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检索条件"主题词=particle swarm algorithm"

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A Hybrid Immune algorithm for Solving Dynamic Optimization Problems

A Hybrid Immune Algorithm for Solving Dynamic Optimization P...

引用

第30届中国控制与决策会议

作者： YANG Zhou YUAN Yi-chuan LUO Ting-xing QIN Jin College of Computer Science and Technology Guizhou University

Dynamic optimization problem(DOP) is a kind of optimization problem in the real world, and its optimization target or constraint may change over time. In order to solve the dynamic optimization problem effectively, a hybrid algorithm is proposed based on artificial immune network algorithm. On the one hand, update operator in particle swarm algorithm is introduced to realize a supplement to artificial immune network algorithm and improve its convergence speed. On the other hand, due to its local search capability, chaotic variables are used to avoid premature convergence in the late optimization of artificial immune network algorithm and improve search precision. At the same time, a Cauchy mutation operator is added to improve the global search capability. Simulation experiments are conducted on GDBG. The experimental results show that the algorithm can not only effectively improve the global search ability, convergence speed and search precision, but also keep the population diversity. Compared with similar optimization algorithms, the algorithm has obvious advantages in error and convergence indexes.

关键词： dynamic optimization artificial immune particle swarm algorithm chaotic variables Cauchy mutation

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Research on Image Network Retrieval Application Based on swarm Optimization algorithm

Research on Image Network Retrieval Application Based on Swa...

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2018 International Conference on Network, Communication, Computer Engineering (NCCE 2018)

作者： Qingpeng Nie Rizhao Polytechnic

ISBN: (纸本)9781510871076

The traditional image retrieval adopts a text-based method,which requires a lot of manual annotation of the images in the image library and matches according to the keywords when *** not suitable for the modern image network retrieval,this paper proposes a network model based on the particle swarm optimization algorithm,and then establishes a mathematical model based on swarm *** images' low-level visual features to retrieve images is the core of content-based image ***-based image retrieval is an approximate matching technique that combines computer vision,image processing,and database and other technical achievements in many *** feature extraction can be done automatically by computer,avoiding the subjectivity of traditional manual annotation,and can also improve the ***.

关键词： particle swarm algorithm image network retrieval color extraction

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A Method of Channel Capacity Optimization Based on Dynamically Adjusted Inertia Weight Acceleration Factor in Cognitive Sensing Network

A Method of Channel Capacity Optimization Based on Dynamical...

引用

International Conference on Software Engineering Research, Management and Applications (SERA)

作者： Yanjun Hu Dongdong Wei Key Laboratory of Intelligent Computing & Signal Processing Ministry of Education Anhui University Hefei China

ISBN: (纸本)9781538658871

The optimization of channel capacity in cognitive sensor networks is a complicated optimization problem. The traditional gradient search method based on the analysis has more restrictions on the objective function, and high complexity, and can not determine the convergence. Aiming at the inherent problems of the traditional gradient search algorithm, the particle swarm optimization(PSO) with simple and easy to implement, distributed computing and fast convergence speed can be used to solve the problem of channel capacity optimization. It is difficult to balance the global search with the local search by adopting a standard particle swarm algorithm with fixed algorithm parameters, which can not solve the premature convergence problem that may occur. The specific meaning of each parameter of the algorithm is analyzed in this paper, and an improved particle swarm optimization algorithm based on dynamic adjustment of inertia weight acceleration factor(DWAPSO) is proposed, and the improved particle swarm optimization algorithm is applied to the optimization of channel capacity in cognitive sensor networks. The simulation results show that the improved channel capacity optimization algorithm(DWAPSO-CA) can speed up the convergence rate, increase the system capacity and get a lower bit error rate.

