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Electrochemical Model Parameter Estimation of a Lithium-Ion Battery Using a metaheuristic algorithm: Cascaded Improved Harmony Search

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IFAC-PapersOnLine 2018年第28期51卷 409-413页

作者： Chun, Huiyong Han, Soohee Pohang37673 Korea Republic of

For the safe and accurate operation of lithium-ion battery, the estimation of inner states of battery is necessary. Among many models of battery, electrochemical model, especially P2D model, represents the real dynamics such as transport and diffusion phenomena of charges and lithium-ions. For this reason, P2D model is an appropriate model for estimating the states. In this paper, metaheuristic algorithm is applied to estimate these parameter values, since it is more useful to implement the metaheuristic method than other jacobian-based methods, if the number of the parameters to be estimated is increased. Harmony search is one of the metaheuristic algorithms, and improved harmony search was developed for high convergence rate and fine-tunning. The cascaded version of improved harmony search is proposed as the estimation algorithm in this paper, and it is the first time to using the harmony search for lithium-ion battery parameterization. As a result, the parameters had reasonable values compared to reference values and lower voltage, temperature errors compared to the results of improved harmony search which is employed as a benchmark. © 2018

关键词： Lithium-ion batteries Ions Parameter estimation Appropriate models Convergence rates Electrochemical modeling Estimation algorithm Improved harmony searches meta heuristic algorithm meta heuristic methods Transport and diffusions

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Online Learning algorithm for LSSVM Based Modeling with Time-varying Kernels

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IFAC-PapersOnLine 2018年第18期51卷 626-630页

作者： Kong, Weijian Ding, Jinliang Department of Automation Donghua University Shanghai China State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang China

Online learning based Least Squares Support Vector Machine (LSSVM) can address the modeling problems of a time-varying process, which has a few advantages such as low training time and good general. Nevertheless, many of online learning algorithms cannot adapt the kernel parameters for the time-varying characteristic, so the inferred LSSVM models are low-accuracy. An online learning algorithm with time-varying kernels is proposed to improve online training accuracy of LSSVM model. The kernel parameters are optimized along with time-varying process using updating samples data. To achieve reliable performance during online optimization, we propose a controllable metaheuristic algorithm that adopts a contracted particle swarm optimization with an elaborate chaotic operator. The proposed modeling approach is utilized in the energy efficiency prediction of the electrical smelting process, and the experimental results show that the proposed online learning algorithm can both improve the accuracy of LSSVM model and ensure low online training time. © 2018

关键词： Learning algorithms E learning Energy efficiency Learning systems Online systems Particle swarm optimization (PSO) Support vector machines LSSVM meta heuristic algorithm Online learning Online optimization Time varying process

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Practical impedance estimation in low-voltage distribution network

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IFAC-PapersOnLine 2015年第30期48卷 312-315页

作者： Han, Sekyung Kodaira, Diasuke Han, Soohee Baek, Jaemin Department of Electrical Engineering Kyungpook National University Daegu Korea Republic of Department of Science and Engineering Tokyo Institute of Technology Tokyo Japan Department of Creative IT Engineering POSTECH Pohang Korea Republic of

This paper proposes a method of estimating the impedances between nodes in a low-voltage distribution network. This method works without any information on the phase differences between nodes. It only requires measurements that can be easily obtained and shared from existing devices and communication technologies such as power line communication(PLC). In other words, it is not necessary to carry out synchronized real-time measurement for observing phase differences. From governing equations based on the basic circuit theory, the mathematical optimization problem is formulated to solve them and then obtain the estimates of the impedances among nodes. To find a solution to the nonconvex and nondifferentiable optimization problem, a representative metaheuristic algorithm, called the particle swamp optimization(PSO), is employed with a new biquadratic and linear hybrid cost function that is proper for fast convergence both far away from and near an optimal point. © 2015, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

关键词： Voltage distribution measurement Carrier transmission on power lines Circuit theory Cost functions Estimation Particle swarm optimization (PSO) Communication technologies Impedance estimation Low voltage distribution network Mathematical optimization problems meta heuristic algorithm Non differentiable optimization Power line communications (PLC) Real time measurements

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A Brief Review of the Intelligent algorithm for Traveling Salesman Problem in UAV Route Planning 9

A Brief Review of the Intelligent Algorithm for Traveling Sa...

