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An Automatic Functional Coverage for Digital Systems Through a Binary particle swarm optimization algorithm with a Reinitialization Mechanism

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JOURNAL OF ELECTRONIC TESTING-THEORY AND APPLICATIONS 2017年第4期33卷 431-447页

作者： Martinez-Cruz, Alfonso Barron-Fernandez, Ricardo Molina-Lozano, Heron Ramirez-Salinas, Marco-Antonio Villa-Vargas, Luis-Alfonso Cortes-Antonio, Prometeo Cheng, Kwang-Ting (Tim) Ctr Res Comp CIC IPN Mexico City 07738 DF Mexico Univ Calif Santa Barbara Elect & Comp Engn Dept Santa Barbara CA 93106 USA

At present, functional verification represents the most expensive part of the digital systems design. Moreover, different problems such as: clock synchronization, code compatibility, simulation automation, new design methodologies, proper use of coverage metrics, among others represent challenges in this area. The automated test vector generation is involved in these problems. In this work, an automated functional test sequences generation for digital systems based on the use of coverage models and a binary particle swarm optimization algorithm with a reinitialization mechanism (BPSOr) is described. Also, a comparison with other meta-heuristic algorithms such as: Genetic algorithms (GA) and pseudo-random generation is presented using different fitness functions, coverage models and devices under verification. The main strategy is based on the combination of the simulation and meta-heuristic algorithms to test the device behavior through the generation of test vector sequences. According to the results, the proposed test generation method represents a good alternative to increase the functional coverage during the automated functional verification of block-level digital systems verification.

关键词： Functional verification particle swarm optimization algorithm Coverage points Genetic algorithm Coverage models Digital systems Meta-heuristic algorithms

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Effects of Random Values for particle swarm optimization algorithm

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algorithmS 2018年第2期11卷 23-23页

作者： Dai, Hou-Ping Chen, Dong-Dong Zheng, Zhou-Shun Cent South Univ Sch Math & Stat Changsha 410083 Hunan Peoples R China Jishou Univ Sch Math & Stat Jishou 416000 Peoples R China State Key Lab High Performance Complex Mfg Changsha 410083 Hunan Peoples R China

particle swarm optimization (PSO) algorithm is generally improved by adaptively adjusting the inertia weight or combining with other evolution algorithms. However, in most modified PSO algorithms, the random values are always generated by uniform distribution in the range of [0, 1]. In this study, the random values, which are generated by uniform distribution in the ranges of [0, 1] and [1, 1], and Gauss distribution with mean 0 and variance 1 (U [0, 1], U [1, 1] and G (0, 1)), are respectively used in the standard PSO and linear decreasing inertia weight (LDIW) PSO algorithms. For comparison, the deterministic PSO algorithm, in which the random values are set as 0.5, is also investigated in this study. Some benchmark functions and the pressure vessel design problem are selected to test these algorithms with different types of random values in three space dimensions (10, 30, and 100). The experimental results show that the standard PSO and LDIW-PSO algorithms with random values generated by U [1, 1] or G (0, 1) are more likely to avoid falling into local optima and quickly obtain the global optima. This is because the large-scale random values can expand the range of particle velocity to make the particle more likely to escape from local optima and obtain the global optima. Although the random values generated by U [1, 1] or G (0, 1) are beneficial to improve the global searching ability, the local searching ability for a low dimensional practical optimization problem may be decreased due to the finite particles.

关键词： particle swarm optimization algorithm random values uniform distribution gauss distribution

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Seepage safety monitoring model for an earth rock dam under influence of high-impact typhoons based on particle swarm optimization algorithm

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Water Science and Engineering 2017年第1期10卷 70-77页

作者： Yan Xiang Shu-yan Fu Kai Zhu Hui Yuan Zhi-yuan Fang Dam Safety Management Center of the Ministry of Water Resources Nanjing Hydraulic Research InstituteNanjing 210029China State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering Nanjing Hydraulic Research InstituteNanjing 210029China College of Mechanics and Materials Hohai UniversityNanjing 210098China School of Water Resources and Hydraulic Engineering Yunnan Agricultural UniversityKunming 650000China

Extreme hydrological events induced by typhoons in reservoir areas have presented severe challenges to the safe operation of hydraulic structures. Based on analysis of the seepage characteristics of an earth rock dam, a novel seepage safety monitoring model was constructed in this study. The nonlinear influence processes of the antecedent reservoir water level and rainfall were assumed to follow normal distributions. The particle swarm optimization （PSO） algorithm was used to optimize the model parameters so as to raise the fitting accuracy. In addition, a mutation factor was introduced to simulate the sudden increase in the piezometric level induced by short-duration heavy rainfall and the possible historical extreme reservoir water level during a typhoon. In order to verify the efficacy of this model, the earth rock dam of the Siminghu Reservoir was used as an example. The piezometric level at the SW1-2 measuring point during Typhoon Fitow in 2013 was fitted with the present model, and a corresponding theoretical expression was established. Comparison of fitting results of the piezometric level obtained from the present statistical model and traditional statistical model with monitored values during the typhoon shows that the present model has a higher fitting accuracy and can simulate the uprush feature of the seepage pressure during the typhoon perfectly.

