a Distributed MIMO Radar Network

Radar network configuration and power allocation are of great importance in military applications, where the entire surveillance area needs to be searched under resource budget constraints. To pursue the joint antenna placement and power allocation (JAPPA) optimization, this paper develops a JAPPA strategy to improve target detection performance in a widely distributed multiple-input and multiple-output (MIMO) radar network. First, the three variables of the problem are incorporated into the Neyman–Pearson (NP) detector by using the antenna placement optimization and the Lagrange power allocation method. Further, an improved iterative greedy dropping heuristic method based on a two-stage local search is proposed to solve the NP-hard issues of high-dimensional non-linear integer programming. Then, the sum of the weighted logarithmic likelihood ratio test (LRT) function is constructed as optimization criteria for the JAPPA approach. Numerical simulations and the theoretical analysis confirm the superiority of the proposed algorithm in terms of achieving effective overall detection performance.

From pairwise to multiple spliced alignment.

Alternative splicing is a ubiquitous process in eukaryotes that allows distinct transcripts to be produced from the same gene. Yet, the study of transcript evolution within a gene family is still in its infancy. One prerequisite for this study is the availability of methods to compare sets of transcripts while accounting for their splicing structure. In this context, we generalize the concept of pairwise spliced alignments (PSpAs) to multiple spliced alignments (MSpAs). MSpAs have several important purposes in addition to empowering the study of the evolution of transcripts. For instance, it is a key to improving the prediction of gene models, which is important to solve the growing problem of genome annotation. Despite its essentialness, a formal definition of the concept and methods to compute MSpAs are still lacking. We introduce the MSpA problem and the SplicedFamAlignMulti (SFAM) method, to compute the MSpA of a gene family. Like most multiple sequence alignment (MSA) methods that are generally greedy heuristic methods assembling pairwise alignments, SFAM combines all PSpAs of coding DNA sequences and gene sequences of a gene family into an MSpA. It produces a single structure that represents the superstructure and models of the gene family. Using real vertebrate and simulated gene family data, we illustrate the utility of SFAM for computing accurate gene family superstructures, MSAs, inferring splicing orthologous groups and improving gene-model annotations. The supporting data and implementation of SFAM are freely available at https://github.com/UdeS-CoBIUS/SpliceFamAlignMulti. Supplementary data are available at Bioinformatics Advances online.

Travelling Salesman Problem Analysis with Complete Enumeration Method, Branch & Bound and Greedy Heuristic

PT XYZ as the best and largest Bed Sheet Set company in Indonesia with products such as Bed Covers, Bed Sheets, Pillowcases, Bolsters and Blankets. The Traveling Salesman Problem (TSP) is a problem faced in finding the best route to visit shops that sell products from PT BIG. A visit to the shop is carried out on the condition that each city can only be visited once except the city of origin. The algorithms applied in this TSP problem include the Complete Enumeration, Branch & Bound and Greedy Heuristic methods.

Travelling Salesman Problem Analysis with Complete Enumeration Method, Branch & Bound and Greedy Heuristic

PT XYZ as the best and largest Bed Sheet Set company in Indonesia with products such as Bed Covers, Bed Sheets, Pillowcases, Bolsters and Blankets. The Traveling Salesman Problem (TSP) is a problem faced in finding the best route to visit shops that sell products from PT BIG. A visit to the shop is carried out on the condition that each city can only be visited once except the city of origin. The algorithms applied in this TSP problem include the Complete Enumeration, Branch & Bound and Greedy Heuristic methods.

