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Firefly algorithm with Proportional Adjustment Strategy 1

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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Fault Diagnosis of Modular Multilevel Converter Based on Optimized Support Vector Machine

Fault Diagnosis of Modular Multilevel Converter Based on Opt...

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第三十九届中国控制会议

作者： Longzhang Ke Zhenxing Liu Yong Zhang School of Information Science and Engineering Wuhan University of Science and Technology School of Electromechanical and Automobile Engineering Huanggang Normal University

In this paper, the open-circuit fault characteristics of IGBT in Modular multilevel converter（MMC） are studied. A fault diagnosis method based on average value of the three-phase ac current to extract fault characteristics, then an intelligent optimization algorithm for fault detection and location of support vector machine(SVM is proposed. When a fault occurs, the average value of three-phase ac current is no longer zero, which can detect the occurrence of the faulty SM. When the open-circuit fault occurs in the SMs of different bridge arms, the average value of three-phase ac current produces different dc bias components. According to the change rule of average value in three-phase current, the fault characteristic is extracted, and then the fault is classified and located by the SVM classifier optimized by genetic algorithm. Finally, Simulation and experimental results show that the proposed method can not only detect the fault block module quickly, but also improve the accuracy of fault classification.

关键词： Modular multilevel converter（MMC） fault diagnosis support vector machine（SVM） intelligent optimization algorithm

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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 ...

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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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A Survey on Route Planning Methods of AUV Considering Influence of Ocean Current

A Survey on Route Planning Methods of AUV Considering Influe...

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IEEE 4th International Conference on Control Science and Systems Engineering (ICCSSE)

作者： Sheng Liang Qiu Zhi-ming Li Heng Naval Univ Engn Coll Weaponry Engn Wuhan Hubei Peoples R China Naval Aviat Univ Coll Aviat Fdn Yantai Peoples R China Naval Res Acad Beijing Peoples R China

ISBN: (纸本)9781538678879

When AUV travel in the ocean, it will unavoidably be affected by the current. Different from the ground and air environment, the influence of ocean current on AUV route planning is much greater than that of wind to ground robot and air UAV path planning. Firstly, we describe the research status of intelligent optimization algorithm combined with ocean current in the field of AUV route planning and optimization in detail and analyze the planning performance and adaptive scenario of different algorithms in the flow environment. Secondly, the application of classical optimization and planning algorithm under the influence of ocean current in AUV route planning is reviewed. The analysis shows that the A* algorithm and the level set algorithm combined with the ocean current get more attraction. Thirdly, the difference between the path planning of the robot and route planning of the AUV is compared and the total research distribution of the AUV route planning algorithm considering the influence of the sea current after 2000 is given. Finally, we propose that the extension research based on A* algorithm and level set method should be the future development of AUV route planning in the real ocean environment.

关键词： AUV route planning ocean current intelligent optimization algorithm classical optimization and planning algorithm A* algorithm level set method

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Application Research Based on GA-FWA in Prediction of Sintering Burning Through Point

Application Research Based on GA-FWA in Prediction of Sinter...

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2018 International Conference on Computer, Communications and Mechatronics Engineering（CCME 2018）

作者： Dan-dan WANG Kai YANG Zhi-jun HE Ya-qiang YUAN Ji ZHANG School of Software University of Science and Technology Liaoning School of materials and metallurgy University of Science and Technology Liaoning

The sintering burn through point(BTP) is the most important parameter in the sintering process, which is usually used to evaluate the quality of sintered products. However, due to the sintering process is a complex physical and chemical reaction process, the specific sintering end point cannot be detected. In this paper, the fireworks algorithm based on genetic algorithm(GA-FWA) is applied to optimize the parameters in support vector machines(SVM). A new intelligent optimization algorithm is proposed to optimize the sintering terminal prediction model of support vector machines. The model uses MATLAB software to test the actual production data of a steel factory. Through a large number of experiments, the average relative error of the experimental results is 0.0778%, and the average absolute error is 0.0843. The accuracy of the experimental results is obviously higher than the other methods of predicting the sintering BTP. It can accurately predict the position of the sintering end point, greatly improve the quality and production of the sinter in the steel plant, and also prolong the service life of the sintering equipment and reduce the production cost.

