Greedy Principle Research Articles

Research on Computer-aided Adjustment and Optimization of Train Service Plan based on Passenger Flow Assignment

Abstract According to the complex characteristics of the mutual influence of passenger flows between different train adjustment schemes, a computer-aided train service plan adjustment optimization model based on passenger flow assignment is proposed with the goal of minimizing the remaining passenger turnover. The actual operating effects of trains and the impact between different adjustment measures and between trains are also considered. A computer iterative optimization algorithm with 2 stages based on the greedy principle is designed. The computer processing is divided into two stages: train adjustment plan selection and passenger flow information analysis. The selection of adjustment schemes is feedback-corrected based on the results of passenger flow assignment. Finally, taking the train operation plan of the Beijing-Shanghai high-speed railway as the research object, an example is demonstrated, and the results show that the model and algorithm can effectively obtain the optimal train adjustment scheme under the current conditions.

Asymptotic collective behaviors of a delayed multi‐particle system with multiple processes

It is a significant issue to explore the collective behaviors of the delayed complex systems. This paper studies asymptotic collective behaviors of a delayed multi‐particle system with multiple processes while particles interact directionally and locally. Up to now, the studies on dynamic behaviors of multi‐particle systems do not involve limited processing capacity, that is, the information queuing phenomenon is ignored. To this aim, we introduce multiple processes to formulate the evolution of a delayed multi‐particle system. To optimize the delay parameters, we choose the greedy principle to set the processing order. The main idea is to decompose the velocity into each process and use functional differential equation theory and matrix theory to constrain the velocity variation rate while the processing order is fixed. As the new results, in the non‐critical and general situation, when the time delay is less than or equal to the threshold value and the eigenvalue 1 of the average matrix is semi‐simple, we achieve the criterion of flocking and multi‐cluster with exponential convergence rate involving multiple processes. Furthermore, we get the lower bound estimate of the processing capacity.

Weighted singular value decomposition basis of Szegő kernel and its applications to signal reconstruction and denoising

For any circular analytic signal f+(eit)∈H2(T), if the sequence {ak}k=0∞ in the unit disc D satisfies the hyperbolic non-separability condition ∑k=0∞(1−|ak|)=∞, it is known that the first N+1 term approximation function based on the Takenaka–Malmquist system (SN+1f+)(eit)=∑k=0N〈f+(eit),Bk(eit)〉Bk(eit),can approximate the analytic signal f+(eit) in the L2-norm sense, where Bk(eit)=1−|ak|21−ak¯eit∏l=0k−1eit−al1−al¯eit, k∈N is the Takenaka–Malmquist system which can be seen as a generalization of Fourier series. We find that (SN+1f+)(eit) can also be represented by the standard basis, Lagrange basis and weighted SVD basis. Different bases have different merits. The standard basis is simple. The Lagrange basis has a neat and compact form, which is very convenient for theoretical analysis. With the Takenaka–Malmquist system, the first N+1 term approximation function (SN+1f+)(eit) is able to write in an iterative form. The Takenaka–Malmquist system has the advantage of inheritance, and the point ak∈D can be easily selected step by step through the greedy principle. In this paper, we focus our attention on the weighted SVD basis. We give some orthogonal properties of the weighted SVD basis. It demonstrates that any real signal f(eit)∈L2(T) can be precisely reconstructed within the given error range by the weighted SVD basis decomposition. The experimental results reveal that the algorithm based on weighted SVD basis is more stable and accurate. Furthermore, compared with classical Fourier decomposition and adaptive Fourier decomposition algorithms, the experimental results show that the weighted SVD basis decomposition is more insensitive to noise. This will provide a new method for signal denoising.