关键词： Channel capacity particle swarm algorithm Bit error rate system capacity particle swarm optimization Inertial forces speedup factor Search methods SEARCHING algorithm Converge Distributed computing

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Accelerating particle swarm algorithm with GPGPU

Accelerating Particle Swarm Algorithm with GPGPU

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19th International Euromicro Conference on Parallel, Distributed and Network-Based Processing (PDP)

作者： Cardenas-Montes, Miguel Vega-Rodriguez, Miguel A. Jose Rodriguez-Vazquez, Juan Gomez-Iglesias, Antonio CIEMAT Dept Fundamental Reseach Madrid Spain Univ Extremadura ARCO Res Grp Caceres Spain CIEMAT Natl Fus Lab Madrid Spain

ISBN: (纸本)9780769543284

This paper focuses on solving large size optimization problems using GPGPU. Evolutionary algorithms for solving these optimization problems suffer from the curse of dimensionality, which implies that their performance deteriorates as quickly as the dimensionality of the search space increases. This difficulty makes very challenging the performance studies for very high dimensional problems. Furthermore, these studies deal with a limited time-budget. The availability of low cost powerful parallel graphics cards has stimulated the implementation of diverse algorithms on Graphics Processing Units (GPU). In this paper, the design of a GPGPU-based Parallel particle swarm algorithm, to tackle this type of problem maintaining a limited execution time budget, is described. This implementation profits of an efficient mapping of the data elements (swarm of very high dimensional particles) to the parallel processing elements of the GPU. In this problem, the fitness evaluation is the most CPU-costly routine, and therefore the main candidate to be implemented on GPU. As main conclusion, the speed-up curve versus the increase in dimensionality is shown. This curve indicates an asymptotic limit stemmed from the data-parallel mapping.

关键词： GPGPU Parallelism Performance Analysis particle swarm algorithm

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The application of the projection pursuit model of the particle swarm algorithm on the investment decision

The application of the projection pursuit model of the parti...

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International Conference on Advanced Materials and Computer Science

作者： Deng Li Luo Fangqiong Liuzhou Teachers Coll Dept Finance & Econ Liuzhou 545004 Guangxi Peoples R China Liuzhou Teachers Coll Dept Math & Comp Sci Liuzhou 545004 Guangxi Peoples R China

ISBN: (纸本)9783037850978

In order to make a good decision in the projection investment, and the application of the projection pursuit model of particle swarm algorithm on the investment decision was studied in depth. Firstly, the development of the projection pursuit model of particle swarm algorithm was introduced, and then the brief introduction of PP and the step of constructing the PPC model were introduced. The basic theory and calculating procession of particle swarm algorithm was analyzed. And then the projection investment decision model was established. Finally a case study was carried out and the results showed that this method was simple and effective.

关键词： projection pursuit model particle swarm algorithm investment decision

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GPU/QoE-Aware server selection using metaheuristic algorithms in multiplayer cloud gaming 18

GPU/QoE-Aware server selection using metaheuristic algorithm...

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Proceedings of the 16th Annual Workshop on Network and Systems Support for Games

作者： Hossein Ebrahimi Dinaki Shervin Shirmohammadi University of Ottawa Ottawa Canada

ISBN: (纸本)9781538660980

The combination of cloud computing and advancements in GPU have made many real time services possible, including Cloud Gaming (CG). Doing all the process-intensive tasks in the cloud frees players from upgrading their heterogeneous devices and installing new software, and lets them play wherever and whenever. However, higher quality is always demanded by players. For instance, as frame rate has major impact on the player's gaming performance, demand of higher frame rate is increasing. On the other hand, service providers aim to offer cost effective services. Management of the graphic-intensive CG service demand and maximizing the service providers' benefits is an issue that must be addressed properly. As remote GPU plays the main role of rendering and is the most expensive infrastructure rented by the service provider, its appropriate management is vital to address the above issue. To do so, we formulate the problem in an efficient manner and propose two methods to maximize both GPU utilization and the users' quality of experience (QoE) at the same time, subject to the constraints of the servers. Our methods are based on two metaheuristic algorithms to solve an NP-Hard optimization problem for GPU-based server selection. Our simulation results shows that by increasing the number of players, both algorithms have increasing performance in terms of GPU utilization, reduced capacity wastage, and QoE.

关键词： particle swarm algorithm cloud gaming frame rate quality of experience GPU utilization server selection genetic algorithm

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A Green Dispatch Model of Power System with Wind Energy Considering Energy-Environmental Efficiency

A Green Dispatch Model of Power System with Wind Energy Cons...

