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

作者： Ertan Bütün Mehmet Kaya Department of Computer Engineering Firat University Elazi□ÄŸ Turkey

Community discovery in complex network has become an interesting topic in recent years. Multi-objective optimizations can entirely handle community discovery problem instead of single objective optimizations. Thus a multi-objective genetic algorithm approach is proposed for community discovery in complex networks in this paper. We improved MOGA-Net [1] proposed by Pizzuti by applying effective searching on search space at genetic algorithm steps. We tested our approach and achieved better results on synthetic networks and real life networks.

关键词： Communities Genetic algorithms optimization Linear programming Sociology Statistics Genetics

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HW/SW partitioning and code generation of embedded control applications on a reconfigurable architecture platform

HW/SW partitioning and code generation of embedded control a...

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international workshop on Hardware/Software Codesign (CODES)

作者： M. Baleani F. Gennari Yunjian Jiang Y. Patel R.K. Brayton A. Sangiovanni-Vincentelli PARADES E.E.I.G. Rome Italy Department of EECS University of California Berkeley CA USA

This paper studies the use of a reconfigurable architecture platform for embedded control applications aimed at improving real time performance. The HW/SW codesign methodology from POLIS is used. It starts from high-level specifications, optimizes an intermediate model of computation (extended finite state machines) and derives both hardware and software, based on performance constraints. We study a particular architecture platform, which consists of a general purpose processor core, augmented with a reconfigurable function unit and data-path to improve run time performance. A new mapping flow and algorithms to partition hardware and software are proposed to generate implementations that best utilize this architecture. Encouraging preliminary results are shown for automotive electronic control examples.

关键词： Hardware Computer architecture Reconfigurable architectures Application software Constraint optimization Computational modeling High performance computing Automata Software performance Partitioning algorithms

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3D Beamforming with Multi-Active Multi-Passive Antenna Arrays Using Stochastic optimization

3D Beamforming with Multi-Active Multi-Passive Antenna Array...

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IEEE workshop on Signal Processing Advances in Wireless Communications (SPAWC)

作者： Georgios K. Papageorgiou Mathini Sellathurai Dimitrios K. Ntaikos Constantinos B. Papadias Heriot-Watt University Edinburgh UK Athens Information Technology Athens GR The American College of Greece Athens GR

ISBN: (数字)9781728154787

ISBN: (纸本)9781728154794

In this paper, we consider the 3D beamforming with Multi-Active Multi-Passive (MAMP) antenna arrays. For the optimization of the hybrid array's active elements' (AEs) weights and passive elements' (PEs) loads, we propose a novel algorithm, i.e., the alternating optimization - stochastic 3D beamforming algorithm (AO-S3DBA). The scheme is built on a generalized cost function and alternates between the optimization of the loads and weights. Despite the dramatic increase in data points, we managed to decrease the algorithm's complexity by employing Adam, a popular accelerator for stochastic optimization, for the update of the optimization variables. We present simulation results, where the proposed MAMP array can successfully emulate the beam of a uniform rectangular array (URA) with the use of 50% less AEs and with beam steering capabilities towards various azimuth and elevation directions. We believe that this newly proposed type of hybrid transceiver offers a good trade-off between cost and performance, which can be particularly useful in many industrial and Internet-of-Things (IoT) applications, where the deployment of a large number of transceivers / sensors directly affects the application's total cost.

关键词： Three-dimensional displays Antenna arrays Array signal processing Signal processing algorithms Cost function Azimuth

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Hierarchical optimization of parallel applications

Hierarchical optimization of parallel applications

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IEEE international workshop on Software Engineering for Parallel and Distributed Systems

作者： A. Mitschele-Thiel IMMD VII Universität Erlangen Nürnberg Erlangen Germany

In order to allow the efficient implementation of parallel application programs, various design decisions have to be made and implemented. These include the task mapping and schedule, the task granularity as well as decisions concerning code optimizations. The paper describes a hierarchical concept for system design and implementation. The concept has been implemented in the DSPL programming environment, an integrated approach to generate efficient parallel programs from data-flow specifications. It supports the design itself as well as the implementation of the design decisions. In contrast to most programming environments for MIMD systems that simply use a runtime kernel or an operating system to implement the design decisions, the DSPL programming environment actually transforms the application program to implement the design decisions as the task schedule and granularity. In this paper we focus on the hierarchical strategy to compute and implement the design decisions concerning the task schedule. We show how most of the schedule can be decided statically even in the presence of input-dependent program behavior. This results in much lower runtime overhead than incurred with dynamic approaches based on operating systems.

关键词： Processor scheduling Parallel programming Programming environments Operating systems Signal design Algorithm design and analysis Signal processing algorithms World Wide Web Runtime environment Dynamic scheduling

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Drone Trajectory optimization using Genetic Algorithm with Prioritized Base Stations

Drone Trajectory Optimization using Genetic Algorithm with P...

