Two-phase Algorithm Research Articles

Optimisation of the national grain reserve system using a two-phase algorithm

ABSTRACT The local grain reserve system is widely recognised as the key to ensure Chinese grain security in response to emergency events. Hence, the government should optimize the amounts and locations of grain reserves. Nevertheless, the grain supply process for emergencies is hard to be analytically model due to the complexity and uncertainty. In this paper, we propose an off-site storage structure to balance the high storage cost and the lack of storage capacity. Based on the off-site storage structure, we build a simulation model of the local grain reserve system and develop a systematic two-phase optimisation algorithm to achieve the optimal scheme. The numerical results show that the optimal off-site grain storage scheme can reduce the total annual operation cost of the entire system by 16%. Finally, other managerial suggestions are proposed for the government to build a more efficient local grain reserve system.

A Slack Departure Strategy for Demand Responsive Transit Based on Bounded Rationality

Demand responsive transit (DRT) is emerging as one of the most potential travel modes to satisfy flexible travel demands. Nevertheless, how to attract more passengers has become a critical problem in the success of DRT projects. It is necessary to take into account the psychological factors impacting passengers’ choices. The study proposes a slack departure strategy considering boundedly rational passengers, which introduces passengers’ decision-making psychology into the optimization process. The strategy can adjust the departure time of passengers to adjacent time windows. The discount-incentive mechanism is presented to attract passengers to accept the changes while maintaining the quality of service. On this basis, the theory of bounded rationality is applied to describe the decision-making process of passengers. We construct a multiobjective programming model to analyze the operator-passenger interactive effect. To address the multiobjective problem, a two-phase heuristic algorithm is established to get the Pareto solution for the model. A numerical experiment is carried out on the Sioux Falls network. The case study of Beijing is discussed to evaluate the effectiveness of the strategy. The results indicate that the slack departure strategy can significantly benefit both the operators and passengers. The operating profit substantially increases by up to 63%. Meanwhile, the passenger’s general travel cost declines by 12%. The optimal discount rate of the incentive mechanism is 20%. Therefore, the study contributes to comprehending the passenger’s decision-making psychology and providing a new optimization strategy for the operator of DRT.

A novel 2-phase residual U-net algorithm combined with optimal mass transportation for 3D brain tumor detection and segmentation

Utilizing the optimal mass transportation (OMT) technique to convert an irregular 3D brain image into a cube, a required input format for a U-net algorithm, is a brand new idea for medical imaging research. We develop a cubic volume-measure-preserving OMT (V-OMT) model for the implementation of this conversion. The contrast-enhanced histogram equalization grayscale of fluid-attenuated inversion recovery (FLAIR) in a brain image creates the corresponding density function. We then propose an effective two-phase residual U-net algorithm combined with the V-OMT algorithm for training and validation. First, we use the residual U-net and V-OMT algorithms to precisely predict the whole tumor (WT) region. Second, we expand this predicted WT region with dilation and create a smooth function by convolving the step-like function associated with the WT region in the brain image with a 5times 5times 5 blur tensor. Then, a new V-OMT algorithm with mesh refinement is constructed to allow the residual U-net algorithm to effectively train Net1–Net3 models. Finally, we propose ensemble voting postprocessing to validate the final labels of brain images. We randomly chose 1000 and 251 brain samples from the Brain Tumor Segmentation (BraTS) 2021 training dataset, which contains 1251 samples, for training and validation, respectively. The Dice scores of the WT, tumor core (TC) and enhanced tumor (ET) regions for validation computed by Net1–Net3 were 0.93705, 0.90617 and 0.87470, respectively. A significant improvement in brain tumor detection and segmentation with higher accuracy is achieved.

An extensive search algorithm to find feasible healthy menus for humans.

Promoting healthy lifestyles is nowadays a public priority among most public entities. The ability to design an array of nutritious and appealing diets is very valuable. Menu Planning still presents a challenge which complexity derives from the problems’ many dimensions and the idiosyncrasies of human behavior towards eating. Among the difficulties encountered by researchers when facing the Menu Planning Problem, being able of finding a rich feasible region stands out. We consider it as a system of inequalities to which we try to find solutions. We have developed and implemented a two-phase algorithm -that mainly stems from the Randomized Search and the Genetic- that is capable of rapidly finding an pool of solutions to the system with the aim of properly identifying the feasible region of the underlying problem and proceed to its densification. It consists of a hybrid algorithm inspired on a GRASP metaheuristic and a later recombination. First, it generates initial seeds, identifying best candidates and guiding the search to create solutions to the system, thus attempting to verify every inequality. Afterwards, the recombination of different promising candidates helps in the densification of the feasible region with new solutions. This methodology is an adaptation of other previously used in literature, and that we apply to the MPP. For this, we generated a database of a 227 recipes and 272 ingredients. Applying this methodology to the database, we are able to obtain a pool of feasible (healthy and nutritious) complete menus for a given D number of days.