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IEEE 9th International Conference on Electronics Information and Emergency Communication (ICEIEC)

作者： Xu, Yunpeng Che, Chang Beijing Univ Posts & Telecommun Coll Software Engn Beijing Peoples R China Univ Elect Sci & Technol China Coll Informat & Commun Engn Chengdu Sichuan Peoples R China

ISBN: (纸本)9781728111902

In the future, unmanned aerial vehicles(UAVs) will be used in a wide range of applications in areas such as wireless communications, logistics, inspections, search and rescue. Path planning in these areas often involves Traveling Salesman Problem (TSP). It is extremely difficult to select the optimal path from a large number of nodes, but it is easy to solve the complex problem after converting it to TSP. The optimization methods for solving TSP include meta heuristic algorithm and fuzzy neural network. With the rapid growth of demand, there is an urgent need for faster and more accurate methods to make UAVs more intelligent. This article reviews the application of UAV route planning in the field of wireless communications and express delivery, and selects a variety of classic and up-to-date algorithms for solving TSP and its variants to promote future researches.

关键词： unmanned aerial vehicles meta heuristic algorithm traveling salesman problem wireless communication neural network fuzzy neural network

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A Fuzzy Crow Search algorithm for Solving Data Clustering Problem 33rd

A Fuzzy Crow Search Algorithm for Solving Data Clustering Pr...

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33rd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems (IEA/AIE)

作者： Wu, Ze-Xue Huang, Ko-Wei Yang, Chu-Sing Natl Kaohsiung Univ Sci & Technol Dept Elect Engn Kaohsiung Taiwan Natl Cheng Kung Univ Inst Comp & Commun Engn Dept Elect Engn Tainan Taiwan

ISBN: (纸本)9783030557881;9783030557898

The crow is one of the most intelligent bird and infamous for observing other birds so that they can steal their food. The crow search algorithm (CSA), a nature-based optimizer, is inspired by the social behavior of crows. Scholars have applied the CSA to obtain efficient solutions to certain function and combinatorial optimization problems. Another popular and powerful method with several real-world applications (e.g., energy, finance, marketing, and medical imaging) is fuzzy clustering. The fuzzy c-means (FCM) algorithm is a critical fuzzy clustering approach given its efficiency and implementation easily. However, the FCM algorithm can be easily trapped in the local optima. To solve this data clustering problem, this study proposes a hybrid fuzzy clustering algorithm combines the CSA and a fireworks algorithm. The algorithm performance is evaluated using eight well-known UCI benchmarks. The experimental analysis concludes that adding the fireworks algorithm improved the CSA's performance and offered better solutions than those by other meta-heuristic algorithms.

关键词： Fuzzy c-means Data clustering Crow search algorithm Fireworks algorithm meta heuristic algorithm

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A Comparative Analysis of Differential Evolution and Genetic algorithm for Solving Optimal Power Flow

A Comparative Analysis of Differential Evolution and Genetic...

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IEEE 5th Power India Conference

作者： Ravi, C. N. Rajan, C. Christober Asir Sathyabama Univ Madras 600119 Tamil Nadu India Pondicherry Engn Coll Dept EEE Pondicherry India

ISBN: (纸本)9781467307666;9781467307635

The objective of the power systems firm is to achieve the maximum profit and customer goodwill by providing reliable and quality power supply. This operation and control problem would solved by Optimal Power Flow (OPF). OPF solutions provide optimal operating state and satisfy the objective of the firm. Modern trend used to solve this non-convex, complex OPF problem is meta heuristic algorithms. This paper presents and compares a two versatile meta heuristic algorithms: Differential Evolution (DE) and Genetic algorithm (GA) for solving OPF. Performance of these algorithms is tested on the standard IEEE 30 bus system.

关键词： differential evolutio genetic algorithm meta heuristic algorithm optimla power flow newton raphson power flow

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An improved Gravitation Search algorithm for Unconstrained Optimization

An improved Gravitation Search Algorithm for Unconstrained O...

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International Conference on Smart Materials and Intelligent Systems

作者： Wang JiaNan Li XiangTao NorthEast Normal Univ Network Informat Ctr Changchun 130024 Jilin Peoples R China Northeast Normal Univ Coll Comp Sci Changchun 130117 Peoples R China

Gravitation search algorithm (GSA) is a novel meta-heuristic algorithm introduced recently. In this paper, a study of boundary conditions is presented indicating the invisible wall technique outperforms absorbing and ... 详细信息

ISBN: (纸本)9780878492237

关键词： Gravitation search algorithm unconstraint optimization meta heuristic algorithm

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A Hybridized Model for Image Encryption through Genetic algorithm and DNA Sequence 6

A Hybridized Model for Image Encryption through Genetic Algo...

