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5th IIAI International Congress on Advanced Applied Informatics (IIAI-AAI)

作者： Song, Qun Fong, Simon Tang, Rui Univ Macau Dept Comp & Informat Sci Macau Macau Sar Peoples R China

ISBN: (纸本)9781467389853

swarm optimization algorithms are some of the most efficient ways to solve complexity optimization problems, especially the NP-hard problems. Wolf Search algorithm (WSA) is a new addition to the family of swarm optimization algorithms. However there are some inherent shortcomings of these algorithms, which is the performance depends heavily on the manually chosen parameters values. One possible approach to solve this limitation is using self-adaptive methods in parameter selection. In this paper we propose some self-adaptive method for WSA to facilitate automatic selection of parameter values. Differential evolution crossover function to iteratively fine-tune the parameters values. Both random and core-guided methods are attempted to choose the evolution agents. In this paper the self-adaptive methods are implemented, certain improvement is shown from the experimental results.

关键词： swarm optimization algorithm WSA Self-adaptive Differential Evolution

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Minimization of Power Pulsations in Traction Supply - Application of Ant Colony algorithm 24th

Minimization of Power Pulsations in Traction Supply - Applic...

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24th International Conference on Information and Software Technologies (ICIST)

作者： Kulesz, Barbara Sikora, Andrzej Zielonka, Adam Silesian Tech Univ Ul Akad 2 PL-44100 Gliwice Poland

ISBN: (纸本)9783319999722;9783319999715

In this paper a particular problem related to transformation of AC voltage into DC voltage used in tram supply is considered. Variable component is always present in rectified voltage. Pulsation of rectified voltage is influenced by different factors. In 12-pulse system, where two secondary transformer windings are used (one delta-connected, the other star-connected), an additional factor increasing the pulsation is the unbalance of the output voltages at these windings. Tap changer may be used to compensate (equalize) those voltages and its setting is optimized here by applying ant colony algorithm. Different supply voltage variants have been considered, with particular attention paid to distorted voltage, containing 5th and 7th harmonic. The effects of applying ACO algorithm are demonstrated.

关键词： swarm optimization algorithm Ant colony optimization Traction voltage Tram traction Voltage transformation Tap changer Distorted voltage Voltage unbalance Multi-winding transformer

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A Particle Pair optimization algorithm

A Particle Pair Optimization Algorithm

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3rd International Conference on Advances in Materials Manufacturing (ICAMMP 2012)

作者： Quan, Haiyan Liu, Zengli Kunming Univ Sci & Technol Fac Informat & Automat Kunming 650500 Peoples R China

ISBN: (纸本)9783037856482

The paper proposes a particle pair optimization (PPO) algorithm for numerical optimization. In the paper, a particle pair search(PPS) is designed. Based on PPS, the competitive roulette strategy is employed to the selection of particle pair from swarm, and a particle pair optimization algorithm is proposed. In the experiments, PPO is applied to solve the 12 benchmark problem, and compared with fast evolutionary programming (FEP). The results demonstrate that PPO algorithm can find optima or close-to-optimal solutions of the complex functions with high-dimension, and the search of PPO is stable and efficient.

关键词： swarm optimization algorithm particle pair search competitive roulette strategy numerical optimization

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Machine Learning Model Based on Improved DBO algorithm Optimized SVM 6

Machine Learning Model Based on Improved DBO Algorithm Optim...

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6th International Conference on Natural Language Processing (ICNLP)

作者： Zhou, You Liu, Tianyu Yang, Erkang Xian Univ Posts & Telecommun Sch Automat Xian Peoples R China

ISBN: (纸本)9798350349122;9798350349115

An innovative model on SVM is proposed, utilizing an enhanced version of the Dung beetle optimizer (DBO) algorithm in conjunction with Support Vector Machine (SVM) optimization. The enhanced DBO algorithm incorporates a Logistic chaotic mapping technique to improve population initialization, enhancing diversity and randomness. To further enhance its performance, strategies like Cauchy mutation and adaptive dynamic weighting are adopted to prevent premature convergence and local optima. This improved algorithm is then applied to SVM parameter optimization, resulting in improved classification performance. To validate the effectiveness of the proposed method, a model called HDBO-SVM is constructed and tested on the UCI dataset. The experimental results substantiate the outstanding performance of the HDBO-SVM model, showcasing a significant improvement compared to conventional models.