关键词： Sintering Burning Though Point Support Vector Machine intelligent optimization algorithm Simulation Experiment

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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

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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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An Always Best Connected-Supported intelligent Handover Decision Mechanism Based on Improved Artificial Fish Swarm algorithm 17

An Always Best Connected-Supported Intelligent Handover Deci...

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2017 17th IEEE International Conference on Communication Technology (ICCT 2017)

作者： Jimei He Xingwei Wang Min Huang College of Software College of Computer Science & Engineering College of Information Science & EngineeringNortheastern University

ISBN: (纸本)9781509039456;9781509039449

In order to cope with the rapid development of the Internet and to solve the lack of IP address resources, this paper proposes an ABC(Always Best Connected)-supported intelligent handover decision mechanism based on Improved Artificial Fish Swarm algorithm(IAFSA). The proposed mechanism takes into consideration both the user side and the network side to support ABC better. We first utilize the triangular fuzzy number analytic hierarchy process to calculate the weight of the QoS parameter, which can ensure the rationality of the weight setting. Then, we introduce the game theory and the Nash equilibrium to consider the benefits of both network providers and end-users to ensure the fairness of handover decision. Finally, based on the NS2(Network Simulator 2) simulation platform, the IAFSA based intelligent handover decision mechanism is evaluated and compared with the existing handover mechanism in terms of utility, satisfaction, and suitability performance. Simulation results show that our mechanism is feasible and effective.

关键词： IAFSA ABC intelligent optimization algorithm handover decision

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intelligent greedy pursuit model for sparse reconstruction based on l₀ minimization

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SIGNAL PROCESSING 2016年 122卷 138-151页

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

l(0) minimization based sparse reconstruction is an NP-hard problem with very high computational complexity, which is difficult to be achieved by traditional algorithms. Although greedy algorithm aims at solving l(0) minimization, it is more likely to obtain a sub-optimal solution. In this paper, we propose an intelligent greedy pursuit (IGP) algorithm to solve the l(0) minimization essentially. Firstly, we propose a novel optimization function for the sparse reconstruction problem with the sparsity level unknown as a prior. Then, a two-cycle optimization algorithm is designed, whose object is to estimate the support collection and its corresponding coefficients intelligently and accurately by searching for the global optimal solution. To this end, we take advantage of intelligent optimization algorithm in global searching and solving combinatorial optimization problems to guide the intelligent estimation. Also, the principle of estimation is designed by the matching strategies of greedy algorithm which performs quite well in reconstruction speed. The so-called IGP model is as simple as greedy algorithm, while it has been proved through experiments that the performance of IGP for signal reconstruction and image reconstruction outperforms the state-of-the-art reconstruction algorithms. Moreover, IGP can reconstruct signal accurately with a relatively small measurement rate. (C) 2015 Elsevier B.V. All rights reserved.

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

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Predicted multi-variable intelligent matching pursuit algorithm for image sequences reconstruction based on l₀ minimization

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JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION 2016年 38卷 316-327页

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

In this paper, we study the problem of reconstructing image sequences which satisfy the conditions that (a) the sparsity level is high in the wavelet domain and (b) the sparsity pattern of adjacent images changes very slowly. The idea of the proposed method predicted multi-variable intelligent matching pursuit (PMIMP) algorithm is to use the estimated support collection of the previous image as prior information and then utilize the prior information to guide the current image reconstruction by solving 10 minimization. Multi-variable scheme is used to sample image sequences to enhance the guidance of prior information and improve the reconstruction accuracy with fewer measurements. to minimization is an NP-hard problem that requires exhaustively listing all possibilities of the original signal and is difficult to be achieved by traditional algorithms. To solve it, we take advantage of the intelligent optimization algorithm which is famous for its global searching ability and superior performance in solving combinatorial optimization problems. To improve the reconstruction speed, matching strategies of greedy algorithm, which performs quite well in reconstruction speed, are utilized to design the updating mechanism of PMIMP. As the sparsity level is hard to be estimated in image sequences reconstruction, we propose a novel optimization function which does not need the sparsity level known as a prior. We illustrate the reconstruction performance of our proposed method PMIMP on several image sequences and compare it with the state-of-the-art algorithms. The experimental results demonstrate that PMIMP achieves the best reconstruction performance in both PSNR, SSIM and visual quality with fewer measurements. (C) 2016 Elsevier Inc. All rights reserved.

关键词： Image sequences reconstruction Prior information Multi-variable sampling l(0) minimization intelligent optimization algorithm Matching strategies

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