Learning Algorithms for Complete Targets Coverage in RF-Energy Harvesting Networks

A key task in Internet of Things (IoTs) networks is determining the active time of a set of devices or set covers to monitor static targets. This task, however, requires the battery level of devices. However, in practice, it is impractical to obtain an accurate battery level or channel state information from all devices, especially in large-scale networks. To this end, we present a number of approaches to construct set covers. We first propose a Two-Phase Algorithm (TPA) that requires devices to first determine their probability of being active in each time slot. This probability is then used by a Hybrid Access Point (HAP) to construct set covers. We then introduce learning approaches based on Gibbs and Thompson sampling. The Gibbs sampling based algorithm, aka GB, allows a sink/gateway to learn the best set cover to use over time. Similarly, our Thompson sampling solutions, namely TS-Random and TS-CB, construct set covers iteratively based on the probability that a device successfully monitors static targets. The numerical results show that GB performs better than TS-CB initially but has similar performance to TS-CB in the long term.

Fast multi-phase gain matrix computation for real time distribution system state estimation

Efficient distribution system state estimation (DSSE) is becoming increasingly important in modern grids. Building the gain matrix is carried out at every iteration of the weighted least squares (WLS) DSSE algorithm that employs the normal equations approach, and the computation of this matrix occupies a substantial portion of the total execution time. This paper presents a two-phase algorithm for the efficient gain matrix calculation suitable for modern CPU architectures. The preparation phase creates the necessary structures for effective data streaming and code execution. The gain matrix is calculated in the second phase using the prepared data and single instruction multiple data (SIMD) intrinsics. The Jacobian measurement matrix is organized as a sparse matrix of small dense blocks with double-precision complex values. The implementation exploits memory-aligned data blocks that require fewer CPU instructions and provide higher memory access speeds, in addition to modern C++ features for utilizing the instruction pipelining technique fully. Numerical results are reported on sizeable distribution networks having up to 3124 multi-phase nodes and 9537 three-phase nodes. They show that the proposed approach for computing the gain matrix is up to three times faster than the sparse matrix–matrix multiplication function in the SuiteSparse package, which makes the procedure useful for the development of real-time DSSE software.

A novel two-phase algorithm for a centralised production planning problem by symmetric weighted DEA approach: a case study in energy efficiency

This paper proposes a novel two-phase algorithm for a centralised production planning problem that covers both long-term and mid-term planning simultaneously. Mid-term decisions (first phase) include determining the amount of new inputs and outputs of units in the next production period and long-term decisions (second phase) are related to planning to create a new efficient production unit. The first phase includes a data envelopment analysis approach with symmetric weights through the penalty function. The second phase includes combining data envelopment analysis and response surface methods. In order to evaluate the proposed method, the National Iranian Gas Company is considered as a real case problem. The results show the superiority of the algorithm in both phases over similar methods. The main advantage of the first phase is the realistic production plan. Also, the main advantage of the second phase is optimising the response surface functions and maximising the efficiency of the unit at the same time using one model. [Submitted: 18 March 2021; Accepted: 27 September 2021]

A Novel Two-Phase Algorithm for a Centralized Production Planning Problem by Symmetric Weighted DEA Approach: A Case Study in Energy Efficiency

A Novel Two-Phase Algorithm for a Centralized Production Planning Problem by Symmetric Weighted DEA Approach: A Case Study in Energy Efficiency

Sediment wear prediction model of ZG06Cr13Ni4Mo turbine guide vane in sediment-laden hydropower station

The guide vane of a hydraulic turbine in any sediment-laden hydropower station is one of the components most seriously affected by sediment abrasion. Damage to a guide vane can significantly impact stable operation and energy characteristics of the unit, and it is thus essential to address and effectively manage this problem. In this study, the k-ε solid–liquid two-phase turbulence model and sample algorithm were used to numerically simulate the sand-water flow through the entire passage of a hydraulic turbine and sand samples were subsequently collected from the hydropower station to examine the sediment abrasion damage to turbine’s guide vane, which was made of ZG06Cr13Ni4Mo. Thereafter, calculation and test results were used to establish a prediction model for sediment abrasion of hydraulic turbine guide vane. These research findings could provide guidance for improved hydraulic turbine design and could thus contribute to the optimized operation of sediment-laden hydropower stations.