关键词： Machine Learning Support Vector Machine swarm optimization algorithm HDBO-SVM

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A Multi-Agent Intelligence Hybrid System Technique for Detection and Defense of DDoS Attacks

A Multi-Agent Intelligence Hybrid System Technique for Detec...

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Workshop on Computer Architecture, Embedded Systems, SoC, and VLSI/EDA / International Computer Symposium (ICS)

作者： Chen, Hsia-Hsiang Huang, Shih-Kun Natl Chiao Tung Univ Dept Comp Sci Hsinchu Taiwan Natl Chiao Tung Univ Informat Technol Serv Ctr Hsinchu Taiwan

ISBN: (纸本)9781614994848;9781614994831

In the paper, we propose the distributed detection and identification multi-agent system (DDIMAS) framework that is the first attempt to apply in solving distributed denial of service (DDoS) problem. It includes three stages which are information heuristic rule, meta-heuristic algorithm and backward and forward search (BFS) rule, respectively. Moreover, the framework is a flexible architecture that can incorporate into other algorithms or rules to improve the overall performance. From the evaluation design, the experiment results show that our method is with higher detection rate and better accuracy than standard repositories. The proposed framework resolves issues in other swarm optimization algorithms and reveals that the performance of DDIMAS is better than existing methods and the adaptive meta-heuristic algorithm framework outperforms other methods for detecting DDoS attacks.

关键词： distributed denial of service meta-heuristic algorithm swarm optimization algorithm

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A bearing fault image recognition method based on reliability analysis and RSO-optimized Alexnet

A bearing fault image recognition method based on reliabilit...

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2023 IEEE International Conference on Electrical, Automation and Computer Engineering, ICEACE 2023

作者： Gao, Shangbin Li, Jiale Zhang, Zhun Changsha University of Science and Technology Changsha China Xi'An Technological University Xi'an China Lanzhou Jiaotong University Lanzhou China

ISBN: (纸本)9798350309614

Aiming at the problem of insufficient feature extraction ability and credibility loss in the recognition of bearing fault image, a bearing fault diagnosis method based on credibility analysis and RSO (Rat swarm Optimizer) optimized Alexnet is proposed. Firstly, the collected bearing original vibration signal data is converted to a time-frequency graph by the method of signal conversion to image, and generate data sets. Secondly, the noise resistance of various lightweight network frames is tested, and the optimal network frame is obtained. Then, the parameters of the network framework are compared and adjusted by the progressive mesh method and the RSO algorithm. Finally, the interpretability and reliability characteristics of the network framework are illustrated by the Grad-CAM method. The experimental results of Case Western Reserve University bearing data set show that the proposed method can achieve close to 100% bearing fault image recognition accuracy and good anti-noise performance, and has excellent application prospects. © 2023 IEEE.

关键词： Alexnet model bearings fault diagnosis progressive grid method swarm optimization algorithm

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Breaking time control of back-to-back double E-type alternating current contactor

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INTERNATIONAL JOURNAL OF DISTRIBUTED SENSOR NETWORKS 2020年第6期16卷

作者： Qi, Lei Zong, Ming Hao, Shuyi Wang, Xuejie Shenyang Univ Technol Sch Elect Engn Shenyang 110870 Peoples R China Liaoning Prov Inst Safety Sci Shenyang Peoples R China State Grid Liaoning Elect Power Supply Co Ltd Shenyang Peoples R China Shenyang Inst Engn Sch Elect Power Shenyang Peoples R China

The arc generated by breaking the main circuit exacerbates the ablation of the contact, which affects the life of the contactor seriously. By controlling the breaking time of the contactor and making contact break in the optimum time zone are the effective ways to weaken the arc. In this article, the contact movement characteristic is concerned directly in order to control the contact breaking time. A back-to-back double-E alternating current contactor is proposed, and its structural parameters are optimized by particle swarm optimization algorithm. The mathematical model of circuit and magnetic is established. A closed-loop control system based on velocity feedback is proposed. The simulation results show that the designed contactor and the proposed control method are correct, and the actual velocity curve of the contact is consistent with the planned motion curve. It can realize zero-crossing breaking of the main circuit to achieve the effect of no-arc breaking.

