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A Novel Social Insect optimization algorithm for the Optimal Design of an Interior Permanent Magnet Synchronous Machine

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IEEE TRANSACTIONS ON MAGNETICS 2018年第12期54卷 1-6页

作者： Kwon, Hyuk-Sung Ro, Jong-Suk Jung, Hyun-Kyo Seoul Natl Univ Dept Elect & Comp Engn Seoul 08826 South Korea Chung Ang Univ Sch Elect & Elect Engn Seoul 06974 South Korea

optimization of an electric machine is a nonlinear multi-variable problem. For optimization of the nonlinear multi-variable problem, many function evaluations are required, which in turn requires much time. To address this problem, we propose a novel optimization algorithm of which the convergence speed, accuracy, and reliability are superior compared to those of widely used conventional algorithms. The performance of the proposed algorithm is verified through mathematical test functions and applied to a practical optimization scenario of cogging torque minimization for an interior permanent magnet synchronous machine.

关键词： Electric machine interior permanent magnet synchronous machine (IPMSM) multivariable problem nature-inspired optimization algorithms optimization algorithm

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A novel Master-Slave optimization algorithm for generating an optimal release policy in case of reservoir operation

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JOURNAL OF HYDROLOGY 2019年 577卷 123959-000页

作者： Turgut, Mert Sinan Turgut, Oguz Emrah Afan, Haitham Abdulmohsin El-Shafie, Ahmed Ege Univ Fac Engn Mech Engn Dept TR-35040 Izmir Turkey Izmir Bakircay Univ Fac Engn Mech Engn Dept TR-35665 Izmir Turkey Univ Malaya Fac Engn Civil Engn Dept Kuala Lumpur 50603 Malaysia

Dams and reservoirs provide decision-makers and managers with appropriate control on the available water resources, allowing the implementation of various strategies for the most efficient usage of the available water resources. In areas where water supply exhibits significant temporal variation when compared with the demand, the challenge is to bridge the gap and achieve an optimal match between the water supply and demand patterns. Therefore, the release of water from reservoirs should be controlled to ensure that the operation rule for the available water storage in the reservoir is optimized to satisfy the future water demands. This level of optimal control can only be achieved using an efficient optimization algorithm to optimally derive the operation rule for such a complex water system. Herein, two main methods have been considered to tackle this water resource management problem. First, three different optimization algorithms, namely particle swarm optimization, differential evolution, and whale optimization algorithm, have been applied. In addition, two different optimization algorithms, namely crow search algorithm and master-slave algorithm, have been introduced to generate an optimal rule for water release policy. Further, the proposed optimization algorithms have been applied to one of the most critical dam and reservoir water systems, namely the Aswan High Dam (AHD), which controls almost 95% of Egypt's water resources. The current operation of AHD using the existing optimization rules resulted in a mismatch between the water supply and water demand. In other words, the water availability could be higher than the water demand during a certain period, whereas it could be less than the water demand during another period. The results denoted that the master-slave algorithm outperforms the remaining algorithms and generates an optimization rule that minimizes the mismatch between the water supply and water demand.

关键词： Dam and reservoir operation optimization algorithm Aswan High Dam Crow search algorithm Master-slave algorithm

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Research on cross-layer design and optimization algorithm of network robot 5G multimedia sensor network

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INTERNATIONAL JOURNAL OF ADVANCED ROBOTIC SYSTEMS 2019年第4期16卷

作者： He, Yongqiang Yang, Mingming Henan Univ Engn Coll Comp Zhengzhou 451191 Henan Peoples R China Henan IoT Engn Res Ctr Smart Bldg Zhengzhou Henan Peoples R China

Cross-layer optimization based on maximizing the utility of network robot 5G multimedia sensor network is a systematic method for cross-layer design of wireless networks. It abstracts the functional and performance requirements of the layers in the protocol stack into objective functions and constraints in mathematical optimization problems. In this article, the cross-layer optimization problem of wireless Mesh networks using multi-radio interface multi-channel technology is studied. The optimization problem is modelled based on the network utility maximization method, and the corresponding algorithm is proposed. Based on the random network utility maximization method, the cross-layer optimization model of network robot 5G multimedia sensor network is established. Aiming at the time-varying randomness of random data flow and wireless propagation environment in network robot 5G multimedia sensor network, a model of joint congestion control and power control based on chance constrained programming is proposed, and its genetic algorithm is used to verify it. Reforming research will help speed up the practical pace of the field, with certain theoretical forward-looking and practical value.

