Three-phase Algorithm Research Articles

A three-phase algorithm for the pollution traveling Salesman problem

This paper studies a variant of the Pollution Traveling Salesman Problem (PTSP) focused on fuel consumption and pollution emissions (PTSPC). The PTSPC generalizes the well-known Traveling Salesman Problem (TSP), classified as NP-Hard. In the PTSPC, a vehicle must deliver a load to each customer through a Hamiltonian cycle, minimizing an objective function that considers the speed of each edge, the mass of the truck, the mass of the load pending delivery, and the distance traveled. We have proposed a three-phase algorithm for the PTSPC. The first phase solves the Traveling Salesman Problem (TSP) exactly with a time limit and heuristically using a Nearest Neighborhood Search approach. This phase considers the constraints associated with the PTSPC by using commercial software. In the second phase, both the obtained solutions and their inverse sequences from the initial phase undergo enhancement utilizing metaheuristic algorithms tailored for the PTSPC. These algorithms include Variable Neighborhood Search (VNS), Tabu Search (TS), and Simulated Annealing (SA). Subsequently, for the third phase, the best solution identified in the second phase—determined by having the minimum value by the PTSPC objective function—is subjected to resolution by a mathematical model designed for the PTSPC, considering the heuristic emphasis of commercial software. The efficiency of the former algorithm has been validated through experimentation involving the adaptation of instances from the Pollution Routing Problem (PRP) to the PTSPC. This approach demonstrates the capacity to yield high-quality solutions within acceptable computing times.

On Optimizing Ensemble Models using Column Generation

AbstractIn recent years, an interest appeared in integrating various optimization algorithms in machine learning. We study the potential of ensemble learning in classification tasks and how to efficiently decompose the underlying optimization problem. Ensemble learning has become popular for machine learning applications and it is particularly interesting from an optimization perspective due to its resemblance to column generation. The challenge for learning is not only to obtain a good fit for the training data set, but also good generalization, such that the classifier is generally applicable. Deep networks have the drawback that they require a lot of computational effort to get to an accurate classification. Ensemble learning can combine various weak learners, which individually require less computational time. We consider binary classification problems studying a three-phase algorithm. After initializing a set of base learners refined by a bootstrapping approach, base learners are generated using the solution of an linear programming (LP) master problem and then solving a machine learning sub-problem regarding a reduced data set, which can be viewed as a so-called pricing problem. We theoretically show that the algorithm computes an optimal ensemble model in the convex hull of a given model space. The implementation of the algorithm is part of an ensemble learning framework called decolearn. Numerical experiments with CIFAR-10 data set show that the base learners are diverse and that both the training and generalization error are reduced after several iterations.

An intelligent algorithm of amyloid plucks to timely fault-predicting and contending dependability in IoMT

The security problem and the demand for service availability create risky conditions for individuals connected to the Internet of Medical Things (IoMT) technology. They are vulnerable to passive and active attacks involving the sharing of malicious information and data theft. Researchers have attempted to address these issues but often overlooked the significant impact of service priority and fault prediction on their methods for improving efficiency. The characteristics are undeniable when it comes to demanding appropriate service and rectifying the negative effects caused by faulty devices in an IoMT application. This study analyzes the challenges associated with IoMT and proposes a model and a three-phase algorithm called RPD for role mapping and pattern detection (RPD). RPD has successfully overcome the challenges posed by recent algorithms in defining service priority for various IoMT applications. It is capable of accurately predicting the area of faulty components and detecting and predicting them. The algorithm's features emphasize its role in providing a dependable service based on application priority, improving efficiency in service dependability, and reducing the cost overheads of defective components. Our approach involves transferring the responsibilities of IoMT application nodes onto the vertices of a subgraph while also defining their relationships using its edges. This study is inspired by the behaviors of human brain cells in facing Alzheimer's, Parkinson's, and acute Depression. It aims to develop a graph that can effectively identify patterns for predicting the distribution of amyloid plaques, specifically GPR56, α, and β proteins. To achieve this, we utilize the RPD algorithm, which identifies related patterns in technology applications based on their service priority predicted faulty components and halts them through phagocyte operations. This work demonstrates notable improvements in energy efficiency, communication delay, and dependability compared to recent Q-learning methods, with enhancements ranging from approximately 57.3% to 59.8%, 17.8% to 19.3%, and 37% to 41%, respectively.