关键词： Monitoring model particle swarm optimization algorithm Earth rock dam Lagging effect Typhoon Seepage pressure Mutation factor Piezometric level

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A Novel Power Losses Reduction Method Based on a particle swarm optimization algorithm Using STATCOM

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ENERGIES 2018年第10期11卷 2851-2851页

作者： Tuzikova, Valeriya Tlusty, Josef Muller, Zdenek Czech Tech Univ Dept Elect Power Engn Prague 16627 Czech Republic Czech Tech Univ Fac Biomed Engn Kladno 27201 Czech Republic

In the modern electric power industry, Flexible AC Transmission Systems (FACTS) have a special place. In connection with the increased interest in the development of smart energy, the use of such devices is becoming especially urgent. Their main function is the ability to manage modes in real time: maintain the necessary level of voltage in the grids, control the power flow, increase the capacity of power lines and increase the static and dynamic stability of the power grid. The problem of system reliability and stability is related to the task of definitions and optimizations and planning indicators, design and exploitation. The main aim of this article is the definition of the best placement of the STATCOM compensator in case to provide stability and reliability of the grid with the minimization of the power losses, using particle swarm optimization algorithms. All calculations were performed in MATLAB.

关键词： STATCOM FACTS power compensation particle swarm optimization algorithm power losses stability reliability optimal power flow

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A Hybrid algorithm Based on Gravitational Search and particle swarm optimization algorithm to Solve Function optimization Problems

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ENGINEERING LETTERS 2017年第1期25卷 22-29页

作者： Wang, Jie-Sheng Song, Jiang-Di Univ Sci & Technol Liaoning Sch Elect & Informat Engn Anshan 114051 Peoples R China Univ Sci & Technol Liaoning Natl Financial Secur & Syst Equipment Engn Res Ct Anshan Peoples R China

Gravitational search algorithm (GSA) is a swarm intelligence heuristic optimization algorithm based on the law of gravitation. Aiming at the disadvantage of poor local search ability and slow convergence speed in standard GSA, four improved GSA-PSO hybrid algorithm are proposed by introducing a small constant updating strategy in order to enhance the update ability of velocity, acceleration factor and the optimal individual location, where PSO strategy was used to optimize the position and velocity of the GSA. Through simulation experiments on typical test functions to verify its performance, the simulation results show that the optimal setup of GSA parameters can improve the convergence rate of the algorithm and improve the accuracy of the solution.

关键词： gravitational search algorithm particle swarm optimization algorithm function optimization

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A combined model based on seasonal autoregressive integrated moving average and modified particle swarm optimization algorithm for electrical load forecasting

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2017年第5期32卷 3447-3459页

作者： Ma, Tao Wang, Fen Wang, Jianzhou Yao, Yukai Chen, Xiaoyun Lanzhou Univ Sch Informat Sci & Engn 222 Tianshui South Rd Lanzhou 730000 Gansu Peoples R China Dongbei Univ Finance & Econ Sch Stat Dalian Peoples R China Ningxia Normal Univ Sch Math & Comp Sci Ningxia Peoples R China Lanzhou Univ Technol Coll Comp & Commun Lanzhou Peoples R China

Electric load prediction is an important decision tool in area of electricity economy. Recently researchers have presented innovative models to improve the forecasting accuracy of short-term electricity series, which is valuable in allowing both consumers and electric power sector to make effective planning. This study proposed novel combing optimization model to improve the precision of electric load forecasting and called SSPM. First, taken the advantage of linear prediction for the seasonal autoregressive integrated moving average (SARIMA) model and non-linear prediction for the support vector machines (SVM) model to combine a new model. Next, the produce results by SARIMA model is regarded as linear component and used SVM model for correcting the residual from SARIMA as non-linear component of forecasting results. Third, in order to show the dynamic relationship of linear and non-linear components, the weight variable of alpha(1) and alpha(2) are proposed that optimized by particle swarm optimization (PSO) algorithm with lower error of fitness function, the combining model is applied in the daily electric load data at New South Wales (NSW) in Australia. The experimental results indicate that the proposed optimization model obtains better performance of precise and stability than models of SARIMA and SVM respectively, outperform than conventional artificial neural network (ANN). Although the novel model is applied to electric load forecasting in this paper, it has more scopes for application in a number of areas to gain improvement of forecast accuracy in complex time series.

