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Service composition based on genetic algorithm and fuzzy rules

INTERNATIONAL JOURNAL OF KNOWLEDGE-BASED AND INTELLIGENT ENGINEERING SYSTEMS 2022年第3期26卷 201-217页

作者： Gheisari, Mohammad Reza Emadi, Sima Islamic Azad Univ Dept Comp Engn Yazd Branch Yazd Iran

The expansion of service-oriented architecture and the increasing number of web services has led to an increase in demand for their use. But since a single service alone may not be enough to meet the most relatively complex business processes requirements, it is necessary to combine several individual services to deliver user satisfaction. By increasing the number of services that have the same functionality, the quality of service provided by each service will play an important role in the service selection process;in the process of service composition, different services with different quality parameters come together to provide a new task. Therefore, offering the best quality service to the user is considered an important issue. The challenging issues in the service composition process include how to combine the web services with quality parameters based on user preference, long response time for the composition process, large search space, and the correlation between the services. In this paper, the quality-based service composition is modeled by considering the relationship between the services to improve the quality of service (QoS) parameters. The proposed model consists of several steps. In the first step, the inappropriate services will be pruned by applying the correlation between the services. In the second step, by determining the quality levels for the QoS, the APSO algorithm is used to select the best levels and, finally, the best services. In the service combination stage, the services selected from the previous stage are combined using a fuzzy genetic algorithm (FGA) to create a suitable combination service. The results show that when the correlation between the services is considered, the response time criterion improves significantly by integrating the quality parameters and pruning the candidate services, and reduces the search space.

关键词： Context awareness service correlation genetic algorithm fuzzy rules service composition particle swarm algorithm

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Fault Diagnosis in Shipboard Area Power Distribution Systems Based on PSO-SVM-CNN 3

Fault Diagnosis in Shipboard Area Power Distribution Systems...

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3rd International Conference on Electrical Engineering and Control Science, IC2ECS 2023

作者： Wang, Huimin Shi, Weifeng College of Logistics Engineering Shanghai Maritime University Shanghai China

ISBN: (纸本)9798350382426

Shipboard power systems are the core of maritime electrical automation, playing a crucial role in ensuring the normal operation of vessels. Efficient fault diagnosis facilitates rapid localization and judgment of faults, thereby safeguarding the safety of maritime operations. This research uses Simulink to simulate and analyze line faults in shipboard area distribution power systems. The simulation model is constructed to emulate common line faults in the power system. The study employs the Empirical Mode Decomposition (EMD) algorithm and Hilbert-Huang Transform as methods for extracting fault characteristics in shipboard power distribution systems. EMD is utilized to analyze the characteristic quantities of voltage and current fault signals, followed by the extraction of fault features from intrinsic mode functions through the Hilbert-Huang Transform. The extracted fault feature vectors serve as input parameters for a convolutional neural network (CNN) optimized using the particle swarm optimization (PSO) and support vector machine (SVM) to establish a fault diagnosis model. The analysis demonstrates that the PSO-SVM-CNN model significantly enhances the fault diagnosis accuracy of shipboard area power distribution systems, achieving a precision rate of up to 99.5%. This research contributes to advancing the safety and reliability of shipboard electrical systems through effective fault diagnosis methodologies. © 2023 IEEE.

关键词： component Convolutional Neural Network Empirical Mode Decomposition algorithm Hilbert-Huang Transform particle swarm algorithm Shipboard Area Power Distribution Electrical System

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Multi-objective optimal dispatch strategy for distribution networks with high-density PV and energy storage 3

Multi-objective optimal dispatch strategy for distribution n...

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3rd International Conference on Electrical Engineering and Control Science, IC2ECS 2023

作者： Wei, Liyan Qi, Zhiyuan Deng, Dezheng Wang, Qingbo State Grid Henan Electric Power Company Sanmenxia Power Supply Company Sanmenxia China State Grid Tianjin Dongli Power Supply Company Tianjin China Extra High Voltage Company of State Grid Qinghai Electric Power Company Xining China

ISBN: (纸本)9798350382426

To optimize high-density PV usage, integrating energy storage in the distribution network reduces peak and valley loads and mitigates grid voltage pressure from distributed PV. PV generation and energy storage work together, influencing the network's load distribution. Selecting appropriate energy storage and PV output strategies ensures efficient and stable network operation. Optimization focuses on minimizing network losses and voltage deviations, assigning comprehensive weights to these objectives. A particle swarm algorithm constructs the optimization model, determining the optimal dispatch strategy for PV and energy storage collaboration. This is exemplified through a simulation of a 30-bus model, highlighting the importance of coordinated dispatching. © 2023 IEEE.

