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IEEE Global Communications Conference

作者： Jonathan Stokes Steven Weber Department of Electrical and Computer Engineering Drexel University

ISBN: (纸本)9781509013296

Social networks are naturally modeled as graphs, with edges indicating associations of users, and as such user popularity or influence is captured by the degree of the node corresponding to the user. It is of natural interest to seek maximum degree nodes, i.e., most popular members, in this graph, and as these networks are large, this requires an efficient search algorithm. In this paper we address the problem of finding a maximum degree node (as distinct from finding the maximum degree) in Erdos Renyi (ER) random graphs, where these graphs are "rewired" in a way to reach a target assortativity without affecting the marginal degree distribution. We address two questions: i) how many maximum degree nodes are in such a graph, on average, and ii) is it easier to find such a node via a biased random walk (BRW) or via random (star) sampling (RSS)? To answer the first question, we show that large ER graphs possess on average a unique maximum degree node. The answer to the second question is that the superiority of BRW vs. RSS depends critically on the assortativity. In particular, RSS is independent of, and BRW is highly sensitive to, the assortativity. We demonstrate numerically the sensitivity of BRW to assortativity is connected to the prevalence of local maxima;these maxima limit the ability of BRW to follow a gradient on the graph. Finally, we establish how the prevalence of local maxima may be computed from the joint degree distribution of the graph.

关键词： Social networks Graph search Random graphs Random walks Random sampling Random walk Random sampling graph search Social Capital Social Networks Really Simple Syndication RSS gene REACTOR SAFETY STUDY Line of nodes searching algorithm

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Discrete harmony search algorithm for solving urban traffic light scheduling problem

Discrete harmony search algorithm for solving urban traffic ...

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American Control Conference

作者： Kaizhou Gao Yicheng Zhang Rong Su Antonios Lentzakis School of Electrical and Electronic Engineering at Nanyang Technological University Singapore 639798

ISBN: (纸本)9781467386838

This study addresses the urban traffic light scheduling problem (UTLSP). A novel centralized model is proposed to describe the urban traffic light control problem in a scheduling framework. The objective is to minimize the total network-wise delay in a fixed time window. A discrete harmony search algorithm (DHS) is proposed to solve the UTLSP. A novel new solution generation strategy is proposed to improve the performance of DHS. A dynamic local search approach is proposed based on the characteristics of UTLSP to improve the search performance in local space. The population initialization is based on the four traffic light stages in Singapore. Extensive computational experiments are carried out using the traffic data in some area of Singapore. The dynamic local search approach is evaluated and tested. The results by DHS algorithm are compared to those by existing traffic light control system. The comparisons and discussions verify that the DHS algorithm is better than the existing traffic light control.

关键词： traffic network Urban traffic light scheduling discrete harmony search traffic network Traffic lights searching algorithm traffic in cities Hereditary Xerocytosis Traffic data optical control

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Hybrid multi-swarm particle swarm optimisation based multi-objective reactive power dispatch

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IET GENERATION TRANSMISSION & DISTRIBUTION 2015年第8期9卷 727-739页

作者： Srivastava, Laxmi Singh, Himmat Madhav Inst Sci & Technol Dept Elect Engn Gwalior MP India

Most of the real-world optimisation problems are subject to different types of constraints and are known as constrained optimisation problems. Reactive power dispatch (RPD) in electrical power system is also a non-linear, multi-objective or a single objective constrained optimisation problem. In this study, hybrid multi-swarm particle swarm optimisation (HMPSO) algorithm has been proposed to solve the RPD problem. HMPSO is one of the recently proposed population based search algorithm, in which the existing swarm is partitioned into several sub-swarms. Particle swarm optimisation is applied as the search engine for each sub-swarm. In addition, to explore more promising regions of the search space, differential evolution (DE) algorithm is implemented to improve the personal best of each particle. The RPD problem is formulated as non-linear, constrained multi-objective optimisation problem with equality and inequality constraints for minimisation of power losses and improvement of voltage profile simultaneously. To find the Pareto optimal set for RPD problem, weighted sum method has been applied. Afterwards, for finding the preferred solution out of the Pareto-optimal set, fuzzy membership function has been used. Effectiveness of the HMPSO algorithm has been verified on the standard IEEE 30-bus and a practical 75-bus Indian systems.

