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IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS)

作者： Guo, Qiang Xue, Zhiwei Zhang, Longying Lu, Xiaohui Yin, Yue Huang, Congzhi State Grid Shanxi Elect Power Res Inst Taiyuan 030001 Shanxi Peoples R China State Grid Shanxi Elect Power Co Taiyuan 030001 Shanxi Peoples R China North China Elect Power Univ Sch Control & Comp Engn Beijing 102206 Peoples R China

ISBN: (纸本)9781538626184

The construction of the wind power curve is of great significance to the wind turbines. Based on the accurate model of wind power curve developed, it can be employed for the wind power prediction and fault diagnosis. Normally, the wind turbine manufacturer provides the standard wind power curve, which is measured at standard conditions. However, the actual situation of the wind turbine is different from the standard state and is constantly changing. The wind power curve needs to be modified. The wind power curve essentially establishes a functional relationship between wind speed and active power. The neural networks have the ability to approximate function. In this paper, based on the actual data from a wind farm in Shanxi Province, the wavelet neural network is used to model the wind power curve, and the initial parameters are determined by using the brain storm optimization algorithm. The probability of the non-convergence in the learning process of the wavelet neural network is greatly reduced. Extensive experimental results are presented to validate the effectiveness of the proposed approach.

关键词： Wind power curve Wavelet neural network brain storm optimization algorithm Power prediction

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Wind Turbine Pitch Control Based on Error-based ADRC Approach Optimized by brain storm optimization algorithm 1

Wind Turbine Pitch Control Based on Error-based ADRC Approac...

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1st International Conference on Industrial Artificial Intelligence (IAI)

作者： Huang, Congzhi Yin, Yue North China Elect Power Univ Sch Control & Comp Engn Beijing Peoples R China

ISBN: (纸本)9781728135939

Most pitch controllers of wind turbines are PI controllers in practical application. However, due to the nonlinear characteristic of pitch control process and inaccurate modeling of wind turbine, it is inevitable to study new and more effective controllers. In this paper, an error-based ADRC approach is designed based on the simplified two-mass model of wind turbine. The total disturbances, including the internal model uncertainties, and external disturbances, can be estimated by the constructed extended state observer, and then rejected by the PD controller in real time. The two parameters in the controller, including the observer bandwidth, and the controller bandwidth, are optimized by brain storm optimization algorithm. The experiment is carried out by FAST software. The results show that the control performance of error-based ADRC approach is much better than that of PI controller.

关键词： wind turbine error-based ADRC brain storm optimization algorithm FAST

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Mobile Robot Path Planning by Improved brain storm optimization algorithm

Mobile Robot Path Planning by Improved Brain Storm Optimizat...

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

作者： Tuba, Eva Strumberger, Ivana Zivkovic, Dejan Bacanin, Nebojsa Tuba, Milan Singidunum Univ Fac Informat & Comp Belgrade Serbia

ISBN: (纸本)9781509060177

Robots have found their purpose in various situations, from speeding the manufacturing processes to performing complicated tasks in dangerous and hostile environments. One of the important problems in robotics is mobile robot path planning. Robot path planning represents a hard optimization problem that needs to be solved in numerous applications. In this paper we propose path planning method in environments with static obstacles based on the recent swarm intelligence algorithm, brain storm optimization. The brain storm optimization algorithm was improved by local search procedure that each new candidate solution moves to the local best position thus reducing computational time. We tested the proposed method on several benchmark examples from the literature and it has been shown that our approach finds better and more consistent paths using less computational time.

关键词： robot path planning brain storm optimization algorithm swarm intelligence metaheuristic optimization

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Classification and Feature Selection Method for Medical Datasets by brain storm optimization algorithm and Support Vector Machine 7

Classification and Feature Selection Method for Medical Data...

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7th International Conference on Information Technology and Quantitative Management (ITQM) - Information Technology and Quantitative Management Based on Artificial Intelligence

作者： Tuba, Eva Strumberger, Ivana Bezdan, Timea Bacanin, Nebojsa Tuba, Milan Singidunum Univ Danijelova 29 Belgrade 11000 Serbia

Medicine is one of the sciences where development of computer science enables a lot of improvements. Usage of computers in medicine increases the accuracy and speeds up processes of data analysis and setting the diagnoses. Nowadays, numerous computer aided diagnostic systems exist and machine learning algorithms have significant role in them. Faster and more accurate systems are necessary. Common machine learning task that is part of computer aided diagnostic systems and different medical data analytic software packages is classification. In order to obtain better classification accuracy it is important to choose feature set and proper parameters for the classification model. Medical datasets often have large feature sets where many features are in correlation with others thus it is important to reduce the feature set. In this paper we propose adjusted brain storm optimization algorithm for feature selection in medical datasets. Classification was done by support vector machine where its parameters are optimized also by brain storm optimization algorithm. The proposed method is tested on standard publicly available medical datasets and compared to other state-of-the-art methods. By analyzing the obtained results it was shown that the proposed method achieves higher accuracy and reduce the number of feature needed. (C) 2020 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-ne-nd/4.0/) Peer-review under responsibility of the scientific committee of the 7th International Conference on Information Technology and Quantitative Management (ITQM 2019)

关键词： medical datasets classification feature selection support vector machine optimization swarm intelligence brain storm optimization algorithm

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Energy Efficient Sink Placement in Wireless Sensor Networks by brain storm optimization algorithm 14

Energy Efficient Sink Placement in Wireless Sensor Networks ...