Toward Convergence: A Gradient-Based Multiobjective Method With Greedy Hash for Hyperspectral Unmixing

Multiobjective optimization aims at addressing the conflicting objectives, which has been introduced to improve the performance of sparse hyperspectral unmixing. Recently proposed multiobjective unmixing methods usually employ evolutionary algorithms to improve the unmixing accuracy. However, evolutionary algorithms may suffer the challenge of convergence, in which case the reasonability of the solutions is hard to guarantee. To solve the problem of convergence, in this paper, we present a new gradient-based multiobjective unmixing method, which explores the optimization direction in a theoretically reliable manner. Furthermore, considering the mathematical model of hyperspectral sparse unmixing where sparsity error objective of selected endmembers is discrete, we develop a greedy hash based coding approach which is able to well describe the discrete constraints imposed on endmembers. The major components of the proposed method are a search approach and an update approach. In the search approach, we construct the pareto descent direction via a gradient-based strategy, which contributes to converging to an optimal continuous solution by searching along this direction. In the update approach, we update discrete binary endmember via hash coding under the guidance of greedy principle, which allows our method to handle the problem of discrete objective. The major contribution of the proposed method is designing a new framework that can get the optimal discrete endmembers in a convergent way. Moreover, we provide the theoretical analysis and proof for the convergence. Synthetic and real-world experiments have indicated the advantages of our algorithm when compared with evolutionary multiobjective unmixing methods.

Optimization of supercapacitor’s location and capacity in the urban railway lines based on greedy principle

Using energy storage equipment is an effective solution to improve energy efficiency for urban railway lines. Two of the important parameters are the location and capacity of the storage device that will determine the efficiency of the power supply system. This paper presents an algorithm that combines the greed principle and the retrieval principle to determine the optimal location and capacity of supercapacitor storage stations along the track to maximize the economic benefits. The proposed algorithm is tested on a simulation model built based on the data of the Cat Linh - Ha Dong urban railway line. The simulation results show that the optimal installation of energy storage stations will increase maximum energy efficiency to contribute to economic growth and reduce the load peaks that will be reducing voltage fluctuations.

Green Tethered UAVs for EMF-Aware Cellular Networks

A prevalent theory circulating among the non-scientific community is that the intensive deployment of base stations over the territory significantly increases the level of electromagnetic field (EMF) exposure and affects population health. To alleviate this concern, in this work, we propose a network architecture that introduces tethered unmanned aerial vehicles (TUAVs) carrying green antennas to minimize the EMF exposure while guaranteeing a high data rate for users. In particular, each TUAV can attach itself to one of the possible ground stations at the top of some buildings. The location of the TUAVs, transmit power of user equipment, and association policy are optimized to minimize the EMF exposure. Unfortunately, the problem turns out to be a mixed integer non-linear programming (MINLP), which is non-deterministic polynomial-time (NP) hard. We propose an efficient low-complexity algorithm composed of three submodules. Firstly, we propose an algorithm based on the greedy principle to determine the optimal association matrix between the users and base stations. Then, we offer two approaches, modified <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> -mean and shrink and realign (SR) process, to associate each TUAV with a ground station. Also, we put forward two algorithms based on the golden search and SR process to adjust the TUAV’s position within the hovering area over the building. Finally, we consider the dual problem that maximizes the sum rate while keeping the exposure below a predefined value, such as the level enforced by the regulation. Numerical results show that TUAVs with green antennas can effectively mitigate the EMF exposure by more than 20% compared to fixed green small cell while achieving a higher data rate.

CHARACTER EDUCATION CARES ENVIRONMENT BASED ON LOCAL KNOWLEDGE IN URUG SOCIETIES BOGOR WEST JAVA

The objective of this research is to find out about character education cares environment based on local knowledge. The studies method used is ethnographic with qualitative approach. This method is implemented because the research is related to the social, traditional ceremony, customary societies, and social as well as cultural phenomena. The data are gathered through in-depth interview, observation, and documentation. The selection of respondent as samples uses purposive sampling technique. The result is probed meticulously through technique triangulation. The result shows that Urug societies have created societies character for the environment both social and ecology environment through applicating and conserve the tradition of heritage namely picking must ask for permission. The tradition is conducted collectively as societies habit when they release cultivating of paddies. There are two sorts of ceremony cultural such as Harvesting traditional ceremony (seren taun) and alms to earth (sedekah bumi). Permiting tradition in picking paddies must be conducted because they have the view that a person must admit to the god before planting paddies through alms to earth. When the time for harvesting coming, societies propose admitting again before collecting paddies in the field. Once after paddies have been stored, Urug societies held harvesting ceremony collectively as a form that they have been gratefully when they obtain paddies yield. The other is Urug societies avoid greedy principle, it enacts Urug societies avoid from the environment devastated typically land because they farm paddies only one time in a year rather than 3-4 periods in a year. Other is Urug societies have applicated environment ethics both ecocentrism and biocentrism perspective to preserve nature from ravage. They also select agriculture as their venue to survive because they think that farmer is honest person and vanish envy temper in their life. Farmer embedded cooperation, mutual assisting, and mutual supporting among them.

