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Integration of a robust shortest path algorithm with a time dependent vehicle routing model and applications

Integration of a robust shortest path algorithm with a time ...

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IEEE international Conference on Computational Intelligence for Measurement Systems and applications (CIMSA)

作者： A.V. Donati R. Montemanni L.M. Gambardella A.E. Rizzoli Istituto Dalle Molle di Studi sull Intelligenza Artificiale Manno Switzerland

This paper describes a way of combining two techniques, in order to define a framework that can deal with the following problem: find an optimized set of routes when the customers set is a proper subset of an entire network, and variable traffic conditions have to be taken into account. This is accomplished on one hand by extending the vehicle routing problem (VRP) to a time dependent case (TDVRP), on the other hand by using an appropriate algorithm, the robust shortest path (RPS) that can provide itineraries when moving to a location to another, and guarantee good performance under any possible road network situation. Once a proper description of the TDVRP model is given, we discuss the optimization technique, based on the ant colony system (ACS), and the robust shortest path (RPS) algorithm. Different ways of integrating these techniques are discussed The one presented here consists in using the RPS algorithm for the calculation of the paths among each pair of customers, so that this information can to be used by the TDVRP optimization in a very efficient way. In the case of a real road network, some tests have been made, that show that the optimal solutions obtained for the classic VRP case are sub optimal when considered in a time dependent context, revealing that the approximation of constant speeds is sometimes inadequate.

关键词： Robustness Vehicles Routing Ant colony optimization Cellular neural networks Roads Logistics Joining processes Heuristic algorithms

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Dynamic Job Scheduling in the Cloud Using Slowdown optimization and Sandpile Cellular Automata Model

Dynamic Job Scheduling in the Cloud Using Slowdown Optimizat...

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IEEE international Symposium on Parallel and Distributed Processing workshops and Phd Forum (IPDPSW)

作者： Jakub Gasior Franciszek Seredynski Systems Research Institute Polish Academy of Sciences Warsaw Poland Dept. of Math. & Natural Sci. Cardinal Stefan Wyszynski Univ. Warsaw Poland

ISBN: (纸本)9781467376853

We present in this paper a general framework to study issues of effective load balancing and scheduling in highly parallel and distributed environments such as currently built Cloud computing systems. We propose a novel approach based on the concept of the Sandpile cellular automaton: a decentralized multi-agent system working in a critical state at the edge of chaos. Our goal is providing fairness between concurrent job submissions by minimizing slowdown of individual applications and dynamically rescheduling them to the best suited resources. The algorithm design is experimentally validated by a number of numerical experiments showing the effectiveness and scalability of the scheme in the presence of a large number of jobs and resources and its ability to react to dynamic changes in real time.

关键词： Load management Dynamic scheduling Heuristic algorithms Load modeling Automata Processor scheduling

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Phased Array Beamforming Method Based on Deep Learning

Phased Array Beamforming Method Based on Deep Learning

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IEEE MTT-S international Microwave workshop Series on Advanced Materials and Processes for RF and THz applications (IMWS-AMP)

作者： T. F. Li P. F. Gu S. Y. Wu M. Q. Lv Z. H. Fan D. Z. Ding Department of Communication Engineering Nanjing University of Science and Technology Nanjing China

ISBN: (数字)9798331520717

ISBN: (纸本)9798331520724

By controlling the amplitude and phase of the array element, the array antenna synthesizes different beam direction and shape, and has the characteristics of strong directivity, low sidelobe, wide scanning, etc., which is widely used in modern radio systems. Phased array antenna means an antenna that changes the shape of the directional map by controlling the feed phase of the radiating units in an array antenna. This paper presents a beamforming algorithm based on radial basis function (RBF) neural networks. For the traditional beamforming method which uses optimization algorithm to reduce sidelobe, the antenna element phase and pattern parameters before optimization are input to RBF neural network and trained to approximate the optimized antenna phase. The simulation results show that the beamforming method based on RBF neural network is effective.

关键词： Phased arrays Array signal processing Shape Simulation Neural networks Directive antennas Approximation algorithms Antenna feeds optimization Antenna arrays

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Frontiers in Algorithmics and Algorithmic Aspects in Information and Management: Third Joint international Conference, FAW-AAIM 2013, Dalian, China, June 26-28, 2013. Proceedings 1

引用

丛书名： Lecture Notes in Computer Science 7924

2013年

作者： Dániel Marx (auth.), Michael Fellows, Xuehou Tan, Binhai Zhu (eds.)