关键词： Network robot 5G multimedia sensor cross-layer design optimization algorithm

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Fruit fly optimization algorithm based on a hybrid adaptive-cooperative learning and its application in multilevel image thresholding

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APPLIED SOFT COMPUTING 2019年 84卷 105704-000页

作者： Ding, Guoshen Dong, Fengzhong Zou, Hai Chinese Acad Sci Anhui Inst Opt & Fine Mech Anhui Prov Key Lab Photon Devices & Mat Hefei 230031 Anhui Peoples R China Univ Sci & Technol China Hefei 230061 Anhui Peoples R China Anhui Univ Sch Comp Sci & Technol Hefei 230601 Anhui Peoples R China

Multilevel thresholding is widely exploited in image processing, however, most of the techniques are time-consuming. In this paper, we present a novel approach, multilevel thresholding with fruit fly optimization algorithm (FOA). As yet, FOA has not been applied to resolve the complex image processing problems. Nevertheless, the merits of FOA were validated in former research, which include few parameters, simple structure, easy to understand and implement. Here, we introduce it into the study of multi-threshold image processing area. Moreover, we incorporate a hybrid adaptive-cooperative learning strategy with the proposed method called HACLFOA. The fruit fly population is divided into two sub-populations and both of them have a different iteration step range. In addition, each dimension of the solution vector will be optimized during one search, and we also make the best of the temporary global optimum information. The results of computational experiments on 24 benchmark functions demonstrate that the proposed algorithm has superior global convergence ability against other algorithms. Most significantly, extensive results show that the proposed algorithm is time-saving in multilevel image thresholding, and that it has great potential in the image processing field. (C) 2019 Elsevier B.V. All rights reserved.

关键词： optimization algorithm Fruit fly optimization algorithm (FOA) Hybrid adaptive-cooperative learning method Multilevel image thresholding

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Research on neural network optimization algorithm for building energy consumption prediction

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JOURNAL OF COMPUTATIONAL METHODS IN SCIENCES AND ENGINEERING 2018年第3期18卷 695-707页

作者： Chen, Song Ren, Ting-Ting Wu, Zhong-Cheng Chinese Acad Sci High Field Magnet Lab Hefei Anhui Peoples R China Univ Sci & Technol China Hefei Anhui Peoples R China Anhui Jianzhu Univ Coll Mech & Elect Engn Hefei Anhui Peoples R China

Building energy consumption prediction per month is an important content of building energy consumption management and company's financial budget. BP neural network with parameter optimization, network optimized by mind evolutionary algorithm, network optimized by genetic algorithm, network optimized by particle swarm algorithm and network optimized by adaptive weight particle swarm algorithm are used to forecast the energy consumption. The optimal values of the learning rate and hidden layer node number are choosen. The characteristics of various kinds of optimization algorithm are compared. The neural network optimized by adaptive weight particle swarm algorithm is proved to be the most accurate in predicting energy consumption.

关键词： BP neural network optimization algorithm energy consumption prediction

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AN OPTIMAL APPROACH FOR EEG/ERP NOISE CANCELLATION USING ADAPTIVE FILTER WITH OPPOSITIONAL WHALE optimization algorithm

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BIOMEDICAL ENGINEERING-APPLICATIONS BASIS COMMUNICATIONS 2019年第5期31卷

作者： Nagal, Rachana Kumar, Pradeep Bansal, Poonam AMITY Univ AMITY Sch Engn & Technol Noida 201303 Uttar Pradesh India Maharaja Surajmal Inst Technol Dept Comp Sci & Engn New Delhi 110086 India

In this paper, the Oppositional Whale optimization algorithm (OWOA) is applied to Adaptive Noise Canceller (ANC) for the filtering of Electroencephalography/Event-Related Potentials (EEG/ERP) signals. Performance of ANC will be improved by calculating the optimal weight value and proposed OWOA technique is used to update weight value. Adaptive filter's noise reduction capability has been tested through consideration of White Gaussian Noise (WGN) over contaminated EEG signals at various SNR levels (-10 dB, -15 dB and -20 dB). The performance of the proposed OWOA algorithm is assessed in terms of Signal to Noise Ratio (SNR) in dB, mean value, and the correlation between resultant and input ERP. In this work, ANCs are also implemented by utilizing conventional gradient-based techniques like Recursive Least Square (RLS), Least Mean Square (LMS) and other optimization algorithms such as Genetic algorithm (GA), Particle Swarm optimization (PSO) and WOA techniques. In average cases of noisy environment, comparative analysis shows that the proposed OWOA technique provides higher SNR value and significantly lower mean, and correlation as compared to gradient-based and swarm-based techniques. The comparative results show that extracting the desired EEG component is more effective in the proposed OWOA method. So, it has seen that OWOA-based noise reduction technique removing the artifacts and improving the quality of EEG signals significantly for biomedical analysis.