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6th International Conference on Smart Computing and Communications (ICSCC)

作者： Pujari, Saswat K. Bhattacharjee, Gargi Bhoi, Soumyakanta Tata Consultancy Serv Bombay Maharashtra India Veer Surendra Sai Univ Technol Dept Informat Technol Burla Odisha India Wipro Technol Hyderabad Andhra Pradesh India

With the advent of multimedia technology and rapid data transmission ability of networks, images have become a vital source of extracting information. So its security becomes a critical issue. Images are encrypted to another form in order to preserve their data. In this paper, we have put forward a hybridized model for encrypting images through a combination of Genetic algorithm and DNA Sequence. DNA sequence is basically chosen as it offers greater storage and higher computing capabilities. The encryption method consists of two phases-Transposition or Scrambling phase and Substitution phase. In the first phase, pixel locations are altered using GA to reduce the correlation among adjacent pixels. In substitution phase, the pixels are replaced by using XOR operation between the pixel values converted into binary strings and DNA substrings derived from a random DNA string. DNA substrings are used as keys for image encryption. The experimental outcome validates that the algorithm is simple, fast enough and feasible. Performance analysis asserts the robustness of the algorithm against all kinds of attacks and thereby maintaining higher security. (C) 2018 The Authors. Published by Elsevier B.V.

关键词： Image Encryption Genetic algorithm DNA Sequence meta heuristic algorithm Image Cryptosystem Image Security

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Power Control of DFIG based WECS using SOSMC and PSO algorithm 2

Power Control of DFIG based WECS using SOSMC and PSO algorit...

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2nd International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET)

作者： El Qouarti, Ouassima Essadki, Ahmed Laghridat, Hammadi Nasser, Tamou Mohammed V Univ Ecole Natl Super Arts & Metiers ENSAM Rabat Morocco Mohammed V Univ Ecole Natl Super Arts & Metiers Rabat Morocco Mohammed V Univ Ecole Natl Super Informat & Anal Syst Rabat Morocco

ISBN: (纸本)9781665422093

In recent years many researches went on to explore the deep and powerful impact of evolutionary algorithms on tuning the control parameters of the Doubly Fed Induction Generator (DFIG). The final purpose for that is to optimize controllers' gain and thus improve the systems' robustness and stability. In this article, we focus on Particle Swarm Optimization (PSO) algorithm in order to tune DFIG's parameters;Firstly, a state of art is addressed for articles that have dealt with both PSO and Super-Twisting (ST) algorithms applied on the DFIG based Wind Energy Conversion Systems (WECS). Secondly, a study is conducted on modeling, simulation and control of the DFIG based WECS using ST and PSO algorithms in order to control active and reactive powers. Finally, a comparative study is conducted for different control scenarios under normal and abnormal conditions in order to demonstrate and highlight the impact of each combination on the performances of a grid connected wind turbine generator.

关键词： WECS DFIG optimisation meta heuristic algorithm power control ST PSO

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A Hybrid Batch Job Scheduling algorithm For Grid Environment 4

A Hybrid Batch Job Scheduling Algorithm For Grid Environment

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4th International Conference on Computer and Knowledge Engineering (ICCKE)

作者： Zahedani, Shirin Dehghani Dastghaibyfard, GholamHossin Shiraz Univ Coll Elect & Comp Engn Dept Comp Sci & Engn Shiraz Iran

ISBN: (纸本)9781479954865

Grid computing is a collection of geographically heterogeneous distributed computational resources that enables users for sharing data and other computing resources. One of the major challenges in grid computing is how to schedule batch jobs across such an environment with minimum makespan (the finishing time of the last job) and flow time. in this study, a hybrid batch job scheduling method is proposed for grid environment that combines genetic and particle swarm optimization techniques to reduce makespan and Bow time. Experimental results show a reduction in makespan for 7 out of 12 instances of Braun workload comparing to minmin, maxmin, and discrete PSO algorithms.

关键词： grid computing particle swarm optimization makespan meta heuristic algorithm flowtime genetic algorithm

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