关键词： Equality power loss Particle swarm optimization power systems Reactive power Constrained optimization Power dispatch searching algorithm Search engines fuzzy membership function Dispatching inequality constraint

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FANNG: Fast Approximate Nearest Neighbour Graphs

FANNG: Fast Approximate Nearest Neighbour Graphs

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IEEE Conference on Computer Vision and Pattern Recognition

作者： Ben Harwood Tom Drummond Department of Electrical and Computer Systems Engineering Monash University

ISBN: (纸本)9781467388528

We present a new method for approximate nearest neigh-bour search on large datasets of high dimensional feature vectors, such as SIFT or GIST descriptors. Our approach constructs a directed graph that can be efficiently explored for nearest neighbour queries. Each vertex in this graph represents a feature vector from the dataset being searched. The directed edges are computed by exploiting the fact that, for these datasets, the intrinsic dimensionality of the local manifold-like structure formed by the elements of the dataset is significantly lower than the embedding space. We also provide an efficient search algorithm that uses this graph to rapidly find the nearest neighbour to a query with high probability. We show how the method can be adapted to give a strong guarantee of 100% recall where the query is within a threshold distance of its nearest neighbour. We demonstrate that our method is significantly more efficient than existing state of the art methods. In particular, our GPU implementation can deliver 90% recall for queries on a data set of 1 million SIFT descriptors at a rate of over 1.2 million queries per second on a Titan X. Finally we also demonstrate how our method scales to datasets of 5M and 20M entries.

关键词： Nearest neighbor Dataset Eigenvector critical distance state of the art searching algorithm Line graph Approximate

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Remote Radio Head Selection for Power Saving in Cloud Radio Access Networks

Remote Radio Head Selection for Power Saving in Cloud Radio ...

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IEEE Vehicular Technology Conference

作者： Wentao Zhao Shaowei Wang School of Electronic Science and Engineering Nanjing University

ISBN: (纸本)9781509016990

Cloud radio access network (C-RAN) is considered as a cost- and energy-efficient architecture to meet the ever-increasing mobile data traffic, where baseband processing is decoupled from the remote radio head (RRH) and conducted in a centralized baseband unit (BBU) pool. Though C-RAN enables efficient resource allocation and reduces network operation cost, the optical transport network power consumption between the RRHs and the BBU pool is usually enormous. In this paper, we focus on the minimization of the total power consumption by jointly considering the transport network power and the transmission power of the RRHs. We formulate a general RRH selection task that falls into the form of capacitated facility location problem (CFLP) and propose an efficient local search algorithm to address it. Numerical results validate that our proposal can significantly reduce the total power consumption of the C-RAN.

关键词： Cloud radio access network (C-RAN) Power saving RRH selection Traffic density Power consumption Wireless networks Traffic density power transmission Resource utilization RRH gene Traffic data Transport networks Network operations Radio Clouds searching algorithm aggregate capacity

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Stochastic approximation search algorithms with randomization at the input

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AUTOMATION AND REMOTE CONTROL 2015年第5期76卷 762-775页

作者： Granichin, O. N. St Petersburg State Univ St Petersburg 199034 Russia

This work presents a comprehensive survey of the development of pseudogradient stochastic approximation algorithms with randomized input disturbance, considers the problems of their applicability in optimization problems with linear constraints, and discusses new possibilities to use them for multiagent control for load balancing of nodes in computational networks. Justifications of the algorithms' correctness and their optimal convergence rate are based on the foundational works of B.T. Polyak.

关键词： Load balancing algorithms searching algorithm stochastic approximation Approximation algorithms Ganglia ENTERING Randomisation

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Co-optimizing task and motion planning

Co-optimizing task and motion planning

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IEEE/RSJ International Conference on Intelligent Robots and Systems

作者： Chongjie Zhang Julie A. Shah Computer Science and Artificial Intelligence Lab MIT Cambridge 02139 United States

ISBN: (纸本)9781509037636

Solutions to robotic manipulation problems can be substantially improved through integrated task and motion planning. Existing approaches typically focus on satisfaction, finding a feasible solution, instead of optimization. We formulate large-scale robotic manipulation problems as multi-level optimization, incorporating task, action, and motion planning. We develop an integrated planning approach for solving this optimization problem and generating a combined motion plan for a robot to optimize a task-level objective. This approach utilizes a combinatorial search algorithm for task planning and incrementally exploits information from lower-level optimization to improve the high-level task plan. Empirical results show that this integrated approach not only significantly outperforms a traditional top-down approach in solution quality, but also avoids infeasible lower-level motion plans.

