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Evaluation of contemporary evolutionary algorithms for optimization in reservoir operation and water supply

JOURNAL OF WATER SUPPLY RESEARCH AND TECHNOLOGY-AQUA 2018年第1期67卷 54-67页

作者： Ehteram, Mohammad Karami, Hojat Mousavi, Sayed-Farhad Farzin, Saeed Kisi, Ozgur Semnan Univ Fac Civil Engn Dept Water Engn & Hydraul Struct Semnan Iran Ilia State Univ Sch Nat Sci & Engn Tiblisi Georgia

This study evaluates three contemporary evolutionary algorithms, namely, shark, genetic, and particle swarm algorithms, for optimization in reservoir operation and water supply. The Klang gate dam in Malaysia is selected as the case study to optimize reservoir operation. The key objective of this study is the minimization of water deficits based on demands and released water. The global solution of the problem is computed based on software Lingo and the average solution of the shark algorithm is able to attain 99% of global solution. As well, the shark algorithm can furnish demand values at a faster convergence rate than both genetic and particle swarm algorithms. The reliability index and resiliency index, as useful indices in water resource management, are used and the values of these indices have the highest percent for the shark algorithm, indicating its superiority over other evolutionary algorithms.

关键词： genetic algorithm particle swarm algorithm shark algorithm water resource management

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Optimization design based on hierarchic genetic algorithm and particles swarm algorithm

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JOURNAL OF algorithmS & COMPUTATIONAL TECHNOLOGY 2018年第3期12卷 217-222页

作者： Lin DengWei Jiaozuo Univ Fac Informat Engn Jiaozuo 454003 Peoples R China

For a lot of data, it is time-consuming and unpractical to get the best combination by manual tests. The genetic algorithm can make up for this shortcoming through the optimization of parameters. In this paper, the advantages of traditional similarity algorithm is studied, the time model and the trust model for further filtering are introduced, and the parameters with the combination of hierarchical genetic algorithm and particle swarm algorithm are optimized. In the collaborative filtering algorithm, genetic algorithm is improved with hierarchical algorithm, and the user model and the algorithm process are optimized using the fitness function of selection, crossover, and variation, along with the optimization of recommendation result set. In the algorithm, the global optimal parameters can be calculated with the optimization of the obtained initial data, and the accuracy of the similarity calculation can also be improved. This study does the recommendation and comparison experiment in the MovieLens Dataset, and the results show that, on the basis of obtaining the nearest neighbor user group, the mixing use of the hierarchical genetic algorithm and the particle swarm algorithm can make more improvement in the recommendation quality than that of the traditional similarity algorithm.

关键词： Hierarchical genetic algorithm particle swarm algorithm similarity optimization algorithm resource recommendation

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Lithium-ion battery modeling and parameter identification based on fractional theory

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ENERGY 2018年第PartB期165卷 153-163页

作者： Hu, Minghui Li, Yunxiao Li, Shuxian Fu, Chunyun Qin, Datong Li, Zonghua Chongqing Univ Sch Automot Engn State Key Lab Mech Transmiss Chongqing 400044 Peoples R China Changan New Energy Automobile Res Inst Chongqing 401120 Peoples R China

To effectively use and manage lithium-ion batteries and accurately estimate battery states such as state of charge and state of health, battery models with good robustness, accuracy and low-complexity need to be established. So the models can be embedded in microprocessors and provide accurate results in real-time. Firstly, this paper analyzes the electrochemical impedance spectrogram of lithium-ion battery, and adopts impedance elements with fractional order characteristics such as constant phase element and Warburg element to improve the second-order RC integer equivalent circuit model based on the fractional calculus theory. Secondly, a fractional-order equivalent circuit model of lithium-ion battery is established, which can accurately describe the electrochemical processes such as charge transfer reaction, double-layer effect, mass transfer and diffusion of lithium-ion battery. Thirdly, based on the mixed swarm-based cooperative particle swarm optimization, parameter identification of the fractional-order equivalent circuit model is conducted using the federal city driving schedule experimental data in the time domain. The simulation results show that the model has higher accuracy and better robustness against different driving conditions, different SOC ranges and different temperatures than the second order RC equivalent circuit model. The SOC estimation accuracy based on the fractional-order equivalent circuit model of lithium-ion battery is validated. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Lithium-ion battery Fractional-order model Parameter identification particle swarm algorithm Electric vehicle

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A new maximum power point control algorithm of photovoltaic generation system

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SYSTEMS SCIENCE & CONTROL ENGINEERING 2018年第3期6卷 333-338页

作者： Bian, Xiaoxue Sun, Xiujuan Yang, Zhuo Shandong Univ Sci & Technol Coll Elect Engn & Automat Qingdao Peoples R China

Due to the severe environmental changes, especially in the context of the greatly change of the sunlight, the output power of the solar power system will be unstable. In the case of partial obstruction, the equivalent characteristic curves of the photovoltaic panel show a multi-peak state, traditional maximum power control technology is prone to misjudgment and cannot find the maximum power point (MPP). In order to solve this problem, a Newton method based on particle swarm optimization (PSO) is proposed to control the multi-peak MPP. The algorithm will firstly optimize the inertia weight of the particle swarm algorithm, then it will use the optimized particle swarm algorithm to search for the multi-peak MPP. The Newton method will rapidly solve the maximum value to achieve multi-peak MPP tracking when curves approaching to the maximum value. Construction of the boost chopper circuit, which was compared with the particle swarm algorithm and Newton method, was simulated under different light intensities by the simulation softwares such as Simulink and Matlab. The results show that Newton method based on the PSO can quickly find the maximum power with characteristics of good convergence speed and accuracy.