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14th IEEE International Wireless Communications and Mobile Computing Conference (IWCMC)

作者： Tuba, Eva Simian, Dana Dolicanin, Edin Jovanovic, Raka Tuba, Milan Singidunum Univ Fac Informat & Comp Belgrade Serbia Lucian Blaga Univ Fac Sci Sibiu Romania State Univ Novi Pazar Dept Tech Sci Novi Pazar Serbia Hamad bin Khalifa Univ Qatar Environm & Energy Res Inst Doha Qatar

ISBN: (纸本)9781538620700

Wireless sensor networks represent one of the most promising technologies whose use has significantly increased in the past years. They are used in various applications such as health care monitoring, surveillance and monitoring in agriculture, industrial monitoring, habitat and underwater monitoring, etc. Deployment of the wireless sensor networks introduces number of hard optimization problems. Placement of the elements such as sensors, gateways, sinks and base stations, depend on different conditions and constraints such as signal propagation, distance, energy preservation, reliability. In this paper, we propose a method based on brain storm optimization algorithm for placing multiple sinks in a network consisting of regular sensors and gateways with higher battery capacity. Regular sensor nodes are statically organized in clusters around gateways considering not only distance but also energy efficiency. Gateways communicate with sinks for which optimal positions need to be determined. Location problems are in general difficult and in this case requirement of minimizing the energy additionally complicates the problem. The simulation results report estimation of the network life time. Obtained results have shown that our proposed method outperformed particle swarm optimization based method from literature in terms of mentioned metrics.

关键词： Wireless sensor networks Sink placement brain storm optimization algorithm Swarm intelligence Metaheuristic optimization algorithms

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Model on Oil Platform Using brain storm optimization algorithm 17th

Model on Oil Platform Using Brain Storm Optimization Algorit...

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17th International Conference on Hybrid Intelligent Systems (HIS)

作者： Margain, Lourdes Ochoa, Alberto Martinez Almaguer, Lissette Velazquez, Rigoberto Univ Politecn Aguascalientes Aguascalientes Mexico Univ Autonoma Ciudad Juarez Computo Aplicado Ciudad Juarez Mexico Univ Ciencias Informat Havana Cuba Univ Autonoma Sinaloa Culiacan Mexico

ISBN: (纸本)9783319763514;9783319763507

Logistics is a complex field, requires use bioinspired algorithms used to refer a solution of computational problems, based on the planning and implementation on existing models in the evolutionary process. However, other paradigms that can be taken in the creation of evolutionary algorithms also exist such as the forces of nature, which have been many algorithms based on water, gas and wind reactions. Many of the environments involving unstructured problems in this case a problem of accommodation vessels relate with survive supplies to an oil platform with limited resources, which can be considered from the perspective of brainstorm process. This process offer a wide range categorized models that ignore the possible solutions to the problem common situation in real life. The purpose of this research is to apply evolutionary computation properties of brain process to a problem related with technology, to corroborate through data mining analysis of how is the support of various companies which use technology and carry different types of goods deemed survival supplies. A model arrangement of supplies vessels was developed in order to enable learning of this intelligent logistics problem, modelling a problem from PEMEX (Oil Government Company in Mexico) using to resolve brain storm optimization algorithm.

关键词： brain storm optimization algorithm Data mining Modeling intelligent logistics problems

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Diameter Detection of Silicon Single Crystal Based on brain storm optimization algorithm Hough Transform 38

Diameter Detection of Silicon Single Crystal Based on Brain ...

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38th Chinese Control Conference (CCC)

作者： Kang, Lu Liu, Ding Xian Univ Technol Natl & Local Joint Engn Res Ctr Crystal Growth Eq Xian 710048 Peoples R China Shaanxi Key Lab Complex Syst Control & Intelligen Xian 710048 Peoples R China

ISBN: (纸本)9789881563972

In order to improve the speed and accuracy of silicon single crystal diameter detection. this paper proposes an ellipse detection method based on brain storm optimization algorithm Hough transform, this method uses brain storm optimization algorithm to search ellipse parameters that make the accumulator obtains the maximum value after using Hough transform, then the accurate measurement value of silicon single crystal diameter is obtained. The proposed algorithm is simulated on the complete ellipse with Gaussian white noise and silicon single crystal diameter detection engineering experiments, and the results are compared with other intelligent optimization algorithms. Experimental results show that the proposed algorithm can effectively improve the speed and accuracy of ellipse detection with Hough transform, thereby verify the feasibility and effectiveness of the proposed algorithm, and improve the speed and accuracy of silicon single crystal diameter detection.

