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IEEE Green Energy and Smart Systems Conference (IGESSC)

作者： Jalali, Sammuel Calif State Univ Long Beach Long Beach CA 90840 USA

ISBN: (纸本)9781538677414

Advanced adaptive signal processing algorithms commonly used in optimization, filtering, and estimation are subjects of seeking innovations and advancements. The important performance factors of algorithms are adaptation or convergence (the probability of, and the speed of), and computational cost (proportional to the algorithm complexity). The improvement in convergence often comes with higher computational needs. In this report we exploit the diversity in algorithms by using cooperative processes. An odd number of cooperative processes running simultaneously participate in a simple majority-vote decision method to improve the convergence speed of the corresponding application. The iterative algorithms concurrently progressing require higher computational resources, however only the most computationally-efficient algorithm remains active after the initial convergence is achieved. Additional technique is essential to synchronize the process delays of participating algorithms.

关键词： Adaptive systems cooperative algorithms Diversity in algorithm Channel equalization

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Deconstructing B-Trees Using Upshoot and cooperative algorithms

Deconstructing B-Trees Using Upshoot and Cooperative Algorit...

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2009 Asia-Pacific Conference on Information Processing

作者： Jiang Zhengzheng Library Changsha University of Science and Technology Changsha 410004, China Yong Huang School of Civil Engineering Hunan University Changsha 410082, China

Many system administrators would agree that, had it not been for cooperative algorithms, the simulation of link-level acknowledgements might never have occurred. While this outcome is mostly a natural mission, it has ample historical precedence. In our research, we disconfirm the visualization of the look aside buffer, which embodies the private principles of hardware and architecture. Upshoot, our new methodology for perfect theory, is the solution to all of these problems.

关键词： deconstructing B-Trees upshoot cooperative algorithms

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An Evaluation on Competitive and cooperative Evolutionary algorithms for Data Clustering

An Evaluation on Competitive and Cooperative Evolutionary Al...

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IEEE Congress on Evolutionary Computation (CEC) as part of the IEEE World Congress on Computational Intelligence (IEEE WCCI)

作者： Pacifico, Luciano D. S. Ludermir, Teresa B. Univ Fed Rural Pernambuco UFRPE Dept Comp DC Recife PE Brazil Univ Fed Pernambuco UFPE Ctr Informat CIn Recife PE Brazil

ISBN: (数字)9781728169293

ISBN: (纸本)9781728169293

Data clustering methods are important tools for exploratory data analysis in many real world applications, such as data mining, image understanding, text analysis, engineering, medicine, and so on. Partitional clustering models are the most popular clustering methods, but these approaches suffer from some limitations, like the sensibility to algorithm initialization and the lack of mechanisms to help them escaping from local minima points. Evolutionary algorithms (EAs) are global optimization meta-heuristics known for their capabilities to find optimal solutions even when dealing with hard and complex problems. Although many EAs are based on competitive behavior among individuals, its is known that cooperation may lead to better solutions then sheer competition. In this work, we perform a comparative analysis among four state-of-the-art EAs (Genetic Algorithm, Differential Evolution, Particle Swarm Optimization and Group Search Optimization), implemented in both competitive and cooperative frameworks, in the context of data clustering problem. Experiments are executed using eleven real world benchmark datasets as the testing bed, so we could access whether competitive or cooperative behaviors would prevail. The experimental results showed that cooperative algorithms are able to find better solutions, in average, when dealing with clustering problems, than their corresponding competitive approaches, and such models also require less storage memory to keep their population in comparison to competitive methods.

关键词： Evolutionary Computing Data Clustering cooperative algorithms cooperative Coevolutionary algorithms

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cooperative and Distributed algorithms for Dynamic Fire Coverage Using a Team of UAVs

Cooperative and Distributed Algorithms for Dynamic Fire Cove...

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作者： Shrestha, Kripash University of Nevada Reno

学位级别：M.S., Master of Science/Master of Surgery

Recent large wildfires in the United States and subsequent damage that they have caused have increased the importance of wildfire monitoring and tracking. However, human monitoring on the ground or in the air may be too dangerous and therefore, there needs to be alternatives to monitoring wildfires. Unmanned Aerial Vehicles (UAVs) are currently being considered and used for applications such as reconnaissance, surveying, and monitoring in the spatial-time domain because they can be deployed in teams remotely to gather information and minimize the harm and risk to human operators. UAVs have been previously used in this problem domain to track and monitor wildfires with approaches such as potential fields and reinforcement learning. In this thesis, we aim to look at a team of UAVs, in a distributed approach, over an area to maximize the sensor coverage in dynamic wildfire environments and minimize the energy consumption of deployed UAVs in a network. The work implements and compares an implementation of Deb's NSGA-II to optimize potential fields, Experience Replay with Q-learning, a Deep Q-Network (DQN), and a Deep Q-Network with a state estimator (autoencoder) to track and cover wildfires. The application of this work is a not a final suggestion or an absolute solution for wildfire monitoring and tracking but instead compares the methods to declare the most promising method for future work and research.