Cooperative self-configuring nature-inspired algorithm for a scheduling problem

One of the crucial challenges related to operational manufacturing planning is an optimal plan search in the current situation using a workflow model. The problem solving is greatly hindered by the rapid growth of the search space with an increase in dimension or the so-called combinatorial explosion. This paper uses two different approaches to solving a hierarchical scheduling problem based on different solution representations. The first approach assumes a search of an optimal project order and then solving of resource-constrained project scheduling problem (RCPSP) for each of the projects using a model based on the greedy principle. In the second approach we are searching for priorities of all actions of all projects and then use them in the process of building a schedule if there are any conflicts when choosing the next action. To solve the problem with both approaches, the paper considers some nature-inspired algorithms such as the intelligent water drops algorithm (IWDs), a genetic algorithm (GA) and ant colony optimization (ACO) as well as a self-configuring version of the last two. The paper shows the efficiency of the application of the coevolution algorithm using IWDs, self-configuring GA and ACO.

A Resource Management Model for Distributed Multi-Task Applications in Fog Computing Networks

While the effectiveness of fog computing in Internet of Things (IoT) applications has been widely investigated in various studies, there is still a lack of techniques to efficiently utilize the computing resources in a fog platform to maximize Quality of Service (QoS) and Quality of Experience (QoE). This paper presents a resource management model for service placement of distributed multitasking applications in fog computing through mathematical modeling of such a platform. Our main design goal is to reduce communication between the candidate nodes hosting different task modules of an application by selecting a group of nodes near each other and as close to the source of the data as possible. We propose a method based on a greedy principle that demonstrates a highly scalable and near-optimal performance for resource mapping problems for multitasking applications in fog computing networks. Compared with the commercial Gurobi optimizer, our proposed algorithm provides a mapping solution that obtains 93% of the performance, attributed to a higher communication cost, while outperforming the reference method in terms of the computing speed, cutting the mapping execution time to less than 1% of that of the Gurobi optimizer.

A force levels and gestures integrated multi-task strategy for neural decoding

This paper discusses the problem of decoding gestures represented by surface electromyography (sEMG) signals in the presence of variable force levels. It is an attempt that multi-task learning (MTL) is proposed to recognize gestures and force levels synchronously. First, methods of gesture recognition with different force levels are investigated. Then, MTL framework is presented to improve the gesture recognition performance and give information about force levels. Last but not least, to solve the problem that using the greedy principle in MTL, a modified pseudo-task augmentation (PTA) trajectory is introduced. Experiments conducted on two representative datasets demonstrate that compared with other methods, frequency domain information with convolutional neural network (CNN) is more suitable for gesture recognition with variable force levels. Besides, the feasibility of extracting features that are closely related to both gestures and force levels is verified via MTL. By influencing learning dynamics, the proposed PTA method can improve the results of all tasks, and make it applicable to the case where the main tasks and auxiliary tasks are clear.

Continuous Route Planning over a Dynamic Graph in Real-Time

Abstract Central to many location-based services is the route planning over road networks. The immense scale of urbanization and the sharp increase of vehicles on the road make it urgent to explore a navigation system to provide effective and effcient services to users. To achieve it, we need to continuously search a good route from the user’s current position to the destination as the travel time of every road probably changes frequently. Here, the road network is abstracted as a dynamic graph and the fluctuating travel time of each road is viewed as the varying weight of the corresponding edge. Some work has investigated the problem and most of them is to find the exact shortest path for a given query on the entire graph. In fact, it is unnecessary and an approximate optimal path obtained with a much smaller cost also can satisfy the requirements. In this paper, we propose a continuous route planning algorithm with the idea of greedy principle and substantially speed up processing by reducing the query area.