Peak temperature analysis and optimization for pipelined hard real-time systems

This study aims to minimize the peak temperature for pipelined multi-core systems while providing hard real-time guarantees. Periodic Thermal Management (PTM) is adopted to control the temperature by periodically switching each pipelined stage into a lower power mode. The Pay-Burst-Only-Once principle from Real-Time Calculus theory is used in reverse to transform real-time guarantees into the constraints of the PTM schemes applied to all stages. We systematically study the peak temperature under PTM and present two algorithms to calculate it with different levels of accuracy and speed. A greedy principle-based heuristic method is proposed to solve the peak temperature optimization problem. Experiments are conducted on an Intel processor with physical temperature sensors. The results demonstrate that our approaches optimize the peak temperature more effectively than the sub-deadline partition approach. Simulations for scenarios involving more stages reveal that the proposed algorithms are scalable with respect to the number of stages.

Non-linear biobjective EE-SE optimization for NOMA-MIMO systems under user-rate fairness and variable number of users per cluster

The trade-off between energy efficiency (EE) and spectrum efficiency (SE) in downlink (DL) multiple-input and multiple-output (MIMO) non-orthogonal multiple access (NOMA) systems under equal-rate constraint has been investigated. The maximum fairness of the NOMA-MIMO system is attained when it is assured the same data rate for all active users in the system. The SE-EE trade-off was formulated as a multi-objective (MOO) problem. The optimization problem was solved by using primarily ∊-Constraint (∊-C) scalarization method combined with nonlinear programming (NLP) techniques, as sequential quadratic programming (SQP) and Dinkelback method (DK). The proposed iterative-analytical ∊-C-DK and ∊-C-SQP methods were able to achieve optimal EE-SE solutions at the Pareto frontier. Besides, both proposed iterative-analytical optimization methods are compared with a greedy local search heuristic method, by combining the ∊-C with the Hill Climbing (HC) heuristic and weighted sum (WS) scalarization method. Numerical results have demonstrated the ability of the ∊-C method in finding a diversity of EE-SE NOMA-MIMO solutions in the Pareto frontier. Furthermore, the proposed ∊-C-SQP method is able to attain a much more variety of solutions in the Pareto frontier in a remarkably reduced computational time.

Heuristics for k-domination models of facility location problems in street networks

We present new greedy and beam search heuristic methods to find small-size k-dominating sets in graphs. The methods are inspired by a new problem formulation which explicitly highlights a certain structure of the problem. An empirical evaluation of the new methods is done with respect to two existing methods, using instances of graphs corresponding to street networks. The k-domination problem with respect to this class of graphs can be used to model real-world facility location problem scenarios. For the classic minimum dominating set (1-domination) problem, all except one methods perform similarly, which is due to their equivalence in this particular case. However, for the k-domination problem with k>1, the new methods outperform the benchmark methods, and the performance gain is more significant for larger values of k.

Comparing greedy constructive heuristic subtour elimination methods for the traveling salesman problem

Purpose This paper aims to define the class of fragment constructive heuristics used to compute feasible solutions for the traveling salesman problem (TSP) into edge-greedy and vertex-greedy subclasses. As these subclasses of heuristics can create subtours, two known methodologies for subtour elimination on symmetric instances are reviewed and are expanded to cover asymmetric problem instances. This paper introduces a third novel subtour elimination methodology, the greedy tracker (GT), and compares it to both known methodologies. Design/methodology/approach Computational results for all three subtour elimination methodologies are generated across 17 symmetric instances ranging in size from 29 vertices to 5,934 vertices, as well as 9 asymmetric instances ranging in size from 17 to 443 vertices. Findings The results demonstrate the GT is the fastest method for preventing subtours for instances below 400 vertices. Additionally, a distinction between fragment constructive heuristics and the subtour elimination methodology used to ensure the feasibility of resulting solutions enables the introduction of a new vertex-greedy fragment heuristic called ordered greedy. Originality/value This research has two main contributions: first, it introduces a novel subtour elimination methodology. Second, the research introduces the concept of ordered lists which remaps the TSP into a new space with promising initial computational results.

