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检索条件"主题词=Intelligent Optimization Algorithm"

共 181 条记录，以下是111-120 订阅

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Study of Short-term Load Forecasting in Big Data Environment 29

Study of Short-term Load Forecasting in Big Data Environment

引用

第29届中国控制与决策会议

作者： Haifan Zhao Zhaohui Tang Weidong Shi Zixun Wang School of Information Science and Engineering Central South University

ISBN: (纸本)9781509046584

This paper presents an improved support vector machine method based on the Hadoop,which can solve the problem of short-term load forecasting in the big data *** the control design,the fuzzy C clustering model of quantum behaved particle swarm optimization is used to analyze the historical load ***,the load data and the influencing factors data are stored and matched in Cloud platform,through this way which can improve the accuracy of the input data and the speed of the *** last,the model is *** to the certain area load big data,the improved support vector machine forecast model and the normal support vector machine forecast model are compared in forecasting accuracy and forecasting *** results indicate that Hadoop-SVM has better predictive ability and faster computing time when processing the big data.

关键词： Load forecasting intelligent optimization algorithm support vector machine cloud platform

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A Bilevel Genetic algorithm for Global optimization Problems 14th

A Bilevel Genetic Algorithm for Global Optimization Problems

引用

14th International Conference on Swarm Intelligence (ICSI)

作者： Lun, Ziru Ye, Zhanyu Liu, Qunfeng Dongguan Univ Technol Dongguan 532000 Peoples R China

ISBN: (纸本)9783031366215;9783031366222

Genetic algorithm is an important intelligent optimization algorithm that operates on specific population by simulating the natural evolution process and using artificial evolution to continuously optimize the population so as to search for the optimal solution. At present, there are a large number of methods focus on improving genetic algorithms, but the current stage of genetic algorithm tends to have the problems of falling into local optimal premature and slow convergence. In this paper, we try to design a bilevel evolutionary particle swarm optimization algorithm based on the idea of genetic algorithm within the framework but without increasing the complexity, using a data-driven idea, and verify it by the genetic algorithm in the commercial software MATLAB. Numerical experiments showthat the data-driven a bilevel genetic algorithm-based algorithm significantly improves the algorithm performance.

关键词： intelligent optimization algorithm genetic algorithm bilevel evolution.s

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A Novel Feature Selection Method Based on Salp Swarm algorithm

A Novel Feature Selection Method Based on Salp Swarm Algorit...

引用

IEEE International Conference on Information Communication and Software Engineering (ICICSE)

作者： Yan, Chaokun Suo, Zhihao Guan, Xinyu Luo, Huimin Henan Univ Sch Comp & Informat Engn Kaifeng Peoples R China

ISBN: (纸本)9781665419192

Biomedical and clinical data usually contain some redundant and irrelevant features, which may lead to misleading and over-fitting problems in the process of modeling algorithms. In order to effectively remove irrelevant or redundant features, the use of feature selection methods can reduce the number of features, improve the accuracy of the model, and reduce the running time. In recent years, Wrapper-based feature selection algorithms have received widespread attention because they can obtain better accuracy. This paper uses a wrapper feature selection algorithm FS_SSA based on Salp swarm. In the FS_SSA algorithm, the position of the follower salps is updated by the relative position of the Salp. The followers gradually moves to the leading Salp. The gradual movement of the follower salps can make the Salp Swarm algorithm not easy to fall into a local optimal state. The two behaviors of exploration and development in subset searches are balanced, and the search process of feature subsets is prevented from falling into the local optimum. Experimental results based on public medical data sets show that the FS_SSA has better classification performance than other methods.

关键词： feature selection intelligent optimization algorithm salp swarm algorithm

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Optimal Design of Heliostat Field Layout Based on Improved Sparrow Search algorithm 20th

Optimal Design of Heliostat Field Layout Based on Improved S...