Implementasi Algoritma Dijkstra Dalam Pembuatan Aplikasi Pesona Palu

This study aims to build application that is able to provide information on the location of culinary tourism, shopping and recreation center for the area of Palu City that runs on the Android platform and able to show the closest path to be traversed in order to help tourists or newcomers who are not too familiar with the city of Palu. Determination of the shortest route in this application using Dijkstra algorithm. This algorithm is used to find the shortest path on a directed graph. This algorithm uses the Greedy principle which states that at each step we choose the minimum-weighted side and put it into the solution set.

PENERAPAN ALGORITMA GREEDY DALAM MENENTUKAN MINIMUM SPANNING TREES PADA OPTIMISASI JARINGAN LISTRIK JALA

This article discusses the applied of greedy algorithm principle in finding the optimum solution in determine minimum spanning tree on graph. Graph theory is one of the studies in discrete mathematics that are widely applied in various scope. This article is a literature study and applied of nets electricity network optimization using Prims algorithm and Kruskal algorithm. Network Nets System is one type of electrical network system construction. Based on results of the study and discussion can be concluded that the application of greedy algorithm using Prims algorithm and Kruskal algorithm in determine minimum spanning tree on its principle is the same. However, after a comparison between the two algorithms we consider that the ideal algorithm used to optimize the nets electric network is the Kruskal algorithm because in the case of the electric network has few sides and many vertices.

Length Shuffle: Achieving high performance and flexibility for data center networks design

Scale-out data center requires the underlying network to provide high performance and design flexibility on commodity switches. Current rigid architectures can provide high performance, but they hardly support network design flexibility. Flexible proposals address the issue of elasticity in the rigid networks. However, the introduction of random connection cannot provide guaranteed network performance. In this paper, we proposed Length Shuffle connection to address the performance issue in flexible data center networks. The proposed approach follows a greedy principle that gradually connects the furthest two switches with their available ports. Length Shuffle connection can reduce the number of long paths significantly, which results in a small average length of routing path. The proposed connection can work with random connection and regular connection in flexible network construction, and does not interfere the original routing protocols. Numerical analysis shows the nice properties of Length Shuffle connection in various dimensions. The extensive experimental results demonstrate the advantages of Length Shuffle connection in data transmission delay and aggregate throughput under different traffic patterns.

Throughput enhancement of IEEE 802.11ad through space‐time division multiple access scheduling of multiple co‐channel networks

60-GHz millimetre-wave (mm-wave) communication is gradually becoming a promising candidate for the next generation wireless system to meet the demands of mobile applications. To compensate for high path loss, directional links are established in mm-wave communication system, which adds opportunity for spatial reuse. As one of its most promising protocols for commercial production, IEEE 802.11ad standard provides a mechanism to support space-time division multiple access (STDMA) within a single network. However, the interference level among different co-channel networks is much lower than that inside a network, which provides greater potential for spatial reuse. In this study, based on the architecture and timing structure of IEEE 802.11ad, the authors propose a spatial reuse strategy among multiple co-channel networks. They formulate the problem as a mixed-integer non-linear programming problem, and then propose an inter-network STDMA scheduling algorithm, which considers clustering frame structure in IEEE 802.11ad, and combines greedy principle and mutual interference avoidance strategy. Extensive simulation results have shown that the proposed scheme enlarges total throughput in comparison with STDMA inside each network, as well as non-STDMA scheme. Meanwhile, it achieves the lowest packet loss rate under heavy traffic load.