关键词： optimization algorithm Adaptive noise canceller White Gaussian noise Gradient based algorithm Nature inspired algorithm

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STUDY ON WAX DEPOSITION RATE optimization algorithm BASED ON LEVENBERG-MARQUARDT algorithm AND GLOBAL optimization

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FRONTIERS IN HEAT AND MASS TRANSFER 2019年 12卷

作者： Xiao, Rongge Zhu, Yue Jin, Wenbo Dai, Zheng Li, Shifang Zhang, Fan Xian Shiyou Univ Coll Petr Engn Shaanxi Key Lab Adv Stimulat Technol Oil & Gas Re Xian 710065 Shaanxi Peoples R China PetroChina Changqing Oilfield Shaanxi Xian 710018 Shaanxi Peoples R China China Huanqiu Contracting & Engn Co Ltd Beijing Huanqiu Corp Beijing Peoples R China

In order to accurately obtain the wax deposition rate model, according to the kinetic principle of wax deposition, several factors affecting the wax deposition rate were selected, and by a optimization software of First optimization(1stOpt), The parameters of two typical wax deposition rate models are solved respectively based on optimization algorithm combined by Levenberg-Marquardt (L-M) algorithm and global optimization and the calculated data were compared. The results show that: compared with the model parameters obtained by least squares method;the model parameters obtained by this optimization algorithm can describe the variation of wax deposition rate more accurately. The maximum error is reduced from 30% to 10%, and the average error is reduced from 10.3% to 2.42%;Alike, the mathematical model obtained by this optimization algorithm is also better than that solved by L-M algorithm alone. The maximum error is reduced from 13.62% to 11%, and the average error is reduced from 6.46% to 4.77%. To a certain extent, this optimization algorithm avoids the premature phenomenon caused by using Levenberg-Marquardt alone. In addition, the use of the optimization algorithm does not require suitable initial values, prior knowledge and programming, easy to use, and has important use value.

关键词： Oil pipeline Wax deposition rate mathematical model optimization algorithm error analysis

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The optimization algorithm of the Forced Current Cathodic Protection Base on Simulated Annealing

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algorithmS 2019年第4期12卷 83-83页

作者： Song, Chunfeng Wang, Mei Qin, Xuebin Wang, Pai Liu, Bao Xian Univ Sci & Technol Coll Elect & Control Engn Xian 710054 Shaanxi Peoples R China

The grounding grid of a substation is important for the safety of substation equipment. Especially to address the difficulty of parameter design in the auxiliary anode system of a grounding grid, an algorithm is proposed that is an optimization algorithm for the auxiliary anode system of a grounding grid based on improved simulated annealing. The mathematical model of the auxiliary anode system is inferred from the mathematical model of cathodic protection. On that basis, the parameters of the finite element model are optimized with the improved simulated annealing algorithm, thereby the auxiliary anode system of a grounding grid with optimized parameters is structured. Then the algorithm is proven as valid through experiments. The precision of the optimized parameters is improved by about 1.55% with respect to the Variable Metric Method and the Genetic algorithm, so it can provide a basis for parameter design in the auxiliary anode system of a grounding grid.

关键词： simulated annealing auxiliary anode system grounding grid optimization algorithm finite element model

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Detection and parameter estimation of R peaks in ECG signal using optimization algorithm 6

Detection and parameter estimation of R peaks in ECG signal ...

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6th International Symposium on Computing and Networking (CANDAR) - Across Practical Development and Theoretical Research

作者： Hsieh, Sheng-Ta Lin, Chun-Ling Oriental Inst Technol Dept Commun Engn New Taipei Taiwan Ming Chi Univ Technol Dept Elect Engn New Taipei Taiwan

ISBN: (纸本)9781538691847

Accurate R-peak detection is very important for arrhythmia diagnosis. Our previous effective R detection algorithm consisted of three strategies: band-pass filter, adaptive definition of interesting block and dynamic threshold. Then, it adopted the optimization algorithm to replace the knowledge-based theory and found out the suitable parameters (El, F2, N, WI, W2, beta and mu) in R detection algorithm quickly and obtained the high performance of detecting R peaks (99.77%). In order to improve the performance of the previous study, this study proposes to add the median filter in the algorithm to correct baseline wander components of electrocardiography (ECG) signals. It is necessary to defined two parameters (Ti and T2) in median filter. Therefore, this study adopts particle swarm optimization (PSO) to find the suitable parameters (Ti, T2, F1, F2, N, W1, W2, beta and mu) in the proposed method. The proposed method is applied to MIT-BIH arrhythmia database. The results show that PSO can find out the suitable parameters in R detection algorithm and have a higher accuracy (99.95%) than one of the previous study.

关键词： R-peak detection arrhythmia diagnosis optimization algorithm knowledge-based theory electrocardiography (ECG) particle swarm optimization (PSO) MIT-BIH arrhythmia database

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optimization Model and algorithm for Multi-Label Learning

Optimization Model and Algorithm for Multi-Label Learning

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作者： Zhengyang Li Fuzhou University

This paper studies a kind of urban security risk assessment model based on multi-label learning, which is transformed into the solution of linear equations through a series of transformations, and then the solution of linear equations is transformed into an optimization problem. Finally, this paper uses some classical optimization algorithms to solve these optimization problems,the convergence of the algorithm is proved, and the advantages and disadvantages of several optimization methods are compared.

关键词： Operations Research Multi-Label Learning Linear Equations Solving optimization algorithm

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