关键词： Solar power generation multi-peak particle swarm algorithm Newton method

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Location-Routing Problem With Demand Range

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IEEE ACCESS 2019年 7卷 149142-149155页

作者： Yu, Vincent F. Jodiawan, Panca Ho, Yi-Hsuan Lin, Shih-Wei Natl Taiwan Univ Sci & Technol Dept Ind Management Taipei 106 Taiwan Chang Gung Univ Dept Informat Management Taoyuan 333 Taiwan Linkou Chang Gung Mem Hosp Dept Neurol Taoyuan 333 Taiwan Ming Chi Univ Technol Dept Ind Engn & Management New Taipei 243 Taiwan

This research proposes a new variant of the location-routing problem (LRP) called LRP with Demand Range (LRPDR) by allowing flexibility in the delivery quantity. The goal of the LRPDR is to minimize the objective value calculated by the total cost minus the extra revenue. The total cost consists of the travelling cost of vehicles, the opening cost of the depots, and the activation cost of vehicles. This study proposes a new hybrid algorithm, SAPSO, that combines simulated annealing (SA) and particle swarm algorithm (PSO) for solving the LRPDR. Since this problem has not yet been studied in the literature, a mathematical model is proposed and solved by the Gurobi solver. The results obtained by Gurobi are then compared with those obtained by the proposed SAPSO algorithm. In addition, the performance of the proposed SAPSO algorithm is assessed by solving the LRP benchmark instances, and comparing the results with those of existing state-of-the-art algorithms for LRP. Based on the experimental results, the proposed SAPSO algorithm improves the performance of the basic SA algorithm and outperforms Gurobi. Moreover, the benefits of the LRPDR over LRP are presented in terms of total cost reduction.

关键词： Demand range hybrid algorithm location routing problem particle swarm algorithm simulated annealing

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Optimization of a Low-Voltage Load Switch for a Smart Meter Based on a Double Response Surface Model

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MAPAN-JOURNAL OF METROLOGY SOCIETY OF INDIA 2018年第3期33卷 261-270页

作者： Xiong, Dezhi Chen, Xiangqun Yang, Jie Wuhan Univ Technol Key Lab Fiber Opt Sensing Technol & Informat Proc Minist Educ Wuhan 430070 Hubei Peoples R China State Grid Hunan Elect Power Co Power Supply Serv Ctr Metrol Ctr Hunan Prov Key Lab Intelligent Elect Measurement Changsha 410007 Hunan Peoples R China

The existing electric energy meter load switches have issues such as a low electrical life, poor resistance to short-circuit currents, high contact point resistivity and severe heating. Research has thus been conducted on the optimization of the electric energy meter built-in load switch mechanism to establish a double response surface model for the electromagnetic attractive force and reed reaction force. The model introduces a niche fitness sorting strategy and a Gaussian mutation mechanism that are based on the standard particle swarm algorithm and form an improved multi-objective particle swarm optimization algorithm in which the static attractive force is regarded as the optimization objective. The multi-objective optimization of the structural parameters of the electromagnetic system and the reed system was carried out, and the optimization results were applied in the devices' design and implementation. The new structure improves the electromagnetic suction and reed force at the suction position. And the results from simulation show that the proposed method exceeds that of the back propagation neural network method in achieving the optimization goal and the new structure can effectively improve the performance of meter built-in load switch by avoiding the recoil phenomenon.

关键词： Electric energy meter Load switch Double response surface model particle swarm algorithm Optimization

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A Hybrid Immune algorithm for Solving Dynamic Optimization Problems 30

A Hybrid Immune Algorithm for Solving Dynamic Optimization P...

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30th Chinese Control and Decision Conference (CCDC)

作者： Yang Zhou Yuan Yi-chuan Luo Ting-xing Qin Jin Guizhou Univ Coll Comp Sci & Technol Guiyang 550025 Guizhou Peoples R China Guiyang Informat Ind Dev Ctr Guiyang 550081 Guizhou Peoples R China

ISBN: (纸本)9781538612446

Dynamic optimization problem (DOP) is a kind of optimization problem in the real world, and its optimization target or constraint may change over time. In order to solve the dynamic optimization problem effectively, a hybrid algorithm is proposed based on artificial immune network algorithm. On the one hand, update operator in particle swarm algorithm is introduced to realize a supplement to artificial immune network algorithm and improve its convergence speed. On the other hand, due to its local search capability, chaotic variables are used to avoid premature convergence in the late optimization of artificial immune network algorithm and improve search precision. At the same time, a Cauchy mutation operator is added to improve the global search capability. Simulation experiments are conducted on GDBG. The experimental results show that the algorithm can not only effectively improve the global search ability, convergence speed and search precision, but also keep the population diversity. Compared with similar optimization algorithms, the algorithm has obvious advantages in error and convergence indexes.