ISBN: (数字)9783642387562

ISBN: (纸本)9783642387555;9783642387562

This book constitutes the refereed proceedings of the 7th international Frontiers of Algorithmics workshop, FAW 2013, and the 9th international Conference on Algorithmic Aspects in Information and Management, AAIM 2013, jointly held in Dalian, China, in June 2013. The 33 revised full papers presented together with 2 invited talks were carefully reviewed and selected from 60 submissions. The Joint Conference provide a focused forum on current trends of research on algorithms, discrete structures, operation research, combinatorial optimization and their applications, and will bring together international experts at the research frontiers in these areas to exchange ideas and to present significant new results. The mission of the Joint Conference is to stimulate the various fields for which algorithmics can become a crucial enabler, and to strengthen the ties between the Eastern and Western research communities of algorithmics and applications.

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Integration of TACO and BP Neural Network

Integration of TACO and BP Neural Network

引用

workshop on Intelligent Information Technology Application

作者： Jing Leng Dept. of Inf. Technol. Hubei Univ. of Police Wuhan

As one of the extensive applications of artificial neural network, BP algorithm has some shortcomings such as local optimum. In this paper, we propose a new method--TACO-BP algorithm to train neural network, which may overcome the shortcoming. Firstly, we give description about the TACO-BP. After experiments, we compare the performance between TACO-BPNN and BPNN. Lastly, we analyze the results of the experiments.

关键词： Neural networks Artificial neural networks optimization methods Information technology Convergence Artificial intelligence Intelligent networks Multi-layer neural network Acceleration Genetic algorithms

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Handling Datasets in a Multi-Relational Environment: Cluster Dispersion vs Cluster Purity

Handling Datasets in a Multi-Relational Environment: Cluster...

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IEEE international workshop on Intelligent Data Acquisition and Advanced Computing Systems: Technology and applications

作者： Rayner Alfred Dimitar Kazakov SEIT Universiti Malaysia Sabah Kota Kinabalu Sabah Malaysia York University York UK

Clustering multiple-instances in a multi-relational environment requires data transformations (e.g. data aggregation) from datasets stored in multiple tables into a single table. Unfortunately, most relational databases are limited to a few basic methods of aggregation (e.g. max, min, sum, count, ave) to aggregate continuous and categorical values. Therefore, data transformation is limited only to aggregation of continuous and categorical values. In this paper, to get the best number of clusters, we propose a genetic semi-supervised clustering technique as a means of aggregating data stored in multiple tables. This algorithm is suitable for classification of datasets with a high degree of one-to-many associations, in which a single record has multiple instances that are associated with it. The clustering algorithm can be used in two ways. One is the unsupervised clustering, where the user may control the result of clustering by optimizing the value of cluster dispersion. The other is a semi-supervised clustering, where the user uses an unsupervised clustering method optimized with a genetic algorithm incorporating a measure of classification accuracy used in decision tree algorithm, the GINI index. In this paper, we examine both methods to dynamically cluster multiple instances, as a means of aggregating them, and illustrate the effectiveness of the semi-supervised genetic algorithm-based clustering technique.

关键词： Relational databases Clustering algorithms Supervised learning Genetic algorithms Decision trees Conferences Data acquisition Aggregates Clustering methods optimization methods

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An image threholding approach based on cuckoo search algorithm and 2D maximum entropy

An image threholding approach based on cuckoo search algorit...

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IEEE international workshop on Intelligent Data Acquisition and Advanced Computing Systems: Technology and applications

作者： Wei Zhao Zhiwei Ye Mingwei Wang Lie Ma Wei Liu School of Computer Science Hubei University of Technology Engineering China Beijing Key Laboratory of Urban Spatial Information Beijing China

ISBN: (纸本)9781467383622

The image thresholding approach based on the basis of 2-D maximum entropy has better segmentation performance by the use of local space information of pixels, but it is unpractical for heavy computation required by this method. In the paper, an image segmentation technology based on cuckoo search and 2-D maximum entropy is presented, which views the seeking of 2-D maximum entropy of the image as a function optimization problem and uses the behavior of the obligate brood parasitism of some cuckoo species to simulate the process of searching optimal threshold. Furthermore, a local search strategy is employed to improve the results in the cuckoo search algorithm. The experimental results proves that compared with 2-D maximum entropy thresholding optimized with genetic algorithm, differential evolution algorithm and particle swarm optimization algorithm, the proposed method is able to get the optimal thresholds quickly and with a higher probability to get optimal threshold, which is a fast and robust image segmentation method.

关键词： Entropy Image segmentation Histograms optimization Algorithm design and analysis Search problems Genetic algorithms

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optimization Approaches of Multizone Furnace Loading Process Control

Optimization Approaches of Multizone Furnace Loading Process...