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IEEE international workshop on Computer-Aided Modeling, Analysis, and Design of Communication Links and Networks, CAMAD

作者： Tianrui Qiao Yusuf A. Sambo Muhammad A. Imran Wasim Ahmad Glasgow College UESTC University of Electronic Science and Technology of China Chengdu China James Watt School of Engineering University of Glasgow Scotland United Kingdom

ISBN: (数字)9781728163390

ISBN: (纸本)9781728163406

Drones have been widely applied to perform emergent tasks in the post-disaster scenario, due to their unique characteristics such as mobility, flexibility, and adaptivity to altitude. However, drones have limited energy capacity, which presents a major drawback in flight time and affects their performance in such scenarios. Hence, trajectory optimization has become a critical research problem for such applications of drones. In this paper, we present an optimal trajectory design for a single drone to ferry data from temporary Base Stations (BSs) deployed within a disaster zone to a fixed gateway node that is the point of origin and final destination for the drone flight. We have used a Genetic Algorithm (GA)-based approach that takes into account the shortest distance traveled and least time spent by the drone during flight. We also examine the case where some BSs have delay requirements that are unknown to the drone in advance. Simulation results show that the performance of our proposed GA-based approach matches that of the benchmark exhaustive search algorithm and the difference in computational time between the 2 algorithms increases with the number of BSs. Accordingly, our proposed algorithm has 96.4% lower computational time complexity compared to the benchmark exhaustive search algorithm when there are 12 BSs in the disaster area.

关键词： Drones Logic gates Genetic algorithms Trajectory Sociology Statistics Biological cells

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Direct optimization of Evaluation Measures in Learning to Rank Using Particle Swarm

Direct Optimization of Evaluation Measures in Learning to Ra...

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international workshop on Database and Expert Systems applications

作者： Oscar Alejo Juan M. Fernández-Luna Juan F. Huete Ramiro Perez-Vázquez Informatic Faculty University of Cienfuegos Cienfuegos Cuba Departamento de Ciencias de la Computación e I.A. CITIC-UGR Universidad de Granada Granada Spain Center of Informatic Studies Central University Las Villas Santa Clara Cuba

One of the central issues in Learning to Rank (L2R) for Information Retrieval is to develop algorithms that construct ranking models by directly optimizing evaluation measures used in IR such as Precision at n, Mean Average Precision and Normalized Discounted Cumulative Gain. In this work we propose a new learning-to-rank method, referred as RankPSO. This algorithm is based on Particle Swarm optimization. It builds a ranking model able to directly optimize evaluation measures used in Information Retrieval. To evaluate performance of RankPSO, we have compared it with other methods referenced in literature. We have carried out an experimental study using Letor OHSUMED dataset. The obtained results were analyzed statistically, demonstrating that RankPSO has significant improvement in precision compared to RankSVM, RankBoost and Regression methods; nevertheless, it does not have significant differences with AdaRank-MAP, AdaRank-NDCG, ListNet and FRank. The results show the advantages to use Particle Swarm optimization as bio-inspired algorithm for learning to rank.

关键词： Particle measurements Atmospheric measurements Particle swarm optimization optimization Loss measurement Machine learning Position measurement

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Cyber Security Problem based on Multi-Objective Distributed Constraint optimization Technique

Cyber Security Problem based on Multi-Objective Distributed ...

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Annual IEEE/IFIP international Conference on Dependable Systems and Networks workshops

作者： Tenda Okimoto Naoto Ikegai Katsumi Inoue Hitoshi Okada Tony Ribeiro Hiroshi Maruyama Transdisciplinary Research Integration Center Tokyo National Institute of Informatics Tokyo The Graduate University for Advanced Studies Tokyo Institute of Mathematical Statistics Tokyo

ISBN: (纸本)9781479901807

A cyber security problem is an important application domain for systems resilience. The increase of malware, computer viruses, and intensive cyber attacks are serious problems for our information society. In this paper, we introduce a new presentation of a cyber security problem. Our model is based on a Multi-Objective Distributed Constraint optimization Problem (MO-DCOP) which is a fundamental problem that can formalize various applications related to multi-agent cooperation. MO-DCOP is suitable for modeling a cyber security problem, since cyber security problems involve multiple criteria, e.g., risk (security), surveillance (privacy) and cost. Furthermore, MO-DCOP is a decentralized model. In this model, variables and constraints are distributed among agents. Since there exists no single agent which maintains all information, it is resilient against intensive cyber attacks. Furthermore, we develop a novel algorithm for solving a cyber security problem which utilizes well-known and widely used branch and bound technique and depth-first search strategy and finds all trade-off solutions. We also propose the extension of this algorithm which utilizes a preprocessing technique called soft Arc Consistency. The softAC is a well-known preprocessing technique which transforms a constraint optimization problem into a simplified problem that can be solved efficiently. In the experiments, we examine the run time of our proposed algorithms in cyber security problems and show that our algorithms can solve cyber security problems quickly.