Detection and filling of functional holes in microservice systems: Method and infrastructure support

With the widespread use of microservices technology, a growing number of Microservice Systems (MSS) have emerged. Monolithic applications are divided into several small and independent microservices that provide functions through APIs. These microservices can be orchestrated by service composition to satisfy various user requirements: microservice functionalities are aggregated into coarse-grained solutions to provide composite functions to users. However, although various service composition approaches have been presented in many works of literature, they failed to solve the situation that no feasible solutions can be found because of missing functions in MSS, named Functional Hole (FH). As a result, the system cannot satisfy user requirements and faces Quality of Service (QoS) declines. In this paper, to reduce the impact of missing functions, we defined the FH to describe the absence of functions for MSS when it cannot satisfy user requirements. Moreover, for the first time, we proposed the Detection and Filling Problem of FH (DFPFH) in a running MSS. A three-phase algorithm with supporting infrastructure was developed to solve DFPFH. It detects FHs based on hypergraphs, fills FHs with services from an external service system at runtime, and generates suggestions for thoroughly filling FH to developers. Plenty of experiments were conducted, and the results validate our approaches’ usability, effectiveness, and performance.

A Decomposition Algorithm for Single and Multiobjective Integrated Market Selection and Production Planning

We study an integrated market selection and production planning problem. There is a set of markets with deterministic demand, and each market has a certain revenue that is obtained if the market’s demand is satisfied throughout a planning horizon. The demand is satisfied with a production scheme that has a lot-sizing structure. The problem is to decide on which markets’ demand to satisfy and plan the production simultaneously. We consider both single and multiobjective settings. The single objective problem maximizes the profit, whereas the multiobjective problem includes the maximization of the revenue and the minimization of the production cost objectives. We develop a decomposition-based exact solution algorithm for the single objective setting and show how it can be used in a proposed three-phase algorithm for the multiobjective setting. The master problem chooses a subset of markets, and the subproblem calculates an optimal production plan to satisfy the selected markets’ demand. We investigate the subproblem from a cooperative game theory perspective to devise cuts and strengthen them based on lifting. We also propose a set of valid inequalities and preprocessing rules to improve the proposed algorithm. We test the efficacy of our solution method over a suite of problem instances and show that our algorithm substantially decreases solution times for all problem instances. History: Accepted by Andrea Lodi, Area Editor for Design & Analysis of Algorithms – Discrete. Funding: This work was supported by TUBITAK [Grant 1059B191801782].

Estimation of the Seismically Induced Residual Drift of Structures from Measured Acceleration

ABSTRACT Estimating the story drift directly from acceleration records is challenging. This paper presents a systematic integration scheme involving the empirical mode decomposition of signals to estimate the earthquake-induced displacement of buildings, including their permanent deformation after an earthquake. A three-phase algorithm is proposed. In phase-1, the displacement waveform without residual displacement is estimated, then in phase-2 the residual deformation is calculated. Combining both phase-1 and phase-2, the displacement waveform is estimated. Three types of acceleration data, namely, shaking table test data, seismic response data for a building, and near-fault ground motion data, were used to validate the proposed method.

Social Media Platform having Bully Free Environment

Abstract: One of the most pernicious consequences of social media Is the Rise Of Cyberbullying And Tends To Be More Evil than traditional bullying because online records are usually available Lived on the Internet for a long time and are hard to reach Control. This article presents a three-phase algorithm. Called BullyNet to detect cyberbullying on the Social media Communication network. Use your bullying tendencies to make powerful suggestions Instructions for building a cyberbullying signed network (SN). Analyze Social media to determine their relevance to cyberbullying Considering the context in which social media exist in succession Optimize your bully score. Also, suggest centrality Measures detect and display cyberbullying from cyberbullying SNs Better than other existing metrics

재투입이 존재하는 유연흐름공정에서 주문의 총 흐름시간을 최소화하기 위한 일정계획 방법론

This study focuses on a scheduling problem in a flexible flow shop, where there are serial stages, each with identical parallel machines. We suggest heuristic algorithms for the problem to minimize the total flow time of a given set of orders. Each order is composed of multiple lots and is processed on any parallel machines at each stage. This shop has reentrant flows since products for specific orders should visit the processing stages twice. We suggest algorithms of two types. The algorithms of the first type are three-phase algorithms, and the second type's algorithm is one-phase. In the first phase of three-phase algorithms, an initial sequence is obtained using priority dispatching rules, and then a construction algorithm is used to obtain sequences for each stage in the second phase. Finally, in the third phase, lots that visit the stages for the second time are scheduled using priority dispatching rules. Computational experiments are performed on randomly generated test problems to evaluate the performance of the suggested algorithms. Results show that the order-based algorithms perform better than the lot-based algorithms and an algorithm used in practice.