A novel D2D\u2013MEC method for enhanced computation capability in cellular networks

Device-to-device (D2D) communications and mobile edge computing (MEC) used to resolve traffic overload problems is a trend in the cellular network. By jointly considering the computation capability and the maximum delay, resource-constrained terminals offload parts of their computation-intensive tasks to one nearby device via a D2D connection or an edge server deployed at a base station via a cellular connection. In this paper, a novel method of cellular D2D–MEC system is proposed, which enables task offloading and resource allocation meanwhile improving the execution efficiency of each device with a low latency. We consider the partial offloading strategy and divide the task into local and remote computing, both of which can be executed in parallel through different computational modes. Instead of allocating system resources from a macroscopic view, we innovatively study both the task offloading strategy and the computing efficiency of each device from a microscopic perspective. By taking both task offloading policy and computation resource allocation into consideration, the optimization problem is formulated as that of maximized computing efficiency. As the formulated problem is a mixed-integer non-linear problem, we thus propose a two-phase heuristic algorithm by jointly considering helper selection and computation resources allocation. In the first phase, we obtain the suboptimal helper selection policy. In the second phase, the MEC computation resources allocation strategy is achieved. The proposed low complexity dichotomy algorithm (LCDA) is used to match the subtask-helper pair. The simulation results demonstrate the superiority of the proposed D2D-enhanced MEC system over some traditional D2D–MEC algorithms.

A novel two-phase cycle algorithm for effective cyber intrusion detection in edge computing

Edge computing extends traditional cloud services to the edge of the network, closer to users, and is suitable for network services with low latency requirements. With the rise of edge computing, its security issues have also received increasing attention. In this paper, a novel two-phase cycle algorithm is proposed for effective cyber intrusion detection in edge computing based on a multi-objective genetic algorithm (MOGA) and modified back-propagation neural network (MBPNN), namely TPC-MOGA-MBPNN. In the first phase, the MOGA is employed to build a multi-objective optimization model that tries to find the Pareto optimal parameter set for MBPNN. The Pareto optimal parameter set is applied for simultaneous minimization of the average false positive rate (Avg FPR), mean squared error (MSE) and negative average true positive rate (Avg TPR) in the dataset. In the second phase, some MBPNNs are created based on the parameter set obtained by MOGA and are trained to search for a more optimal parameter set locally. The parameter set obtained in the second phase is used as the input of the first phase, and the training process is repeated until the termination criteria are reached. A benchmark dataset, KDD cup 1999, is used to demonstrate and validate the performance of the proposed approach for intrusion detection. The proposed approach can discover a pool of MBPNN-based solutions. Combining these MBPNN solutions can significantly improve detection performance, and a GA is used to find the optimal MBPNN combination. The results show that the proposed approach achieves an accuracy of 98.81% and a detection rate of 98.23% and outperform most systems of previous works found in the literature. In addition, the proposed approach is a generalized classification approach that is applicable to the problem of any field having multiple conflicting objectives.

Dynamic Cloud Resource Allocation Considering Demand Uncertainty

Cloud computing provisions scalable resources for high performance industrial applications. Cloud providers usually offer two types of usage plans: reserved and on-demand. Reserved plans offer cheaper resources for long-term contracts while on-demand plans are available for short or long periods but are more expensive. To satisfy incoming user demands with reasonable costs, cloud resources should be allocated efficiently. Most existing works focus on either cheaper solutions with reserved resources that may lead to under-provisioning or over-provisioning, or costly solutions with on-demand resources. Since inefficiency of allocating cloud resources can cause huge provisioning costs and fluctuation in cloud demand, resource allocation becomes a highly challenging problem. In this paper, we propose a hybrid method to allocate cloud resources according to the dynamic user demands. This method is developed as a two-phase algorithm that consists of reservation and dynamic provision phases. In this way, we minimize the total deployment cost by formulating each phase as an optimization problem while satisfying quality of service. Due to the uncertain nature of cloud demands, we develop a stochastic optimization approach by modeling user demands as random variables. Our algorithm is evaluated using different experiments and the results show its efficiency in dynamically allocating cloud resources.