引用

20th International Conference on intelligent Computing (ICIC)

作者： Li, Weikun Huang, Longzhao Chen, Xiaojie Jia, Maoning Zhang, Wenhui Guilin Univ Elect Technol 1 JinJi Rd Guilin 541000 Guangxi Peoples R China

ISBN: (纸本)9789819755776;9789819755783

The heliostat field of a tower solar thermal power tower accounts for 40% to 50% of the total cost, so optimizing the layout of the heliostat field is crucial. This paper constructs a mathematical model of the optical efficiency of the heliostat field based on the radial staggered layout, and uses the annual average optical efficiency of the heliostat field as the objective function to propose a sparrow search algorithm ESSA based on the elite reverse learning strategy: after the sparrow update, take the top 10% of the fitness rankings of the sparrows as the elite solutions, and obtain the dynamic boundaries of the elite sparrows. Use the reverse learning strategy to solve the reverse solution and obtain the optimal value. Finally, taking the Spanish Gemasolar tower power plant as an example, the SSA and ESSA algorithms were used to optimize the objective function. The experimental results showed that after optimization using ESSA, the optical efficiency increased by about 2% and the shadow occlusion efficiency increased by about 4%, which effectively improved the concentrating efficiency of the heliostat field.

关键词： intelligent optimization algorithm sparrow search algorithm elite reverse learning strategy heliostat field

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Large-scale Path Planning and Time Window Allocation in UAV-assisted Wireless Sensor Networks with Variational Multi-dimensional optimization 20

Large-scale Path Planning and Time Window Allocation in UAV-...

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IEEE 20th International Conference on Automation Science and Engineering (CASE)

作者： Zhang, Boyuan Lai, Rui Gong, Guanghong Yuan, Haitao Yang, Jinhong Zhang, Jia Zhou, MengChu Beihang Univ Sch Automat Sci & Elect Engn Beijing 100191 Peoples R China CSSC Syst Engn Res Inst Beijing Peoples R China Southern Methodist Univ Lyle Sch Engn Dept Comp Sci Dallas TX 75205 USA New Jersey Inst Technol Dept Elect & Comp Engn Newark NJ 07102 USA

ISBN: (纸本)9798350358513;9798350358520

Wireless Sensor Networks (WSNs) have been widely deployed, and it is crucial to enhance their network longevity and transmission efficiency. This work comprehensively considers communication interference, transmission rate constraints, and motion constraints of Unmanned Aerial Vehicles (UAVs). We propose an energy-efficient UAV-assisted WSN framework with big data transmission. To minimize the weighted sum of UAV's and WSNs' energy consumption, we jointly optimize sensor clustering, path planning, and time window allocation by an improved intelligent optimization algorithm assisted by deep learning named Variational Multidimensional optimization (VMO) with co-evolved Multiple Subpopulations, noted as VMOMS for short. The effectiveness of the proposed VMOMS algorithm for solving highdimensional problems is demonstrated through numerical analysis and simulation results. These findings highlight the efficiency and practicality of the designed UAV-assisted hierarchical architecture of WSNs, thereby showcasing its potential to enable reliable data transmission from remote WSNs to a centralized cloud server.

关键词： UAV-assisted WSNs high-dimensional optimization intelligent optimization algorithm deep learning

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GASA Based Signal Reconstruction for Compressive Sensing 5

GASA Based Signal Reconstruction for Compressive Sensing

引用

Fifth International Conference on Instrumentation & Measurement, Computer, Communication, and Control (IMCCC)

作者： Li, Dan Wang, Qiang Shen, Yi Harbin Inst Technol Dept Control Sci & Engn 92 West Da Zhi St Harbin 150001 Peoples R China

ISBN: (纸本)9781467377232

Reconstruction, which is the core of compressive sensing (CS), can be implemented by l(0) norm minimization. In practice, l(0) norm minimization is a NP-hard problem that requires exhaustively listing all possibilities of the original signal and is difficult to achieve by traditional algorithms. This paper proposes a signal reconstruction algorithm combining genetic algorithm with simulated annealing algorithm which is famous for their superior performance in solving combinatorial optimization problems. The method in this paper can solve l(0) norm minimization directly and can reconstruct noiseless signal accurately. It has been proved through numerical simulations that the theoretical optimization performance for signal reconstruction can be achieved. The quality of reconstruction based on the proposed method is superior to that of OMP, smooth l(0) norm (SL0) algorithm, Lasso and BP algorithm.

关键词： Compressive sensing l(0) minimization intelligent optimization algorithm Signal reconstruction

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Research on Intelligence optimization of Web Service Composition for QoS

Research on Intelligence Optimization of Web Service Composi...