关键词： Back to back contactor mathematical model swarm optimization algorithm movement characteristics closed-loop control breaking time no arc

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Solving dynamic multimodal optimization problems via a niching-based brain storm optimization with two archives algorithm

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swarm AND EVOLUTIONARY COMPUTATION 2024年 89卷

作者： Jin, Honglin Wang, Xueping Cheng, Shi Sun, Yifei Zhang, Mingming Lu, Hui Wu, Husheng Shi, Yuhui Shaanxi Normal Univ Sch Comp Sci Xian 710119 Peoples R China Shaanxi Normal Univ Sch Phys & Informat Technol Xian 710119 Peoples R China Univ Auckland Dept Mech & Mechatron Engn Auckland 1010 New Zealand Beihang Univ Sch Elect & Informat Engn Beijing 100191 Peoples R China Engn Univ PAP Coll Equipment Support & Management Xian 710086 Peoples R China Southern Univ Sci & Technol SUSTech Dept Comp Sci & Engn Shenzhen 518055 Peoples R China

Dynamic and multimodal properties are simultaneously possessed in the dynamic multimodal optimization problems (DMMOPs), which aim to find multiple optimal solutions in a dynamic environment. However, more work still needs to be devoted to solving DMMOPs, which still require significant attention. A nichingbased brain storm optimization with two archives (NBSO2A) algorithm is proposed to solve DMMOPs. The two niching methods, i.e. , neighborhood-based speciation (NS), and nearest-better clustering (NBC), are incorporated into a BSO algorithm to generate new solutions. The two archives preserve the optimal solutions that meet the requirements and practical, inferior solutions discarded during the generation. Improved taboo area (ITA) removes highly similar individuals from the population. An evolution strategy with covariance matrix adaptation (CMA-ES) is adopted to enhance the local search ability and improve the quality of the solutions. The NBSO2A algorithm and four other algorithms were tested on 12 benchmark problems to validate the performance of the NBSO2A algorithm on DMMOPs. The experimental results show that the NBSO2A algorithm outperforms the other compared algorithms on most tested benchmark problems.

关键词： Brain storm optimization Dynamic optimization Multimodal optimization Dynamic multimodal optimization Embodied swarm intelligence swarm optimization algorithm

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Correlation analysis-based thermal error control with ITSA-GRU-A model and cloud-edge-physical collaboration framework

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ADVANCED ENGINEERING INFORMATICS 2022年第0期54卷

作者： Yuan, Qiang Ma, Chi Liu, Jialan Gui, Hongquan Li, Mengyuan Wang, Shilong Chongqing Univ Coll Mech & Vehicle Engn Chongqing 400044 Peoples R China Chongqing Univ State Key Lab Mech Transmiss Chongqing 400044 Peoples R China

To improve the machining accuracy of machine tools, a correlation analysis-based thermal error control is realized on the basis of the improved tunicate swarm algorithm-gated recurrent unit-attention (ITSA-GRU-A) model and cloud-edge-physical collaboration framework. The memorizing, non-stationary, and nonlinear be-haviors of thermal errors are mathematically and numerically revealed by using the power series solution of the one-dimensional heat transfer equation and the finite element method of the three-dimensional spindle system. To adequately reduce the collinearities among temperatures, the variance inflation factor (VIF) is applied to conduct the grouping and clustering of input temperature variables for the first time. Then the final input is determined by the kernel-based R-vector (KBRV) coefficient and complex correlation coefficient (CCC). Finally, the ITSA-GRU-A model is proposed, and the attention mechanism is introduced to improve the predictive ability. The input variables are selected by the VIF, KBRV, and CCC. The ITSA is proposed to optimize the hyper -parameters of the GRU-A model, and the adaptive weights are introduced into the ITSA to reduce the compu-tation time. The proposed ITSA-GRU-A model has a more powerful predictive performance, generalization ability, and convergence than the GRU, GRU-A, and tunicate swarm algorithm (TSA)-GRU models. Finally, a cloud-edge-physical collaboration framework is proposed. The above algorithms are embedded into the cloud -edge-physical collaboration framework, and then the error control is realized by the collaboration framework and ITSA-GRU-A model. With the implementation of the error control system, the execution time and machining error is reduced significantly.

关键词： Correlation analyses Time -series prediction Recurrent neural network swarm optimization algorithm Cloud computing Edge computing

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Direct Position Determination Based on Neural Network Optimized by Particle swarm 21

Direct Position Determination Based on Neural Network Optimi...

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Proceedings of the 2021 4th International Conference on Artificial Intelligence and Pattern Recognition

作者： Jie Yang Changsheng Han Jingyi Zhang Xi'an University of Posts and Telecommunications China

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