OHTMA: an optimized heuristic topology-aware mapping algorithm on the Tianhe-3 exascale supercomputer prototype

With the rapid increase of the size of applications and the complexity of the supercomputer architecture, topology-aware process mapping becomes increasingly important. High communication cost has become a dominant constraint of the performance of applications running on the supercomputer. To avoid a bad mapping strategy which can lead to terrible communication performance, we propose an optimized heuristic topology-aware mapping algorithm (OHTMA). The algorithm attempts to minimize the hop-byte metric that we use to measure the mapping results. OHTMA incorporates a new greedy heuristic method and pair-exchange-based optimization. It reduces the number of long-distance communications and effectively enhances the locality of the communication. Experimental results on the Tianhe-3 exascale supercomputer prototype indicate that OHTMA can significantly reduce the communication costs.

Application of Algorithms with Variable Greedy Heuristics for k-Medoids Problems

Progress in location theory methods and clustering algorithms is mainly targeted at improving the performance of the algorithms. The most popular clustering models are based on solving the p-median and similar location problems (k-means, k-medoids). In such problems, the algorithm must find several points called cluster centers, centroids, medoids, depending on the specific problem which minimize some function of distances from known objects to the centers. In the the k-medoids problem, the centers (medoids) of the cluster must coincide with one of the clustered objects. The problem is NP-hard, and the efforts of researchers are focused on the development of compromise heuristic algorithms that provide a fairly quick solution with minimal error. In this paper, we propose new algorithms of the Greedy Heuristic Method which use the idea of the Variable Neighborhood Search (VNS) algorithms for solving the k-medoids problem (which is also called the discrete p-median problem). In addition to the known PAM (Partition Around Medoids) algorithm, neighborhoods of a known solution are formed by applying greedy agglomerative heuristic procedures. According to the results of computational experiments, the new search algorithms (Greedy PAM-VNS) give more accurate and stable results (lower average value of the objective function and its standard deviation, smaller spread) in comparison with known algorithms on various data sets. Povzetek: Avtorji predlagajo nove algoritme za resevanje problema lokacije k-medoidov in grozdenja.

Risk-averse flexible policy on ambulance allocation in humanitarian operations under uncertainty

Proactive ambulance management is constructive to improve the response efficiency for emergency medical service (EMS) systems under uncertainty. In this paper, we present a dynamic optimisation model concerning the ambulance dispatching and relocation. We develop a flexible operation policy driven by the interval rolling to match vehicles with calls in batch. We formulate the problem in Markov Decision Process and incorporate queues to minimise the average response and delay time. Considering the curse-of-dimensionality, we provide a simulation-based empirical dynamic programming with the state aggregation and post-decision state to solve the model. To further accelerate the computational efficiency, a greedy heuristic method is introduced to improve the quality of sampling operations. Then, a risk-averse model is developed based on the stochastic dominance strategy to improve operational reliability. We develop an equivalent linear programming to evaluate concave dominating functions. We test the performance by a numerical case and extract managerial insights for practitioners. Our results show that the proposed flexible and risk-averse solution outperforms the classic model on reducing the delay under uncertain calls. And the improvement is more active during peak hours, when real-time needs exceed available ambulances.

A greedy evolutionary hybridization algorithm for the optimal network and quadratic assignment problem

Our paper deals with a combinatorial optimization problem called the optimal network and quadratic assignment problem. The problem has been introduced by Los (Region Sci Urban Econ 8:21–42, 1978) as a model of an urban planning problem that consists in optimizing simultaneously the best location of the activities of an urban area (land-use), as well as the road network design (transportation network) in such a way to minimize as much as possible the routing and network costs. We propose a mixed-integer programming formulation of the problem, and a hybrid algorithm based on greedy and evolutionary heuristic methods. Some numerical experiments on randomly generated instances, and on real-life big data from Dakar city, show the efficiency of the method.