GRIP: Greedy Routing through dIstributed Parametrization for guaranteed delivery in WSNs

Although stateless greedy routing is well investigated in 2D wireless sensor networks (WSNs), it is widely believed to be impossible in 3D. In this paper, we aim at overcoming the impossibility through a distributed parametrization that equips a WSN with virtual coordinates favoring greedy routing. We propose a fundamentally new parametrization to embed the network domain, the resulting embedding domain allows greedy routing to have guaranteed delivery. We also present localized algorithms to realize this map in WSNs. To combat the load concentration caused by greedy routing that applies the distance greedy principle, we further propose tunable greedy routing, which relies on tuning a parameter in the greedy objective to naturally balance routing load. These two proposals form our Greedy Routing through dIstributed Parametrization (GRIP). We prove the correctness and efficiency of GRIP and use simulations to evaluate its performance in terms of complexity, load balancing, and energy efficiency.

Optimal schedules of fractionated radiation therapy by way of the greedy principle: biologically-based adaptive boosting

We revisit a long-standing problem of optimization of fractionated radiotherapy and solve it in considerable generality under the following three assumptions only: (1) repopulation of clonogenic cancer cells between radiation exposures follows linear birth-and-death Markov process; (2) clonogenic cancer cells do not interact with each other; and (3) the dose response function s(D) is decreasing and logarithmically concave. Optimal schedules of fractionated radiation identified in this work can be described by the following ‘greedy’ principle: give the maximum possible dose as soon as possible. This means that upper bounds on the total dose and the dose per fraction reflecting limitations on the damage to normal tissue, along with a lower bound on the time between successive fractions of radiation, determine the optimal radiation schedules completely. Results of this work lead to a new paradigm of dose delivery which we term optimal biologically-based adaptive boosting (OBBAB). It amounts to (a) subdividing the target into regions that are homogeneous with respect to the maximum total dose and maximum dose per fraction allowed by the anatomy and biological properties of the normal tissue within (or adjacent to) the region in question and (b) treating each region with an individual optimal schedule determined by these constraints. The fact that different regions may be treated to different total dose and dose per fraction mean that the number of fractions may also vary between regions. Numerical evidence suggests that OBBAB produces significantly larger tumor control probability than the corresponding conventional treatments.

Applied-Information Technology and Data Processing for Multi-Objective Interval Job Shop Scheduling Problem with Flexible Preventive Maintenance

Interval job shop scheduling problem with flexible preventive maintenance is considered and an multi-objective swarm-based neighborhood search (MOSNS) is proposed for minimizing interval makespan and total interval tardiness. A dominance based greedy principle is utilized to decide if the old solution is replaced with the new one. A solution with the highest rank is replaced with a randomly chosen member of the non-dominated set every few number of iterations. Computational results show that MOSNS is an effective approach to solve the considered problem.

Multi-objective artificial bee colony for interval job shop scheduling with flexible maintenance

In scheduling problem with uncertainty, flexible preventive maintenance (PM) and multiple objectives are seldom investigated. In this study, interval job shop scheduling problem with non-resumable jobs and flexible maintenance is considered and an effective multi-objective artificial bee colony (MOABC) is proposed, in which an effective decoding procedure is used to build the schedule and handle PM operation. The objective is to minimize interval makespan and a newly defined objective called total interval tardiness. In each cycle, a dominance-based greedy principle is adopted, a dominance-based tournament is utilized to choose solution for onlooker bee, and the non-dominated ranking is applied to update the non-dominated set. A solution with the highest rank is replaced with a non-dominated solution every certain cycle. Computational results show the good performance of MOABC on the considered problem.

Maximum stability data gathering trees for mobile sensor networks

We describe a benchmarking algorithm Max.Stability-DG algorithm to determine the sequence of data gathering trees for maximum stability in mobile sensor networks such that the number of tree discoveries is the theoretical global minimum. The Max.Stability-DG algorithm assumes the availability of the complete knowledge of future topology changes and works based on a simple greedy principle: whenever a new data gathering tree is required at time instant t, we determine a spanning tree that exists for the longest time since t, transform the spanning tree to a rooted data gathering tree and use it until it exists. The whole procedure is repeated until the end of the session to obtain a sequence of longest-living stable data gathering trees. We prove the correctness of the Max.Stability-DG algorithm and also evaluate the performance of the Max.Stability-DG trees compared to minimum-distance spanning tree-based data gathering trees through extensive simulations under diverse operating conditions.