关键词： motion planning searching algorithm Integrated planning Solutions Robot

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Improved Hamming Distance Search using Variable Length Hashing

Improved Hamming Distance Search using Variable Length Hashi...

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IEEE Conference on Computer Vision and Pattern Recognition

作者： Eng-Jon Ong Miroslaw Bober Centre for Vision Speech and Signal Processing University of Surrey

ISBN: (纸本)9781467388528

This paper addresses the problem of ultra-large-scale search in Hamming spaces. There has been considerable research on generating compact binary codes in vision, for example for visual search tasks. However the issue of efficient searching through huge sets of binary codes remains largely unsolved. To this end, we propose a novel, unsupervised approach to thresholded search in Hamming space, supporting long codes (e.g. 512-bits) with a wide-range of Hamming distance radii. Our method is capable of working efficiently with billions of codes delivering between one to three orders of magnitude acceleration, as compared to prior art. This is achieved by relaxing the equal-size constraint in the Multi-Index Hashing approach, leading to multiple hash-tables with variable length hash-keys. Based on the theoretical analysis of the retrieval probabilities of multiple hash-tables we propose a novel search algorithm for obtaining a suitable set of hash-key lengths. The resulting retrieval mechanism is shown empirically to improve the efficiency over the state-of-the-art, across a range of datasets, bit-depths and retrieval thresholds.

关键词： searching algorithm binary code Hamming distance variable length hashing long code Arts Theoretical analysis retrieval Dataset

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Queue-aware Energy-efficient Scheduling and Power Allocation in Small-cell Networks with Interference

Queue-aware Energy-efficient Scheduling and Power Allocation...

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IEEE Wireless Communications and Networking Conference

作者： Hongxin Wei Limin Xiao Yunzhou Li Shidong Zhou State Key Laboratory on Microwave and Digital Communications Tsinghua National Laboratory Information Science and Technology Department of Electronic Engineering Tsinghua University

ISBN: (纸本)9781467398152

We investigate the problem of throughput-guaranteed energy saving for base stations in small-cell networks with universal frequency reuse. The objective function is to minimize the time average of total power expenditure in each slot over infinite horizon, while the data arrival rate is constrained in the mean rate stable region. We take advantage of both queue state information and channel state information to find the optimal strategy of user scheduling and power allocation, which consumes the least energy and causes the least inter-cell interference. According to Lyapunov optimization theory, the problem is formulated into a non-convex combinatorial optimization problem of dynamic user scheduling and power allocation in each slot. In general, the problem is NP-hard, so we devise a two-step heuristic algorithm to solve it. Simulation results show that the performance of the proposed algorithm is close to exhaustive search algorithm, and better than Round Robin algorithm and MaxWeight algorithm.

关键词： power splitting average time Optimization theory infinite horizon optimal strategy searching algorithm Heuristic algorithms BASE STATION Channel state information Energy efficiency Nondeterministic polynomial-time hard interference Objective function

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Focused Stochastic Local Search and the Lovasz Local Lemma 16

Focused Stochastic Local Search and the Lovasz Local Lemma

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Annual ACM-Society for Industrial and Applied Mathmatics Symposium on Discrete algorithms

作者： Dimitris Achlioptas Fotis Iliopoulos Department of Computer Science Department of Electrical Engineering and Computer Science

ISBN: (纸本)9781510819672

We develop tools for analyzing focused stochastic local search algorithms. These are algorithms that search a state space probabilistically by repeatedly selecting a constraint that is violated in the current state and moving to a random nearby state which, hopefully, addresses the violation without introducing many new ones. A large class of such algorithms arise from algorithmizations of the Lovasz Local Lemma, a non-constructive tool for proving the existence of satisfying states. Here we give tools that provide a unified analysis of such algorithms and of many more, expressing them as instances of a general framework.

关键词： algorithms Lemma FOCUSING searching algorithm state space Stochastic EQUIPMENT current status analysis of algorithm Moving

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