Three-phase algorithms in solving full fuzzy transportation problem by using fuzzy analytical hierarchy process

Three-phase algorithms in solving full fuzzy transportation problem by using fuzzy analytical hierarchy process

Simultaneous charging station location-routing problem for electric vehicles: Effect of nonlinear partial charging and battery degradation

Recently, electric vehicles (EVs) have gained attention in the field of logistics owing to the strong support received from the government and continuous increase in social environmental awareness. Compared to traditional logistics vehicles, EVs incur additional charging costs, such as charging time and battery wear costs. In this study, the routing problem of EVs is formulated as an integer programming model based on a nonlinear charging model and practical battery wear model. Subsequently, a three-phase algorithm called CWIGALNS was proposed to solve this problem. Based on the proposed model, a series of instances was generated, showing the benefits of combining charging time, battery wear, and distribution. Finally, sensitivity analyses were systematically conducted on the wear cost and charging time under a realistic background. The results show that the optimal planning of an EV network considering time and wear costs is in line with the practical needs of EV logistics enterprises, which can help reduce the operating costs.

An Efficient Spark-Based Hybrid Frequent Itemset Mining Algorithm for Big Data

Frequent itemset mining (FIM) is a common approach for discovering hidden frequent patterns from transactional databases used in prediction, association rules, classification, etc. Apriori is an FIM elementary algorithm with iterative nature used to find the frequent itemsets. Apriori is used to scan the dataset multiple times to generate big frequent itemsets with different cardinalities. Apriori performance descends when data gets bigger due to the multiple dataset scan to extract the frequent itemsets. Eclat is a scalable version of the Apriori algorithm that utilizes a vertical layout. The vertical layout has many advantages; it helps to solve the problem of multiple datasets scanning and has information that helps to find each itemset support. In a vertical layout, itemset support can be achieved by intersecting transaction ids (tidset/tids) and pruning irrelevant itemsets. However, when tids become too big for memory, it affects algorithms efficiency. In this paper, we introduce SHFIM (spark-based hybrid frequent itemset mining), which is a three-phase algorithm that utilizes both horizontal and vertical layout diffset instead of tidset to keep track of the differences between transaction ids rather than the intersections. Moreover, some improvements are developed to decrease the number of candidate itemsets. SHFIM is implemented and tested over the Spark framework, which utilizes the RDD (resilient distributed datasets) concept and in-memory processing that tackles MapReduce framework problem. We compared the SHFIM performance with Spark-based Eclat and dEclat algorithms for the four benchmark datasets. Experimental results proved that SHFIM outperforms Eclat and dEclat Spark-based algorithms in both dense and sparse datasets in terms of execution time.

Modeling of discrete questionnaire data with dimension reduction

The paper deals with the task of modeling discrete questionnaire data with a reduced dimension of the model. The discrete model dimension is reduced using the construction of local models based on independent binomial mixtures estimated with the help of recursive Bayesian algorithms in the combination with the naive Bayes technique. The main contribution of the paper is a three-phase algorithm of the discrete model dimension reduction, which allows to model high-dimensional questionnaire data with high number of explanatory variables and their possible realizations. The proposed general solution is applied to the traffic accident questionnaire analysis, where it takes the form of the classification of the accident circumstances and prediction of the traffic accident severity using the currently measured discrete data. Results of testing the obtained model on real data and comparison with theoretical counterparts are demonstrated.

Nonsubmodular Constrained Profit Maximization in Attribute Networks

The number of social individuals who interact with their friends through social networks is increasing, leading to an undeniable fact that word-of-mouth marketing has become one of the useful ways to promote sale of products. The Constrained Profit Maximization in Attribute network (CPMA) problem, as an extension of the classical influence maximization problem, is the main focus of this paper. We propose the profit maximization in attribute network problem under a cardinality constraint which is closer to the actual situation. The profit spread metric of CPMA calculates the total benefit and cost generated by all the active nodes. Different from the classical Influence Maximization problem, the influence strength should be recalculated according to the emotional tendency and classification label of nodes in attribute networks. The profit spread metric is no longer monotone and submodular in general. Given that the profit spread metric can be expressed as the difference between two submodular functions and admits a DS decomposition, a three-phase algorithm named as Marginal increment and Community-based Prune and Search(MCPS) Algorithm frame is proposed which is based on Louvain algorithm and logistic function. Due to the method of marginal increment, MPCS algorithm can compute profit spread more directly and accurately. Experiments demonstrate the effectiveness of MCPS algorithm.

