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第33届中国过程控制会议

作者： Qi Yanying Hao Jiangyu Jiang Aipeng Zhang bo Kong Jundong Institute of Energy Utilization Systems and Automation Hangzhou Dianzi University School of Mathematics and Quantitative Economics Shandong University of Finance and Economics

In the application of moving horizon estimation(MHE) algorithm,the window length will affect the estimation accuracy and computing *** this kind of problem,a method of parameter optimization is proposed to obtain suitable window ***,in order to facilitate online solution,the optimization problem involved in the algorithm is transformed into a quadratic programming(QP) problem in matrix ***,for the time index and the estimated residual index that measure different properties,the normalization idea is adopted to incorporate them into the same dimension to design the fitness function,and a genetic optimization algorithm based on simulated annealing mechanism is given to search for the optimal window ***,the proposed parameter optimization method is verified by two *** results show that the parameter optimization method has the advantages of excellent local search ability and sufficient convergence,and the window length obtained by this method can better take into account the two performance indexes of the MHE algorithm and improve the estimation performance.

关键词： Moving horizon estimation Window length Quadratic programming Normalization 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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A data-driven approach for multi-objective unit commitment under hybrid uncertainties

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ENERGY 2018年 164卷 722-733页

作者： Zhou, Min Wang, Bo Li, Tiantian Watada, Junzo Nanjing Univ Sch Management & Engn Nanjing 210093 Jiangsu Peoples R China Waseda Univ Grad Sch Informat Prod & Syst Kitakyushu Fukuoka 8080135 Japan

Recent years, renewable energy has taken growing penetration in power systems due to the energy shortage and environmental concerns. As a result, system operators encounter increasing difficulties in solving unit commitment optimization. In this paper, a data-driven unit commitment model is proposed to handle the hybrid uncertainties of wind power and future load. First, a non-parameter kernel density method is utilized to represent the above hybrid uncertainties, and a novel bandwidth selection strategy for the above method is then proposed to capture the inherent correlation between uncertainty representation and unit commitment. Second, a Monte Carlo simulation is developed to integrate the hybrid uncertainties into Value-at-Risk to get a comprehensive system reliability measurement. Third, considering that system operators might be interested in the inherent conflict between reliability and economy, minimizing operation costs and maximizing system reliability are taken as two objectives in the model. To get more practical schedules, the transmission line constraint is considered as well when building the mathematical model. Additionally, by integrating the reinforcement learning mechanism, a novel multi objective particle swarm optimization algorithm is proposed to solve the complicated nonlinear model. Finally, several experiments were performed to demonstrate the effectiveness of this research. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Multi-objective unit commitment Non-parameter kernel density method Hybrid uncertainties Reinforcement learning-based particle swarm optimization algorithm

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A Novel Hybrid ABC-PSO algorithm for Effort Estimation of Software Projects Using Agile Methodologies

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JOURNAL OF INTELLIGENT SYSTEMS 2018年第3期27卷 489-506页

作者： Thanh Tung Khuat My Hanh Le Univ Sci & Technol Univ Danang Danang Vietnam

In modern software development processes, software effort estimation plays a crucial role. The success or failure of projects depends greatly on the accuracy of effort estimation and schedule results. Many studies focused on proposing novel models to enhance the accuracy of predicted results;however, the question of accurate estimation of effort has been a challenging issue with regards to researchers and practitioners, especially when it comes to projects using agile methodologies. This study aims at introducing a novel formula based on team velocity and story point factors. The parameters of this formula are then optimized by employing swarm optimization algorithms. We also propose an improved algorithm combining the advantages of the artificial bee colony and particle swarm optimization algorithms. The experimental results indicated that our approaches outperformed methods in other studies in terms of the accuracy of predicted results.

关键词： Software effort estimation agile software development user story particle swarm optimization artificial bee colony swarm optimization algorithm

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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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LDDoS Attack Detection by Using Ant Colony optimization algorithms

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JOURNAL OF INFORMATION SCIENCE AND ENGINEERING 2016年第4期32卷 995-1020页

作者： Chen, Hsia-Hsiang Huang, Shih-Kun Natl Chiao Tung Univ Dept Comp Sci Hsinchu 300 Taiwan Natl Chiao Tung Univ Informat Technol Serv Ctr Hsinchu 300 Taiwan

Internet service providers and equipment vendors are subject to cyber threats. One of the most prevalent security threats is the distributed denial of service (DDoS) attack. In a DDoS attack, the attack traffic and attacker's IP address are respectively difficult to detect and trace. This is because attack traffic is similar to regular traffic and the attack is executed by multiple attackers. This study focused on solving the low-rate distributed denial of service (LDDoS) problem;this problem is difficult to detect and trace compared with a DDoS attack. We therefore propose a novel distributed detection and identification ant colony system (DDIACS) framework, which is an ant-colony-optimization based metaheuristic technique, for solving the LDDoS problem. The DDIACS framework comprises three stages, which entail an information heuristic rule, a multiagent algorithm, and a backward and forward search method. Moreover, the DDIACS framework is compliant with the emerging software defined network (SDN) because in this framework, a control plane and data plane are used to monitor and manage the network topology. The proposed framework demonstrates SDN advantages such as enabling networks to exhibit flexibility, fast convergence, and robustness in overcoming complicated multi-attacker problems. In addition, this study investigated the time and space complexity of the DDIACS framework and compared this framework with the swarm optimization algorithm and probabilistic packet marking. This study designed the network topology by using the data set from the DARPA and KDD repository. The simulation results show that the proposed framework resolves the problems in using other algorithms and that the DDIACS framework demonstrates better performance than existing methods;furthermore, the adaptive metaheuristic algorithm outperforms other methods in thwarting an LDDoS attack. The detection rate is about 89% and the accuracy is greater than 83%.

关键词： distributed denial of service (DDoS) low-rate distributed denial of service (LDDoS) ant colony system ant colony optimization (ACO) metaheuristic technique software defined network (SDN) swarm optimization algorithm probabilistic packet marking (PPM)

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Self-Adaptive Wolf Search algorithm 5

Self-Adaptive Wolf Search Algorithm

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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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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 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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Tetragonal high-pressure phase of InI predicted from first principles

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PHYSICA B-CONDENSED MATTER 2012年第3期407卷 398-402页

作者： Zhang, Meiguang Yan, Haiyan Zhang, Gangtai Wei, Qun Wang, Hui Baoji Univ Arts & Sci Dept Phys & Informat Technol Baoji 721016 Peoples R China Baoji Univ Arts & Sci Dept Chem & Chem Engn Shaanxi Key Lab Phytochem Baoji 721013 Peoples R China Xidian Univ Sch Sci Xian 710071 Peoples R China Jilin Univ Natl Lab Superhard Mat Changchun 130012 Peoples R China

Ab initio particle swarm optimization algorithm for crystal structural prediction was employed to uncover the high-pressure crystal structure of indium iodide (InI). We have predicted one tetragonal high-pressure phase for InI with P4/nmm symmetry, which is energetically much superior to the previously proposed CsCl-type structure. The P4/nmm-InI possesses alternative stacking of double I and In layers. The arrangement of adjacent I and In layers of P4/nmm-InI is similar to that of the CsCI-type structure. The calculated electronic density of states supports a metallic character for this tetragonal phase that is similar to the high-pressure behavior of IIA-VIB families. Furthermore, the phase transition path from the ambient pressure TII-InI -> P4/nmm-InI has been discussed. (C) 2011 Elsevier B.V. All rights reserved.

关键词： InI swarm optimization algorithm High pressure transition First principles

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