关键词： distribution network optimization energy storage high density photovoltaic multi-target particle swarm algorithm

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Real-time Evaluation Model Construction of GIS Conductor Thermal State Based on PSO-BP Neural Network 3

Real-time Evaluation Model Construction of GIS Conductor The...

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3rd International Conference on Electrical Engineering and Control Science, IC2ECS 2023

作者： Jiao, Kerui Shen, Xiaojun Tongji University Department of Electrical Engineering Shanghai China

ISBN: (纸本)9798350382426

The thermal state of the conductors inside the GIS is an important indicator to assess its safety, but due to the closed nature of the GIS, it is often difficult to achieve a relatively fast monitoring. At present, multi-physics field simulation with the help of finite element method can realize the rapid monitoring of the thermal condition inside GIS, but some of its input parameters are difficult to obtain, and often can only' explore the causes from the results'. Meanwhile, it is difficult to guarantee the real-time performance. In this paper, we propose and construct a real-time evaluation model of conductor heating condition based on multi-physics field simulation and a BP neural network optimized by particle swarm algorithm. With the multi-physics field simulation model, parameters such as current, conductor resistance, gas pressure, and shell temperature rise are extracted as inputs, and contact resistance and conductor temperature rise are used as outputs to train the PSO- BP neural network to build the inverse model of GIS conductor heating state. The inversion system is highly accurate and real-time, reducing the input parameters that are difficult to obtain, and effectively realizing the improvement and enhancement from 'exploring the causes from the results' to 'deriving the results from the causes'. © 2023 IEEE.

关键词： GIS conductor particle swarm algorithm PSO-BP neural network real-time evaluation thermal state

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Optimizing particle swarm Optimization algorithm 27

Optimizing Particle Swarm Optimization Algorithm

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IEEE 27th Canadian Conference on Electrical and Computer Engineering (CCECE)

作者： Koohi, Iraj Groza, Voicu Z. Univ Ottawa Sch Informat Technol & Engn Ottawa ON Canada

ISBN: (纸本)9781479930999

particle swarm Optimization (PSO) algorithm has become more popular recently. It has been shown to be an effective optimization tool in most of the applications. In this paper, we have applied the PSO algorithm to a sample Artificial Neural Network (ANN) application, measured the improvement, and optimized the PSO parameters to improve results as much as possible. The application is character recognition of English numbers. Two indicators of accuracy of the results and processing time are taken in to account. The objective of this paper is to show that we can empirically adjust the PSO parameters to optimize PSO for the best results. Through several iterative processes of extracting improvements and adjusting the PSO parameters, we have recorded optimized PSO parameters and respective variances for similar applications. Indeed, the method can also be extended to alphabetic characters by just providing the input training patterns of each character. The details of the proposed approach and the simulation results are recorded in this paper.

关键词： parametric subharmonic oscillator particle swarm optimization particle swarm algorithm Character recognition paper processing time

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Active Power Distribution Hierarchical Energy Optimal Management of IoT Smart Power System

Active Power Distribution Hierarchical Energy Optimal Manage...

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2023 International Conference on Integrated Intelligence and Communication Systems, ICIICS 2023

作者： Yin, Xiang State Grid XinJiang Electric Power Co. Ltd Urumqi361024 China

ISBN: (纸本)9798350315455

With the development of modern power systems, the research on energy optimization management at the distribution level has also become a hot topic in recent years. In the microgrid system, two safety indicators, load power shortage rate and energy waste rate, are considered and incorporated into the model as constraints. The proposed energy optimization management method is verified by Python programming. To verify the advantages of the particle swarm optimization algorithm used in this paper in energy optimization management, the method in this paper is compared with the traditional real-time one-step optimization method. In this paper, the particle swarm algorithm is used, and the average node voltage deviation is about 0.01pu. The overall voltage evaluation index obtained is 25.86% lower than that of one-step optimization, and the overall optimization effect of reactive power voltage is better. © 2023 IEEE.

关键词： hierarchical energy optimization internet of things particle swarm algorithm smart power system

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The shortest path optimization based on mutation particle swarm optimization algorithm 4

The shortest path optimization based on mutation particle sw...

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4th International Conference on Intelligent System and Applied Material (GSAM)

作者： Li, Juan Wuhan Technol & Business Univ Wuhan 430065 Peoples R China

ISBN: (纸本)9783038353140

The traditional evolutionary algorithm is cannot converge faster to solve the path optimization problems, and the path that is computed is not the shortest path, in allusion to the disadvantage of this algorithm, a mutation particle swarm optimization algorithm is proposed. The algorithm introduces the adaptive mutation strategy, and accelerated the speed to search for the global optimal solution. For seven examples experiment in standard database, the result shows that the algorithm is more efficient..