关键词： Support vector machines seasonal autoregressive integrated moving average combining optimization model electric load forecasting particle swarm optimization algorithm

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Using particle swarm optimization algorithm to search for a power ascension path of boiling water reactors

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ANNALS OF NUCLEAR ENERGY 2017年第Apr.期102卷 37-46页

作者： Lin, Tzung-Yi Yeh, Jau-Tyne Kuo, Weng-Sheng Inst Nucl Energy Res Nucl Engn Div 1000 Wenhua Rd Longtan Township 32546 Taoyuan County Taiwan

During the power ascension, the operating point is based on core power, core flow, control rod pattern, and concentrations of fission product, such as xenon. The thermal limits and fuel conditioning at the operating point should meet the constraints. ASCENTB is an automatic power ascension path searching program for boiling water reactors. The control rod movement is searched for by the particle swarm optimization (PSO) algorithm. The operating points of one control rod withdrawal sequence are based on the PSO1 or PSO2 strategy. The results of ASCENTS for two selected cycles are comparable with the power plant records. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： particle swarm optimization algorithm Power ascension of boiling water reactors Operating point searching strategy ASCENTB

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The optimization of Marine Diesel Engine Rotational Speed Control Process by Fuzzy Logic Control Based on particle swarm optimization algorithm

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FUTURE INTERNET 2018年第10期10卷 99-99页

作者： Tien Anh Tran Vietnam Maritime Univ Fac Marine Engn Haiphong 180000 Vietnam

The marine main diesel engine rotational speed automatic control plays a significant role in determining the optimal main diesel engine speed under impacting on navigation environment conditions. In this article, the application of fuzzy logic control theory for main diesel engine speed control has been associated with particle swarm optimization (PSO). Firstly, the controller is designed according to fuzzy logic control theory. Secondly, the fuzzy logic controller will be optimized by particle swarm optimization (PSO) in order to obtain the optimal adjustment of the membership functions only. Finally, the fuzzy logic controller has been completely innovated by particle swarm optimization algorithm. The study results will be represented under digital simulation form, as well as comparison between traditional fuzzy logic controller with fuzzy logic control-particle swarm optimization speed controller being obtained.

关键词： main diesel engine fuzzy logic control theory particle swarm optimization algorithm optimal engine speed navigation environment

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Using the particle swarm optimization algorithm to calibrate the parameters relating to the turbulent flux in the surface layer in the source region of the Yellow River

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AGRICULTURAL AND FOREST METEOROLOGY 2017年 232卷 606-622页

作者： Yang, Qidong Wu, Jian Li, Yueqing Li, Weidong Wang, Lijuan Yang, Yang Yunnan Univ Dept Atmospher Sci Kunming 650091 Yunnan Province Peoples R China China Meteorol Adm Inst Plateau Meteorol Chengdu 610000 Sichuan Provinc Peoples R China China Meteorol Adm Inst Arid Meteorol Key Open Lab Arid Climat Change & Disaster Reduct Gansu Key Lab Arid Climat Change & Reducing Disas Wuhan 730020 Gansu Peoples R China

Accurately determining the fluxes of mass and energy between land and the atmosphere is important for understanding regional climates and hydrological cycles. In numerical modeling, the parameterization of a turbulent flux is usually based on Monin-Obukhov similarity theory (MOST). According to this theory, it is necessary to simultaneously calculate the empirical similarity parameters beta(m), beta(h), gamma(m), and gamma(h), the aerodynamic roughness (z(om)) and the thermal roughness (z(T)). However, it is difficult to solve a simultaneous set of nonlinear equations for these six parameters. In this study, a new method was introduced to solving this problem. Using measurements from Maqu Station in the source region of the Yellow River, this study employed the artificial intelligence particle swarm optimization (PSO) algorithm to calibrate the parameters relating to the turbulent flux in the surface layer. We concluded that the differences in the sensible heat and momentum fluxes between the calculations that used the calibrated parameters and the measurements were rather small and that their correlation coefficients were relatively high. The results suggested that PSO algorithm is a feasible approach which can be applied in MOST parameter estimation. (C) 2016 The Author(s). Published by Elsevier B.V.

关键词： Maqu station Monin-Obukhov similarity theory particle swarm optimization algorithm Source region of the Yellow River

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A particle swarm optimization algorithm with Time Varying Parameters

A Particle Swarm Optimization Algorithm with Time Varying Pa...

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第30届中国控制与决策会议

作者： Zhen Hu Dexuan Zou Zhi Kong Xin Shen School of Electrical Engineering and Automation Jiangsu Normal University School of Electrical Engineering and Automation Northeastern University at Qinhuangdao

A particle swarm optimization(TVPSO) algorithm with time varying parameters is proposed to improve the performance of particle swarm optimization(PSO) algorithm by two improvements. Aiming at the fact general PSO algorithms have the disadvantages of falling into local optima caused by linearly decreased inertia weight. TVPSO uses the related properties of the trigonometric function to improve the dynamic changes of inertia weight along With Time. The inertia weight maintains a large value in the initial stage, and decreases gradually and reaches a small value at the end. Thus, the global search capability and convergence performance were improved;In order to cope with changes in inertia weight, learning factors also change with time. TVPSO and the other latest particle swarm optimization algorithms are tested on 10 functions at the same time. Experimental results show that TPSO has faster search speed and stronger global search capabilities.

关键词： Time varying parameters particle swarm optimization algorithm Trigonometric function

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