关键词： cooperative algorithms Distributed algorithms Genetic algorithm Reinforcement learning Team of UAVs Wildfire coverage

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Parameter-free and cooperative local search algorithms for graph colouring

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SOFT COMPUTING 2021年第24期25卷 15035-15050页

作者： Chalupa, David Nielsen, Peter Aalborg Univ Fac Engn & Sci Dept Mat & Prod Operat Res Grp Fibigerstraede 16 DK-9220 Aalborg Denmark Univ Hull Sch Engn & Comp Sci Kingston Upon Hull HU6 7RX N Humberside England

Parameter-free algorithms are of an increasing interest in applications that require out-of-the-box solving techniques. For instance, scheduling in volatile environments or control parameters optimisation often requires flexible solving approaches without the need to tune or retune the parameters. In addition, parameter-free algorithms are usually beneficial in solving previously unexplored real-world problem instances. In this paper, we propose a parameter-free local search strategy to solve graph colouring, which is the underlying problem for a number of scheduling applications. Two variants of parameter-free local search are computationally investigated: PFLS-A and PFLS-B. Both of these algorithms are based on accepting strictly improving moves and operating as a random walk if no strictly improving move is found. PFLS-A and PFLS-B differ in randomised versus systematic exploration of the neighbourhood of the current solution. Their cooperative variant CPFLS is also proposed. We compare the results of these three algorithms to the results obtained by 13 other local search methods from the literature. CPFLS provides better or equal results as the other local search algorithms in 67.25% of per-instance comparisons. This is without employing any explicit or implicit parameters that are usually used in metaheuristics. This hints the promising nature of parameter-free metaheuristics not only for this problem but also metaheuristics in general.

关键词： Graph colouring Local search Parameter-free algorithms cooperative algorithms Metaheuristics

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StanisLAX: An artificial laboratory for the development of cooperative speech processing algorithms

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Engineering Applications of Artificial Intelligence 1991年第1期4卷 23-33页

作者： Junqua, Jean-Claude Wakita, Hisashi

It is the belief of the authors that speech recognition algorithms can best be handled through the effective and efficient cooperation of multiple sources of knowledge. For the development of various types of such algorithms, also called hybrid speech algorithms and, more generally, for speech processing, some advanced architectures and speech processing environments are needed. There is also a need to manipulate speech knowledge, through the use of abstract data structures, to process databases, and to help the modeling, simulation, and evaluation of automatic speech recognizers. To tackle these problems, the new concept of an "artificial laboratory";for speech processing is proposed. Such a system simulates a real laboratory and allows analysis of data. It also provides a large range of computing facilities which can be used with ease to perform modeling and simulation. In this paper, the main concepts of the system are developed, and the performance of the current prototype is reported in the case of the modeling and simulation of a voice activation and a dialogue system. © 1991.

关键词： Artificial laboratory cooperative algorithms integration modeling simulation reusability speech recognition

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A cooperative conjugate gradient method for linear systems permitting efficient multi-thread implementation

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COMPUTATIONAL & APPLIED MATHEMATICS 2018年第2期37卷 1601-1628页

作者： Bhaya, Amit Bliman, Pierre-Alexandre Niedu, Guilherme Pazos, Fernando A. Univ Fed Rio de Janeiro Dept Elect Engn Rio De Janeiro RJ Brazil UPMC Univ Paris 06 Inria Sorbonne Univ Lab JL LionsUMR CNRS 7598 Paris France Fundacao Getulio Vargas Escola Matemat Aplicada Rio De Janeiro RJ Brazil Petrobras SA Rio De Janeiro Brazil Univ Estado Rio De Janeiro Dept Elect & Telecommun Engn Rio De Janeiro RJ Brazil

This paper revisits, in a multi-thread context, the so-called multi-parameter or block conjugate gradient (B-CG) methods, first proposed as sequential algorithms by O'Leary and Brezinski, for the solution of the linear system Ax = b, for an n-dimensional symmetric positive definite matrix A. Instead of the scalar parameters of the classical CG algorithm, which minimizes a scalar functional at each iteration, multiple descent and conjugate directions are updated simultaneously. Implementation involves the use of multiple threads and the algorithm is referred to as cooperative CG (CCG) to emphasize that each thread now uses information that comes from the other threads. It is shown that for a sufficiently large matrix dimension n, the use of an optimal number of threads results in a worst case flop count of O (n(7/3)) in exact arithmetic. Numerical experiments on a multi-core, multi-thread computer, for synthetic and real matrices, illustrate the theoretical results.