Design and FPGA Implementation of New Multidimensional Chaotic Map for Secure Communication

Due to their structure and complexity, chaotic systems have been introduced in several domains such as electronic circuits, commerce domain, encryption and network security. In this paper, we propose a novel multidimensional chaotic system with multiple parameters and nonlinear terms. Then, a two-phase algorithm is presented for investigating the chaotic behavior using bifurcation and Lyapunov exponent (LE) theories. Finally, we illustrate the performances of our proposal by constructing three (03) chaotic maps (3-D, 4-D and 5-D) and implementing the 3-D map on Field-Programmable-Gate-Array (FPGA) boards to generate random keys for securing a client–server communication purpose. Based on the achieved results, the proposed scheme is considered an ideal candidate for numerous resource-constrained devices and internet of the things (IoT) applications.

Efficient Two-Phase Algorithm to Solve Nonconvex MINLP Model of Pump Scheduling Problem

AbstractIn water distribution networks, pumps are used to raise the pressure of water and transfer it throughout the network. Because the cost of electricity consumed by pumps is very high, optimal...

An Online Framework for Joint Network Selection and Service Placement in Mobile Edge Computing

With the rapid development and deployment of 5G wireless technology, mobile edge computing (MEC) has emerged as a new computing paradigm to facilitate a large variety of infrastructures at the network edge to reduce user-perceived communication delay. One of the fundamental problems in this new paradigm is to preserve satisfactory quality-of-service (QoS) for mobile users in light of densely dispersed wireless communication environment and often capacity-constrained MEC nodes. Such user-perceived QoS, typically in terms of the end-to-end delay, is highly vulnerable to both access network bottleneck and communication delay. Previous works have primarily focused on optimizing the communication delay through dynamic service placement, while ignoring the critical effect of access network selection on the access delay. In this work, we study the problem of jointly optimizing the access network selection and service placement for MEC, with the objective of improving the QoS in a cost-efficient manner by judiciously balancing the access delay, communication delay, and service switching cost. Specifically, we propose an efficient online framework to decompose a long-term time-varying optimization problem into a series of one-shot subproblems. To address the NP-hardness of the one-shot problem, we design a computationally-efficient two-phase algorithm based on matching and game theory, which achieves a near-optimal solution. Both rigorous theoretical analysis on the optimality gap and extensive trace-driven simulations are conducted to validate the efficacy of our proposed solution.

K-Step Crossover Method based on Genetic Algorithm for Test Suite Prioritization in Regression Testing

Software is an integration of numerous programming modules  (e.g., functions, procedures, legacy system, reusable components, etc.) tested and combined to build the entire module. However, some undesired faults may occur due to a change in modules while performing validation and verification. Retesting of entire software is a costly affair in terms of money and time. Therefore, to avoid retesting of entire software, regression testing is performed. In regression testing, an earlier created test suite is used to retest the software system's modified module. Regression Testing works in three manners; minimizing test cases, selecting test cases, and prioritizing test cases. In this paper, a two-phase algorithm has been proposed that considers test case selection and test case prioritization technique for performing regression testing on several modules ranging from a smaller line of codes to huge line codes of procedural language. A textual based differencing algorithm has been implemented for test case selection. Program statements modified between two modules are used for textual differencing and utilized to identify test cases that affect modified program statements. In the next step, test case prioritization is implemented by applying the Genetic Algorithm for code/condition coverage. Genetic operators: Crossover and Mutation have been applied over the initial population (i.e. test cases), taking code/condition coverage as fitness criterion to provide a prioritized test suite. Prioritization algorithm can be applied over both original and reduced test suite depending upon the test suite's size or the need for accuracy. In the obtained results, the efficiency of the prioritization algorithms has been analyzed by the Average Percentage of Code Coverage (APCC) and Average Percentage of Code Coverage with cost (APCCc). A comparison of the proposed approach is also done with the previously proposed methods and it is observed that APCC & APCCc values achieve higher percentage values faster in the case of the prioritized test suite in contrast to the non-prioritized test suite.