关键词： Cyber Security Problem Multi-Objective Distributed Constraint optimization Network security Constrained optimization Runtime decentralised control Computer viruses information society

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On structural parameterizations of hitting set: Hitting paths in graphs using 2-SAT

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Journal of Graph algorithms and applications 2017年第2期21卷 219-245页

作者： Jansen, Bart M. P. Department of Mathematics and Computer Science Eindhoven University of Technology Netherlands

Hitting Set is a classic problem in combinatorial optimization. Its input consists of a set system F over a finite universe U and an integer t;the question is whether there is a set of t elements that intersects every set in F. The Hitting Set problem parameterized by the size of the solution is a well-known W[2]-complete problem in parameterized complexity theory. In this paper we investigate the complexity of Hitting Set under various structural parameterizations of the input. Our starting point is the folklore result that Hitting Set is polynomial-time solvable if there is a tree T on vertex set U such that the sets in F induce connected subtrees of T. We consider the case that there is a treelike graph with vertex set U such that the sets in F induce connected subgraphs;the parameter of the problem is a measure of how treelike the graph is. Our main positive result is an algorithm that, given a graph G with cyclomatic number k, a collection P of simple paths in G, and an integer t, determines in time 25k(|G| + |P|)O(1)whether there is a vertex set of size t that hits all paths in P. It is based on a connection to the 2-SAT problem in multiple valued logic. For other parameterizations we derive W[1]-hardness and para-NP-completeness results. © 2017, Brown University. All rights reserved.

关键词： Combinatorial optimization

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Multi-objective optimization Algorithm based on PTN Network Index System

Multi-objective Optimization Algorithm based on PTN Network ...

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

作者： Xing Li Chunzhi Wang Zaoning Wang Aijun Liu Yucheng Zhang Yunfei Xiong Hubei University of Technology Wuhan China Wuhan Fiberhome Technology Service Co. Ltd.

ISBN: (纸本)9781665426060

Packet Transport Network (PTN), as an efficient transmission network technology in mobile communications in the era of big data, is used by more and more communication operators. With the rapid increase in the number of users and the continuous decline in revenue per user, the existing PTN network must be optimized in all aspects. In the PTN network, the optimization of one indicator often affects other *** paper proposes a multi-objective optimization method based on linear weighting to optimize the two major indicators in the PTN network. The first indicator is the active and standby routing rate using the Label Switching Path (LSP) technology. The second indicator is the link bandwidth occupancy rate after adding the committed information rate (CIR).The method firstly establishes a multi-objective optimization mathematical model, converts multi-objective optimization into single-objective optimization, and then uses the improved Top K shortest path (KSP) algorithm to optimize LSP active and standby routing services and CIR Services with excessive bandwidth usage are switched to reduce the number of services with high bandwidth usage and the same route. The experimental results show that the multi-objective optimization method has been improved from the initial score of 82.1484 to 98.5346, achieving the overall optimal result. The LSP indicator has been improved from 74 to 96.72, and the CIR has been improved from 88.1 to 99.86, reaching the multi-objective overall best, single objective is relatively optimal. When the existing path resources cannot solve the problem, the solution will be optimized by adding resources, which not only enhances the security of the PTN network but also improves the utilization of network resources.

关键词： Heuristic algorithms optimization methods Switches Bandwidth Routing Indexes Resource management

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Token approach for role allocation in extreme teams: analysis and experimental evaluation

Token approach for role allocation in extreme teams: analysi...

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IEEE international workshop on Enabling Technologies: Infrastructure for Collaborative Enterprises (WET ICE)

作者： P. Seem A. Farinelli S. Okamoto M. Tambe Carnegie Mellon Univ. Pittsburgh PA USA University of Rome “La Sapienza” Italy University of Southern California USA

Open computational systems comprise physical entities coordinating their activities in dynamic environments. Many exciting applications require a large number of such entities to achieve team coordination in complex missions execution. To meet the fundamental challenge of role allocation in such extreme teams, we propose an algorithm called LA-DCOP, that overcomes the limitations of previous algorithms by incorporating three key ideas. First, we represent the role allocation problem as a distributed constraint optimization problem and use tokens representing roles to minimize constraint violations. Second, we use probabilistic information about the team to guide the search quickly towards good solutions. Third, we designed the algorithm to manage constrained roles. We show that LA-DCOP not only meets our requirements in extreme teams, but also compares favorably against previous role allocation algorithms. LA-DCOP has allowed an order of magnitude scale-up in extreme teams, with role allocation in a fully distributed proxy-based teams with up to 200 members.

关键词： Fires Physics computing Constraint optimization Manufacturing Mobile robots Resource management State estimation Space technology Algorithm design and analysis Computer networks

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