Vehicle routing and capacity planning : A three-phase algorithm

In this paper, a three-phase algorithm is designed for solving a real problem of vehicle capacity and route optimization. The proposed algorithm fulfills non-stochastic demand in a limited time window by optimally allocating vehicles of heterogeneous capacity. The pricing model of the supplier (CMMF- a popular Co-operative Milk Marketing Federation of southern India) to transport fodder from the central production facility to different destinations was not very encouraging for the vehicle owners. In CMMF’s pricing model, it is not possible to reduce the cost of transportation by vehicle capacity optimization or route optimization. CMMF was using the manual system of assignment of the vehicle to different demand locations often resulting in vehicles traveling longer distances with unused capacity. Management saw an opportunity to optimally load vehicles and fulfill various demand locations in such a way that the vehicle travels minimum distance with unused capacity. The proposed algorithm is tested on a sample 100 node network with actual demands and relevant data like demand arrival date, a connected network of demand locations, distance of demand locations from the source, and list of vehicles that are used in the existing system. A decision support system is developed to prescribe solutions to the decision-maker with different threshold unused capacity (5%, 10%, 15%, and 20%) of vehicles. Subject to the imposed constraints, a considerable reduction in the unused capacity (Kilogram-Kilometer) of vehicles is recorded.

Three-Phase State Estimation of a Low-Voltage Distribution Network with Kalman Filter

The state estimation of distribution networks has long been considered a challenging task for the reduced availability of real-time measures with respect to the transmission network case. This issue is expected to be improved by the deployment of modern smart meters that can be polled at relatively short time intervals. On the other hand, the management of the information coming from many heterogeneous meters still poses major issues. If low-voltage distribution systems are of interest, a three-phase formulation should be employed for the state estimation due to the typical load imbalance. Moreover, smart meter data may not be perfectly synchronized. This paper presents the implementation of a three-phase state estimation algorithm of a real portion of a low-voltage distribution network with distributed generation equipped with smart meters. The paper compares the typical state estimation algorithm that implements the weighted least squares method with an algorithm based on an iterated Kalman filter. The influence of nonsynchronicity of measurements and of delays in communication and processing is analyzed for both approaches.

Low-Latency, Three-Phase PMU Algorithms: Review and Performance Comparison

Phasor Measurement Units are the most advanced instruments for power network monitoring, since they allow phasors, frequency and rate of change of frequency (ROCOF) to be measured in predetermined time instants with respect to an absolute time reference. The employed estimation algorithm plays a key role in overall performance under off-nominal conditions; the challenge to be faced is combining high steady-state accuracy with fast responsiveness in dynamic conditions, small reporting latency and reduced computational burden. Under regular operation, AC power networks are weakly unbalanced three-phase systems. Based on this consideration, the recent literature has proposed native three-phase estimation algorithms that effectively exploit this property to accurately identify the positive sequence synchrophasor, frequency and ROCOF. In this respect, the present paper describes three among the most promising three-phase algorithms based on the Space Vector transformation. By means of numerical simulations, it compares the achieved performance in terms of response time and estimation error both under steady-state and dynamic conditions. All the considered approaches enable a flexible design that allows balancing accuracy and responsiveness. For this analysis, the reporting latency has been limited to about one and half nominal cycles, i.e., 30 ms at 50 Hz; the P-class algorithm suggested by IEC/IEEE Std 60255-118-1 has also been included as comparison benchmark.

BullyNet: Unmasking Cyberbullies on Social Networks

One of the most harmful consequences of social media is the rise of cyberbullying, which tends to be more sinister than traditional bullying, given that online records typically live on the Internet for quite a long time and are hard to control. In this article, we present a three-phase algorithm, called BullyNet, for detecting cyberbullies on Twitter social network. We exploit bullying tendencies by proposing a robust method for constructing a cyberbullying signed network (SN). We analyze tweets to determine their relation to cyberbullying while considering the context in which the tweets exist in order to optimize their bullying score. We also propose a centrality measure to detect cyberbullies from a cyberbullying SN and show that it outperforms other existing measures. We experiment on a data set of 5.6 million tweets, and our results show that the proposed approach can detect cyberbullies with high accuracy while being scalable with respect to the number of tweets.