Multiscale Feature Line Extraction From Raw Point Clouds Based on Local Surface Variation and Anisotropic Contraction

Recent 3-D scanning techniques can produce various kinds of digitized 3-D data. Most of these scanned data are in a format of unstructured point clouds. Such low-level representation of 3-D data usually contains only geometric properties (point positions), while lacking higher level structure cues, for example, feature lines. Feature lines can be defined as a visually prominent characteristic of the shape, including edges, ridges, and valley lines in multiple scales, which can support a lot of downstream applications, such as shape reconstruction and analysis. We present a two-phase algorithm for extracting line-type features on point clouds. To extract both large-scale and shallow feature lines, we first define a statistical metric to detect all potential feature points while immune to the noise to some extent. Then, for correctly reconstructing the feature lines from these identified coarse feature points, we introduce an anisotropic contracting scheme to force feature points lying on the underlying real feature lines. To illustrate the reliability of our method, various experiments have been conducted on both synthetic and raw data. Both visual and quantitative comparisons show that our method is robust to noise and can correctly extract multiscale feature lines. In addition, our method is generally applicable to robotic picking. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This article was motivated by the problem of the feature line extraction for real scanned point clouds. Feature lines, as one kind of the most important structure information, depict the basic shape of the real object in our life. Extracting this kind of shape features from the unstructured point clouds can facilitate a variety of downstream practical applications, such as product design, workpiece manufacturing, and robotic grasping. Existing approaches to detect features either heavily rely on differential quantities, which are sensitive to the noise, or need an elaborately designed local descriptor but fail to recognize small-scale features. These challenges motivate us to design a new approach aiming at extracting multiscale feature lines while keeping robustness to heavy noise. The technique developed in this work can produce high-quality feature points and feature lines, which would serve as higher level structural information and facilitate many applications. Additional applications in 6-degree-of-freedom (6-DoF) pose estimation demonstrate the potential of our method for robotic picking.

A modified optimization method for optimal control problems of continuous stirred tank reactor

‎Continuous stirred tank reactor (CSTR) is an important and constructive part in various chemical and process industries and therefore it is necessary to control the process in optimal temperature and concentration conditions. Because of the nonlinear nature and limits of the control input‎, ‎solving this problem is very difficult. To achieve a sub-optimal control policy for chemical processes‎, ‎we focused on a new construction model‎. Then‎, ‎a two-phase algorithm‎, ‎denoted as modified sequential general variable neighborhood search (MSGVNS) algorithm based on three local searches that use efficient neighborhood interchange has been employed to solve CSTR problems numerically. The results of the proposed method show that its convergence to the exact solution is achieved by the accuracy comparable to other numerical algorithms in few times‎.

On Analyzing Routing Selection for Aerial Autonomous Vehicles Connected to Mobile Network.

This paper proposes a two-phase algorithm for multi-criteria selection of packet forwarding in unmanned aerial vehicles (UAV), which communicate with the control station through commercial mobile network. The selection of proper data forwarding in the two radio link: From UAV to the antenna and from the antenna to the control station, are independent but subject to constrains. The proposed approach is independent of the intra-domain forwarding, so it may be useful for a number of different scenarios of Unmanned Aerial Vehicles connectivity (e.g., a swarm of drones). In the implementation developed in this paper, the connection is served by three different mobile network operators in order to ensure reliable connectivity. The proposed algorithm makes use of Machine Learning tools that are properly trained for predicting the behavior of the link connectivity during the flight duration. The results presented in the last section validate the algorithm and the training process of the machines.

Using Reconfigurable Manufacturing Systems to minimize energy cost: a two-phase algorithm

Energy consumption has become a major concern for society, and since the industrial sector is the largest consumer, companies are urged to improve energy efficiency of their production systems. This paper investigates how Reconfigurable Manufacturing Systems (RMS), and particularly their scalability feature, can be exploited in order to minimize the energy cost in production systems w.r.t. a Time-Of-Use pricing scheme. In the case of RMS, the resulting energy cost optimization problem is a Bilevel Optimization problem, as it jointly considers both the line balancing and the production planning. After introducing the problem and its features, a solving approach based on a simulated annealing algorithm and a linear program is proposed. The approach is then validated on designed instances based on classical test problems taken from the literature. Results show that considerable savings in terms of energy cost can be achieved w.r.t. dedicated lines, even when optimally designed, ultimately showing the great potential of RMS towards energy efficiency.