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International Conference on Life System Modeling and Simulation (LSMS) / International Conference on Intelligent Computing for Sustainable Energy and Environment (ICSEE)

作者： Chen, Daojun Liang, Liqing Zhang, Lei Zuo, Jian Zhang, Keren Li, Chenkun Guo, Hu State Grid Hunan Elect Power Corp Res Inst Changsha 410007 Hunan Peoples R China Hunan Xiangdian Test & Res Inst Co Ltd Changsha 410007 Hunan Peoples R China

ISBN: (纸本)9789811063640;9789811063633

With rising pressure caused by decreasing natural resources, there emerges stronger call for environmental protection and sustainable development. As a kind of environmental-friendly energy, wind power has been increasing in capacity around the world in recent years. As more and more wind power gets connected with the grid, its impact on power dispatch should also be considered. Based on traditional power system optimized dispatch, this paper introduces the energy-environmental efficiency concept into construction of a green dispatch model for wind incorporated power systems. The strategy takes both minimum resource consumption and best energy-environmental efficiency as indexes to assess the optimization of wind power incorporated systems from an environmental-friendly point of view. Fuzzy technology is adopted along with the tabu search-based PSO algorithm to solve the problem. It is proven that the proposed model is reasonable and of good practicality.

关键词： Wind power Green dispatch model Energy-environmental efficiency Tabu search particle swarm algorithm

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Multilevel thresholding selection based on the fireworks algorithm for image segmentation

Multilevel thresholding selection based on the fireworks alg...

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International Conference on Security, Pattern Analysis, and Cybernetics (ICSPAC)

作者： Chen, Hongwei Deng, Xingpeng Yan, Laiyi Ye, Zhiwei Hubei Univ Technol Sch Comp Sci Wuhan Hubei Peoples R China

ISBN: (纸本)9781538630167

With the increasing number of the threshold, the computation of the multilevel minimum cross entropy thresholding will increase exponentially, and the processing efficiency will be low, thus it is difficult to be applied in real-time processing. Some classical optimization algorithms, such as genetic algorithm, particle swarm algorithm has been used to deal with such problems, but it is easy for them to fall into the local optimal solution, the performance is not robust. In this paper, we use the minimum cross entropy to define the objective function of the optimal image segmentation thresholding, solve the optimization problem with the new intelligence optimization algorithm--fireworks algorithm, and compare it with other algorithm. The experimental results show that the fireworks algorithm can solve the problem of multilevel thresholds image segmentation with minimum cross entropy, which is a promising multilevel thresholding method, and it is not easy to fall into local optimal solution.

关键词： particle swarm algorithm minimum cross entropy image segmentation formatting

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Doppler estimation based on constructing the guide function of the objective function 7

Doppler estimation based on constructing the guide function ...

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IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC)

作者： Ning, Gengxin Ning, Qiuyan Wu, Lifei Zhang, Jun South China Univ Technol Sch Elect & Informat Engn Guangzhou Guangdong Peoples R China

ISBN: (纸本)9781538631423

This paper proposes a Doppler estimation algorithm for underwater acoustic communication by constructing the guide function of the objective function based on linear frequency modulation (LFM) signal. The algorithm employs the least square principle and the particle swarm method, to build the objective function and solves the global optimal solution, respectively. Computer simulations show that the proposed algorithm reaches theoretical performance hound, i.e. Cramer-Rao Lower Bound (CRLB) under a low SNR. The results also reveal that the proposed algorithm can achieve a better performance when compared with other algorithms.

关键词： Underwater acoustic communication CRLB Doppler estimation particle swarm algorithm

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The Improvement of Hybrid particle swarm algorithm and its application

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International Conference on Computational Materials Science (CMS 2011)

作者： Zou Shurong Ding Pengxin Zhang Hongwei Chengdu Univ Informat Technol Chengdu Peoples R China

ISBN: (纸本)9783037851555

Hybrid multi-objective particle swarm algorithm is applied to vehicle routing problem and achieved good results, this paper based on the previous work, dynamic inertia weight is added to the particle swarm algorithm with intelligence factors, it improved the global search ability and the capacity of local convergence of the particle swarm algorithm;and the idea of immunity is introduced in the algorithm,which makes the hybrid multi-objective particle swarm algorithm can effectively discard the repeated solutions in solving vehicle routing problems, this operation can improve the efficiency of the algorithm, and obtain better results under the same conditions.

关键词： vehicle routing problem particle swarm algorithm multi-objective inertia weight immunity

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