Making Neighbors Quiet: An Approach to Detect Virtual Resource Contention

It is imperative for public cloud providers to guarantee performance targets for tenants' virtual machines (VMs) while respecting strict business confidentiality, e.g., having no information on applications nor their performance. A large body of related work addresses the challenges of detecting performance interferences by leveraging client's quality of service (QoS) metrics, e.g., latency, and additional profiling servers. In this paper, we take the perspective of the cloud provider and propose a general black-box approach that detects different resource contentions by throttling neighboring VMs. Specifically, we design a three-phase detection algorithm that includes: (i) an alarm phase to identify statistical outliers using control charts; (ii) a passive clustering phase to match the current sample to historical behaviors; and (iii) an active throttling phase to discern contentions from application phase changes via throttling. The algorithm is specifically designed for scenarios where multiple co-located VMs request detection analysis simultaneously. We implement and evaluate the proposed three-phase algorithm on four latency sensitive applications, i.e., Wikimedia and three benchmarks from Cloudsuite. Our extensive experimental results show that we can reach an average detection accuracy above 90 percent while limiting the performance degradation experienced by offender workloads to short learning phases.

The commodity-split multi-compartment capacitated arc routing problem

The purpose of this paper is to develop a data-driven matheuristic for the Commodity-Split Multi-Compartment Capacitated Arc Routing Problem (CSMC-CARP). This problem arises in curbside waste collection, where there are different recyclable waste types called fractions. The CSMC-CARP is defined on an undirected graph with a limited heterogeneous fleet of multi-compartment vehicle types based at a depot, where each compartment’s capacity can vary depending on the waste fraction assigned to it and on the compression factor of that fraction in that vehicle type. The aim is to determine a set of least-cost routes starting and ending at the depot, such that the demand of each edge for each waste fraction is collected exactly once by one vehicle, without violating the capacity of any compartment. The CSMC-CARP consists of three decision levels: selecting the number of vehicles of each type, assigning waste fractions to the compartments of each selected vehicle, and routing the vehicles. Our three-phase algorithm decomposes the problem into incomplete solution representations and heuristically solves one or more decision levels at a time. The first phase selects a subset of attractive compartment assignments from all assignments of all vehicle types. The second phase solves the CSMC-CARP with an unlimited fleet of the selected assignments. This is done by our C-split tour splitting algorithm, which can simultaneously split a giant tour of required edges into feasible routes while making decisions on the fractions that are collected by each route. The third phase selects the set of best routes servicing all fractions of all required edges without exceeding the number of vehicles available of each type. The algorithm is applied to real-life instances arising from recyclable waste collection operations in Denmark, with graph sizes up to 6,149 nodes and 3,797 required edges, the waste sorted in three to six fractions, and four to six vehicle types with one to four compartments. Computational results show that the generated solutions favor combining different fractions together in vehicles with higher numbers of compartments, and that the algorithm adapts well to the characteristics of the data, in terms of the graph, vehicle types, degree of sorting, and to skewness in demand among waste fractions.

A Three-Phase, One-Tap High Background Light Subtraction Time-of-Flight Camera

In this paper, a time-of-flight 3-D camera is presented for indoor/outdoor high-speed applications. A three-phase algorithm is proposed using one-tap/capacitor in-pixel background light subtraction. By integrating the background light only once, the standard four-phase time-of-flight operation is done in three-phases. The use of a single tap in the proposed pixel provides background light subtraction with lower mismatch and additionally, increases the camera speed. A $24 \times 24 $ pixel array image sensor, fabricated in AMS 0.35- $\mu \text{m}$ CMOS process is characterized in 2-D and 3-D mode for background light subtraction and distance estimation. The pixel pitch is $48~\mu \text{m}$ with 17.36% fill-factor. The sensor subtracts 125-klx background light with 8.2-cm distance offset at 80-cm object distance, without using an optical filter. The background-to-signal-ratio is 32.04 dB while frame-rate of the camera is 207 and 100 fps in 2-D and 3-D mode, respectively.