关键词： Silicon single crystal Diameter detection Ellipse detection brain storm optimization algorithm Hough transform

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Neural Architecture Search Based on Improved brain storm optimization algorithm 14th

Neural Architecture Search Based on Improved Brain Storm Opt...

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14th International Conference on Swarm Intelligence (ICSI)

作者： An, Xiaojie Ma, Lianbo Zhou, Yuee Li, Nan Xing, Tiejun Wen, Yingyou Liu, Chang Shi, Haibo Northeastern Univ Coll Software Shenyang 110819 Peoples R China East China Univ Sci & Technol Minist Educ Key Lab Smart Mfg Energy Chem Proc Shanghai Peoples R China Neusoft Inst Intelligent Med Res Shenyang 110167 Peoples R China Northeastern Univ Sch Comp Sci & Engn Shenyang 110167 Peoples R China Chinese Acad Sci Digital Factory Dept Shenyang Inst Shenyang 110016 Peoples R China

ISBN: (纸本)9783031366215;9783031366222

The performance of deep neural networks (DNNs) often depends on the design of their architectures. But designing a DNN with good performance is a difficult and knowledge-intensive process. In this paper, we propose a neural architecture search method based on improved brain storm optimization (BSO) algorithm to efficiently deal with image classification tasks. BSO successfully transposes the human brainstorming process to design of optimization algorithms, which typically uses grouping, substitution, and creation operators to generate as many solutions as possible to approach the global optimization of the problem generation by generation. However, the BSO algorithm using clustering methods for grouping increases the computational burden, so we use the BSO algorithm based on simple grouping methods to solve the optimal architecture of the neural architecture search (NAS). We also redesigned the search space and designed an efficient encoding strategy for each individual.

关键词： Neural architecture search brain storm optimization algorithm Simple grouping method

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An improved brain storm optimization algorithm with new solution generation strategies for classification

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2022年第0期110卷 104677-104677页

作者： Xue, Yu Zhang, Qi Zhao, Yan Nanjing Univ Informat Sci & Technol Sch Comp & Software Nanjing 210044 Jiangsu Peoples R China

In recent years, brain storm optimization (BSO) algorithm has received much attention in solving classical optimization problems and is used to implement evolutionary classification models. However, in practical applications, large-scale datasets complicate the structure of the classification model, which can have a great impact on the classification performance. In the optimization process, the traditional single-strategy BSO cannot preserve the information of dominant solution well, and its generation strategy is inefficient in solving various complex practical problems. To solve this problem, we introduce feature selection to improve the optimization model structure. Meanwhile, in order to enhance the search capability of BSO, three new generation strategy are embedded in the BSO algorithm in this paper. With the three generation methods of global optimal, local optimal and nearest neighbor, the information of the dominant solution can be better preserved and the search efficiency can be improved. The performance of the proposed generation strategy in solving classification problems is demonstrated on ten datasets with different sizes and dimensions. The experimental results reveal that the new generation strategy can enhance the performance of BSO algorithm for solving classification problems.

关键词： brain storm optimization algorithm Classification Generation strategy Evolutionary classification optimization brain storm optimization algorithm Classification Generation strategy Evolutionary classification optimization

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An Agglomerative Greedy brain storm optimization algorithm for Solving the TSP

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IEEE ACCESS 2020年 8卷 201606-201621页

作者： Wu, Changyou Fu, Xisong Shandong Inst Business & Technol Sch Management Sci & Engn Yantai 264005 Peoples R China

The brain storm optimization algorithm(BSO) is a population based metaheuristic algorithm inspried by the human conferring process that was proposed in 2010. Since its first implementation, BSO has been widely used in various fields. In this article, we propose an agglomerative greedy brain storm optimization algorithm (AG-BSO) to solve classical traveling salesman problem(TSP). Due to the low accuracy and slow convergence speed of current heuristic algorithms when solving TSP, this article consider four improvement strategies for basic BSO. First, a greedy algorithm is introduced to ensure the diversity of the population. Second, hierarchical clustering is used in place of the k-means clustering algorithm in standard BSO to eliminate the noise sensitivity of the original BSO algorithm when solving TSP. Exchange rules for the individuals in the population individuals were introduced to improve the efficiency of the algorithm. Finally, a heuristic crossover operator is used to update the individuals. In addition, the AG-BSO algorithm is compared with the genetic algorithm (GA), particle swarm optimization (PSO), the simulated annealing(SA) and the ant colony optimization (ACO) on standard TSP data sets for performance testing. We also compare it with a recently improved version of the BSO algorithm. The simulations show the encouraging results that AG-BSO greatly improved the solution accuracy, optimization speed and robustness.

关键词： brain storm optimization algorithm traveling salesman problem hierarchical clustering optimization algorithm combinatorial optimization

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