Solving Assembly Production Line Balancing Problem Using Greedy Heuristic Method

This paper focuses on assembly line balancing (ALB) problem which the objective is to maximize the assembly line efficiency. The problem solved using a greedy heuristic method. MATLAB Software is used to perform the proposed greedy heuristic method. Then, the proposed method is applied to a previous real life case problem that is found in literature for the cookers assembly line in the Light Industrial Company in Iraq. The results of the proposed method is compared with the performance of the company without applying any assembly line balancing method. The result of this paper is also compared with the results obtained from a previous work in the literature which is based on Shortest Operation Time (SOT) using the software named Quantitative Methods, Production and Operations Management (POM-QM). The outcomes of the research have shown that the greedy heuristic method is more efficient, where the efficiency increased from 78.24% before applying assembly line balancing and 81.64% for the previous work based on SOT method to 85.53% when applying the greedy heuristic method. The research has recommended that the decision maker in the company should follow the most appropriate method to achieve a high efficiency operations.

A Hierarchical Vehicular-Based Architecture for Vehicular Networks: A Case Study on Computation Offloading

In order to realize an intelligent transportation system (ITS) which will provide smooth urban traffic, autonomous driving, accurate route navigation, etc., enormous computations need to be migrated from cloud centers to edge nodes, especially for the services requiring stringent latency. In addition to base stations and road side units (RSUs), vehicles can be alteratively considered as a kind of computation resources. In this article, a hierarchical vehicular-based architecture which consists of cloud centers and vehicles is investigated. Computation offloading performance in the hierarchical architecture is also studied. In specific, the main components in vehicular networks and their characteristics on communication and computations are presented firstly. Several communication techniques that are essential in enabling computation offloading among these components are then discussed. Secondly, a hierarchical vehicular-based architecture, which integrates the main components, is constructed. Thirdly, a case study on computation offloading in the proposed architecture is conducted. In the concerned scenario, the computation offloading problem is modelled as a multi-dimensional multiple knapsack problem (MMKP). Two algorithms are investigated, among which, the first algorithm is a greedy heuristic method providing a sub-optimal solution with a low computational complexity. The second algorithm is a modified branch and bound (B&B) method, which can obtain the best solution with a high computational complexity. Numerical results are also presented to verify the performance of the two algorithms. It can be demonstrated that the proposed architecture can migrate more computations from cloud centers to vehicular nodes, when the computations require more communication resources.

VNS-BASED ALGORITHMS FOR THE CENTROID-BASED CLUSTERING PROBLEM

The k-means algorithm with the corresponding problem formulation is one of the first methods that researchers use when solving a new automatic grouping (clus-tering) problem. Its improvement, modification and combination with other algorithms are described in the works of many researchers. In this research, we propose new al-gorithms of the Greedy Heuristic Method, which use an idea of the search in variable neighborhoods for solving the classical cluster analysis problem, and allows us to obtain a more accurate and stable result of solving in comparison with the known algorithms. Our computational experiments show that the new algorithms allow us to obtain re-sults with better values of the objective function value (sum of squared distances) in comparison with classical algorithms such as k-means, j-means and genetic algorithms on various practically important datasets. In addition, we present the first results for the GPU realization of the Greedy Heuristic Method.

Evolutionary Algorithms for Optimizing Cost and QoS on Cloud-based Content Distribution Networks

Content Distribution Networks (CDN) are key for providing worldwide services and content to end-users. In this work, we propose three multiobjective evolutionary algorithms for solving the problem of designing and optimizing cloud-based CDNs. We consider the objectives of minimizing the total cost of the infrastructure (including virtual machines, network, and storage) and the maximization of the quality-of-service provided to end-users. The proposed model considers a multi-tenant approach where a single cloud-based CDN is able to host multiple content providers using a resource sharing strategy. The proposed evolutionary algorithms address the offline problem of provisioning infrastructure resources while a greedy heuristic method is proposed for addressing the online problem of routing contents. The experimental evaluation of the proposed methods is performed over a set of realistic problem instances. Results indicate that the proposed approach is effective for designing and optimizing cloud-based CDNs reducing total costs by up to 10.3% while maintaining an adequate quality of service.