关键词： Discrete linear systems Iterative methods Conjugate gradient algorithm cooperative algorithms

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Parallel cooperative micro-particle swarm optimization: A master-slave model

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APPLIED SOFT COMPUTING 2012年第11期12卷 3552-3579页

作者： Parsopoulos, Konstantinos E. Univ Ioannina Dept Comp Sci GR-45110 Ioannina Greece

A parallel master-slave model of the recently proposed cooperative micro-particle swarm optimization approach is introduced. The algorithm is based on the decomposition of the original search space in subspaces of smaller dimension. Each subspace is probed by a subswarm of small size that identifies suboptimal partial solution components. A context vector that serves as repository for the best attained partial solutions of all subswarms is used for the evaluation of the particles. The required modifications to fit the original algorithm within a parallel computation framework are discussed along with their impact on performance. Also, both linear and random allocation of direction components to subswarms are considered to render the algorithm capable of capturing possible correlations among decision variables. The proposed approach is evaluated on two types of computer systems, namely an academic cluster and a desktop multicore system, using a popular test suite. Statistical analysis of the obtained results reveals that, besides the expected run-time superiority of the parallel model, significant improvements in solution quality can also be achieved. Different factors that may affect performance are pointed out, offering intuition on the expected behavior of the parallel model. (c) 2012 Elsevier B.V. All rights reserved.

关键词： Particle swarm optimization Parallel algorithms cooperative algorithms Master-slave model Micro-evolutionary algorithms

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cooperative/Parallel Kalman Filtering for Decentralized Network Navigation

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IEICE TRANSACTIONS ON COMMUNICATIONS 2016年第9期E99B卷 2087-2098页

作者： Gao, Wenyun Chen, Xi Hu, Dexiu Xu, Haisheng Tsinghua Univ Dept Elect Engn Beijing 100084 Peoples R China Tsinghua Univ Space Ctr Beijing 100084 Peoples R China

This paper presents non-iterative cooperative/parallel Kalman filtering algorithms for decentralized network navigation, in which mobile nodes cooperate in both spatial and temporal domains to infer their positions. We begin by presenting an augmented minimum-mean-square error (MMSE) estimator for centralized navigation network, and then decouple it into a set of local sub-ones each corresponding to a mobile node;all these sub-estimators work in parallel and cooperatively-with the state estimates exchanging between neighbors-to provide results similar to those obtained by the augmented one. After that, we employ the approximation methods that adopted in the conventional nonlinear Kalman filters to calculate the second-order terms involved in these sub-estimators, and propose a decentralized cooperative/parallel Kalman filtering based network navigation framework. Finally, upon the framework, we present two cooperative/parallel Kalman filtering algorithms corresponding to the extended and unscented Kalman filters respectively, and compare them with conventional decentralized methods by simulations to show the superiority.

关键词： network navigation decentralized algorithm cooperative algorithms Kalmen filter

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Deployment of an algorithm for large-scale topology discovery

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IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS 2006年第12期24卷 2210-2220页

作者： Donnet, Benoit Raoult, Philippe Friedman, Timur Crovella, Mark Univ Paris 06 LIP6 CNRS Lab F-75015 Paris France N2NSoft F-75001 Paris France Boston Univ Dept Comp Sci Boston MA 02215 USA

Topology discovery systems are starting to be introduced in the form of easily and widely deployed software. Unfortunately, the research community has not examined the problem of how to perform such measurements efficiently and in a network-friendly manner. This paper describes several contributions towards that end. These were first presented in the proceedings of ACM SIGMETRICS 2005. We show that standard topology discovery methods (e.g., skitter) are quite inefficient, repeatedly probing the same interfaces. This is a concern, because when scaled up, such methods will generate so much traffic that they will begin to resemble distributed denial-of-service attacks. We propose two metrics;focusing on redundancy in probing and show that both are important. We also propose and evaluate Doubletree, an algorithm that strongly reduces redundancy, while maintaining nearly the same level of node and link coverage. The key ideas are,to exploit the tree-like structure of routes to and from a single point in order to guide when to stop probing, and to probe each path by starting near its midpoint. Following the SIGMETRICS work, we implemented Doubletree, and deployed it in a real-network environment. This paper describes that implementation, as well as preliminary favorable results.

关键词： cooperative algorithms network topology traceroute

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