关键词： dynamic optimization artificial immune particle swarm algorithm chaotic variables Cauchy mutation

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A load management optimization strategy for smart distribution network considering Time-Of-Use electricity price and load control

A load management optimization strategy for smart distributi...

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Sustainable Power and Energy Conference (iSPEC)

作者： Weihan Xu Leijiao Ge Xingzhen Bai Shuai Zhang Yanhe Liang Xianjun Ge College of Electrical Engineering and Automation Shandong University of Science and Technology Qingdao China Key Laboratory of Ministry of Education of Smart Grid Tianjin University Tianjin China Electric Power Research Institute State Grid Heilongjiang Electric Power Company Harbin China Tsinghua University Beijing China

ISBN: (纸本)9781728149318

The Time-of-use electricity price can guide the users to use electricity orderly, and load control can quickly coordinate the peak-valley load difference of the smart distribution network. A load management optimization strategy for smart distribution network is proposed, which combines the time-of-use electricity price and the interruptible load control. Firstly, the characteristics of flexible load is analyzed. Secondly, the demand response model of smart distribution network is constructed, which considering time-of -use electricity price and direct load control, and the particle swarm algorithm is used to solve the model. Finally, a typical smart distribution network case verifies the superiority of the demand response strategy.

关键词： Demand response time-of-use electricity price direct load control particle swarm optimization load control Demand-Responsive Power grids particle swarm optimization particle swarm algorithm Electricity load management Prices

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Study on Optimal Capacity of Multi-type Energy Storage System for Optimized Operation of Virtual Power Plants

Study on Optimal Capacity of Multi-type Energy Storage Syste...

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China International Conference on Electricity Distribution (CICED)

作者： Sun, Jia Li, Xiangjun Ma, Huimeng Shenyang Univ Technol Shenyang Peoples R China China Elect Power Res Inst Beijing Peoples R China

ISBN: (纸本)9781538667750

The virtual power plant consisting of a large-scale energy storage system and a controllable energy source can reduce the potential safety hazards caused by the unstable output power of new energy when it is connected to the grid, thereby increasing the reliability of power supply. The energy storage system cooperates with the distributed photovoltaic and gas turbine to ensure that the virtual power plant participates in the system demand response, reduces the cost of power generation, and obtains more revenue. To this end, a virtual power plant operation strategy was designed. With the objective of maximizing the net profit of the virtual power plant in each period, a virtual power plant economic optimization configuration model was constructed, and a single-target particle swarm optimization algorithm was used to obtain the output of the energy storage system in the virtual power plant,and the signals are distributed to supercapacitors, lithium titanate batteries, and all-vanadium redox batteries through Fourier transform to realize the configuration of the energy storage system capacity and power.

关键词： Energy storage system virtual power plant particle swarm algorithm

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The Improvement of Indoor Localization Precision through Partial Least Square(PLS) and swarm(PSO) Methods

The Improvement of Indoor Localization Precision through Par...

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IEEE Sensors Applications Symposium (SAS)

作者： Shih, Chihhsiong Liang, Chaolong Tunghai Univ Dept Comp Sci Taichung Taiwan

ISBN: (纸本)9781538620922

Indoor positioning accuracy affects the usability of many IOT applications such as personnel and merchandise tracking. Among the many indoor positioning methods, the signal pattern matching method is also known as the feature fingerprint method. The deployment is simple and low cost. Its working principle is through the collection of wireless signal samples to establish a fingerprint database. When receiving the localization request, the input data is put into the comparison with fingerprint database to determine the exact location of the target emitting the same wireless signal patterns. Many studies are based on this principle to develop different algorithms to improve the positioning accuracy. The shortcomings of this approach is that the positioning precision is easily perturbed by the noisy environment conditions. These noisy signal samples recorded in the fingerprint database will lose the reference value, when low efficiency indexing algorithm or wrong models are used. This paper proposes several novel machine learning based algorithms and indexing methods for indoor positioning accuracy enhancements. They are a modified swarm algorithm, partial least square(PLS) algorithm and genetic algorithms. Different from regular fingerprint localization methods which make use of statistics based models to characterize the signal patterns, our methods make use of several A. I. based algorithms to classify the signal patterns for localization. Compared to other statistics based indoor fingerprint localization methods, our method can reach a precision of 95 % with low development cost and a resolution of 16 cm in a complex computer laboratory environment.

关键词： IOT localization particle swarm algorithm partial least square algorithm machine learning intelligent localization

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