关键词： The shortest path problem particle swarm algorithm adaptive

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Simplified expression and recursive algorithm of multi-threshold Tsallis entropy

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EXPERT SYSTEMS WITH APPLICATIONS 2024年第PartC期237卷

作者： Wang, Shaoxun Fan, Jiulun Xian Univ Posts & Telecommun Sch Commun & Informat Engn Xian 710121 Peoples R China Xian Univ Posts & Telecommun Ctr Image & Informat Proc Xian 710121 Peoples R China

Image segmentation is an important step in obtaining image information, and it has always been an extremely critical link in the fields of computer vision and pattern recognition. Because of its simplicity and effectiveness, threshold-based image segmentation technology has received significant attention and research by researchers. Tsallis entropy and Renyi entropy are two important global threshold selection methods in image thresholding. They not only have a special correspondence in mathematical expression, but also have a certain relationship in the field of image segmentation. Considering the number of thresholds increases, the complexity of the expression of Tsallis entropy criterion function also increases sharply, which is difficult to apply in multiple thresholds. Given this, this paper proposes a simplified expression of Tsallis entropy with multiple thresholds, which is proved by mathematical induction. Simultaneously, it is concluded that the multiple-threshold seg-mentation of Tsallis entropy and Renyi entropy with the same parameters is equivalent. To overcome the inconvenience of high calculation cost under multi-threshold and to improve calculation efficiency, recursive algorithms based on simplified Tsallis entropy and Renyi entropy under multi-threshold are given. The calcu-lation costs of the traditional Tsallis entropy and Renyi entropy threshold, simplified Tsallis entropy and Renyi entropy threshold, and the recursive simplified Tsallis entropy and Renyi entropy are compared through ex-periments. The results have revealed that the calculation time of simplified Tsallis entropy and Renyi entropy is shorter than the traditional methods, and the calculation time of the recursive simplified Tsallis entropy and Renyi entropy is much lower than that of the simplified expression. Finally, we use the three optimization al-gorithms including particle swarm, differential evolution and Harris hawks algorithm to solve the optimal so-lutions of the six exp

关键词： Multi -threshold segmentation Tsallis entropy Renyi entropy Recursive formula particle swarm algorithm Differential evolution algorithm Harris hawks algorithm

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Beam Tracking for Dynamic mmWave Channels: A New Training Beam Sequence Design Approach 20

Beam Tracking for Dynamic mmWave Channels: A New Training Be...

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20th International Symposium on Modeling and Optimization in Mobile, Ad hoc, and Wireless Networks (WiOpt)

作者： Zhang, Deyou Xiao, Ming Skoglund, Mikael KTH Royal Inst Technol Sch Elect Engn & Comp Sci S-10044 Stockholm Sweden

ISBN: (数字)9783903176492

ISBN: (纸本)9783903176492

In this paper, we develop an efficient training beam sequence design approach for millimeter wave MISO tracking systems. We impose a discrete state Markov process assumption on the evolution of the angle of departure and introduce the maximum a posteriori criterion to track it in each beam training period. Since it is infeasible to derive an explicit expression for the resultant tracking error probability, we turn to its upper bound, which possesses a closed-form expression and is therefore leveraged as the objective function to optimize the training beam sequence. Considering the complicated objective function and the unit modulus constraints imposed by analog phase shifters, we resort to the particle swarm algorithm to solve the formulated optimization problem. Numerical results validate the superiority of the proposed training beam sequence design approach.

关键词： Millimeter wave beam tracking training beam sequence design particle swarm algorithm

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CLASSIFICATION OF EDIBLE OILS BY INFRARED SPECTROSCOPY WITH OPTIMIZED K-MEANS CLUSTERING BY A HYBRID particle swarm algorithm

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ANALYTICAL LETTERS 2013年第17期46卷 2727-2738页

作者： Ren Haixia Lin Weiqi Shi Weimin Shen Qi Zhengzhou Univ Coll Chem & Mol Engn Zhengzhou 450052 Peoples R China Xiamen Prod Qual Supervis & Inspect Inst Xiamen Peoples R China

The K-means algorithm has some limitations including dead-unit properties, heavy dependence on the initial choice of cluster centers, convergence to local optima, and sensitivity to the number of clusters. This paper presents an efficient algorithm that optimizes K-means clustering by a hybrid particle swarm algorithm. The modified discrete algorithm is used to select variables and is continuously applied to update cluster centers simultaneously. The nearest center classification is then employed to classify the test samples. The proposed algorithm was applied to discriminate various edible oil varieties by employing Fourier transform infrared spectroscopy. As a comparison, the common K-means clustering, principal component analysis, and partial least squares techniques were also applied to classify these edible oil samples. Results demonstrated that the proposed method is an accurate and rapid strategy for identifying edible oils.

关键词： Edible oil FT-IR spectra K-means algorithm particle swarm algorithm

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