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IEEE international workshop on Intelligent Data Acquisition and Advanced Computing Systems: Technology and applications

作者： R. Pasichnyk A. Vande Wouwer Y. Pigovsky Ternopil State Economic University Ternopil Ukraine Faculté Polytechnique de Mons Mons Belgium

An algorithm for optimal heat treatment in a multizone diffusive furnace is proposed. A mathematical model of the furnace is developed and a performance criterion is defined. This latter criterion is minimized using a procedure based on the simultaneous perturbation stochastic approximation (SPSA) algorithm as well as with the MatLab FMINBND algorithm. Problem simplifications are also considered. Simulation results are discussed.

关键词： Furnaces Process control Crystals Temperature Space heating Heat treatment Mathematical model Approximation algorithms Metallization Silicon

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A Faster FPTAS for Knapsack Problem with Cardinality Constraint 1

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18th international workshop on Approximation and Online algorithms, WAOA 2019

作者： Li, Wenxin Lee, Joohyun Shroff, Ness Department of ECE The Ohio State University Columbus United States Division of Electrical Engineering Hanyang University Seoul Korea Republic of Department of ECE and CSE The Ohio State University Columbus United States

ISBN: (数字)9783030808792

ISBN: (纸本)9783030808785

We study the K-item knapsack problem (i.e., 1.5-dimensional knapsack problem), a generalization of the famous 0–1 knapsack problem (i.e., 1-dimensional knapsack problem) in which an upper bound K is imposed on the number of items selected. This problem is of fundamental importance and is known to have a broad range of applications in various fields. It is well known that, there is no fully polynomial time approximation scheme (FPTAS) for the d-dimensional knapsack problem when d≥ 2, unless P = NP. While the K-item knapsack problem is known to admit an FPTAS, the complexity of all existing FPTASs have a high dependency on the cardinality bound K and approximation error Ε, which could result in inefficiencies especially when K and Ε- 1 increase. The current best results are due to [Mastrolilli and Hutter, 2006], in which two schemes are presented exhibiting a space-time tradeoff–one scheme with time complexity O(n+ Kz2/ Ε2) and space complexity O(n+ z3/ Ε), and another scheme that requires a run-time of O(n+ (Kz2+ z4) / Ε2) but only needs O(n+ z2/ Ε) space, where z= min { K, 1 / Ε}. In this paper we close the space-time tradeoff exhibited in [Mastrolilli and Hutter, 2006] by designing a new FPTAS with a running time of O~ (n+ z2/ Ε2), while simultaneously reaching a space complexity (O~ notation hides terms poly-logarithmic in n and 1 / Ε ) of O(n+ z2/ Ε). Our scheme provides O~ (K) and O(z) improvements on the state-of-the-art algorithms in time and space complexity respectively, and is the first scheme that achieves a running time that is independent of the cardinality bound K (up to logarithmic factors) under fixed Ε. Another salient feature of our algorithm is that it is the first FPTAS that achieves better time and space complexity bounds than the very first standard FPTAS over all parameter regimes. © 2021, Springer Nature Switzerland AG.

关键词： Combinatorial optimization

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Massively parallel simulated annealing embedded with downhill-a SPMD algorithm for cluster computing

Massively parallel simulated annealing embedded with downhil...

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IEEE international Conference on Cluster Computing

作者： Zhihui Du Sanli Li Shuyou Li Mengyue Wu Jing Zhu Department of Computer Science and Technology Tsinghua University Beijing China School of Material Science and Engineering Tsinghua University Beijing China

ISBN: (纸本)0769503438

Simulated Annealing (SA) is a frequently used stochastic algorithm to deal with combinatorial optimization problems and it converges with probability infinitely close to 1. SA is an NP algorithm and the long executive time prevents it from being accepted for many real-time applications. This paper presents a SPMD (Single Program Multiple Data) algorithm which combines SA with local searching algorithm-downhill. The hybrid method not only keeps the convergence of SA but also improves the convergence speed of SA. Approximate solutions can be found quickly for complex optimization problems and more precise solutions can also be found by employing the same algorithm to fine-tune the approximate solutions. SA is an essential serial algorithm, but the SPMD algorithm breaks up the serial bottleneck of SA and its performance scales up linearly with the increase of processors, at the same time, the SPMD algorithm does not require careful choice of control parameters. Application cases show that the algorithm is robust and it can find high quality solution with high speed.

关键词： Computational modeling Simulated annealing Clustering algorithms Concurrent computing Embedded computing Materials science and technology Convergence optimization methods Computer science Stochastic processes

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