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3rd International Conference on Information Computing and Applications (ICICA 2012)

作者： Li, Jianjun Yu, Bin Chen, Wuping Zhengzhou Informat Sci & Technol Inst Zhengzhou 450004 Peoples R China

ISBN: (纸本)9783642340406

With quality of service as the restraint, in accordance with the features of service composition, this paper proposes an intelligent optimization algorithm for Web service composition. By combining a wide search range of shuffled frog leaping algorithm and high accuracy of particle swarm optimization algorithm, this algorithm can find the best one from a lot of service composition schemes. Simulation results show that the algorithm designed by this paper can overcome the low accuracy of shuffled frog leaping algorithm and instability of particle swarm optimization algorithm, and can find the better service composition scheme in all cases.

关键词： Web Service Composition Shuffled Frog Leaping algorithm Particle Swarm optimization algorithm intelligent optimization algorithm Quality of Service

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Firefly algorithm with Proportional Adjustment Strategy 1

引用

5th International WISE Workshop on Data Quality and Trust in Big Data (QUAT)

作者： Wang, Jing Liu, Guiyuan Song, William Wei Jiangxi Univ Finance & Econ Sch Software & Internet Things Engn Nanchang Jiangxi Peoples R China Dalarna Univ Borlange Sweden

ISBN: (数字)9783030191436

ISBN: (纸本)9783030191436;9783030191429

Firefly algorithm is a new heuristic intelligent optimization algorithm and has excellent performance in many optimization problems. However, in the face of some multimodal and high-dimensional problems, the algorithm is easy to fall into the local optimum. In order to avoid this phenomenon, this paper proposed an improved firefly algorithm with proportional adjustment strategy for alpha and beta. Thirteen well-known benchmark functions are used to verify the performance of our proposed algorithm, the computational results show that our proposed algorithm is more efficient than many other FA algorithms.

关键词： Firefly algorithm Meta-heuristic algorithm intelligent optimization algorithm Global optimization

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Environmental/Economic Dispatch Approach for Microgrid with Uncooperative Game Control Strategy

Environmental/Economic Dispatch Approach for Microgrid with ...

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International Conference on Power System Technology (POWERCON)

作者： Chen, Jie Fu, Rong Nanjing Univ Posts & Telecommun Coll Automat Nanjing 210000 Jiangsu Peoples R China

ISBN: (纸本)9781538664612

This article aims to explore and exploit the comprehensive optimization of environmental and economic in the power generation process by applying relevant knowledge of the game theory in the background of optimization scheduling problem in the microgrid system. Furthermore, in order to solve the contradiction between environmental goals and economic goals in microgrid dispatching, the non-cooperative game models of distributed generators based on Carbon trading are proposed using weight coefficient method. The Nash equilibrium solution of each distributed generators output is obtained when the game model is solved by the particle swarm algorithm to obtain the maximum return. Simulation results demonstrate the effectiveness of the environmental/economic scheduling strategy.

关键词： Environmental/economic dispatch game strategy carbon trading intelligent optimization algorithm

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Research of Conical Conformal Array Antenna Based on Hybrid algorithm 10

Research of Conical Conformal Array Antenna Based on Hybrid ...

引用

International Conference on Microwave and Millimeter Wave Technology (ICMMT)

作者： Yao, Qunlei Wang, Ting Liu, Yuhe Liu, Jiang Beijing Inst Technol Sch Mechatron Engn Beijing 100081 Peoples R China

ISBN: (纸本)9781538624166

The synthesis of the array antenna radiation pattern is an important part of the array analysis process. In this paper, a hybrid algorithm is used to optimize the radiation pattern of the 4x4 cone con-formal array to realize beam scanning. This algorithm introduces the selection mechanism of swarm intelligence optimization algorithm, and combines it with the optimal selection mechanism to improve the convergence speed of the algorithm. Furthermore, the union optimization operation of the excitation phase and the amplitude is realized by adopting the normal factor and multi-point cross-over of two-dimensional window. Compared with the classical genetic algorithm, the simulation results show that the algorithm can well balance the relationship between the parameters when multiple parameters are optimized simultaneously, and realize radiation pattern synthesis of the fixed pointing angle.

关键词： intelligent optimization algorithm Cone Conformal Array Beam Scanning

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