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Heuristic particle swarm optimization approach for test point selection with imperfect test

JOURNAL OF INTELLIGENT MANUFACTURING 2017年第1期28卷 37-50页

作者： Deng, Sen Jing, Bo Zhou, Hongliang Air Force Engn Univ Aeronaut & Astronaut Engn Coll Xian 710038 Peoples R China

The problem of near-optimal test point set selection with imperfect test is solved by using the heuristic particle swarm optimization (HPSO) algorithm. First, to describe the uncertainty of each test, the testability analysis model and such indexes as fault detection rate, fault isolation rate, and false alarm rate are redefined. A heuristic function is then established to evaluate the detection isolation capability and uncertainty of the test point, which can provide heuristic information to improve the searching efficiency of particle swarm optimization (PSO). The heuristic function and least test cost principle are used as bases to design a fitness function of PSO algorithm for test point selection. Finally, the HPSO algorithm is proposed to select the optimal test point set for two practical systems. Simulation and experiment results show that the method can determine the global optimal test point accurately and effectively while meeting the requirements of testability indexes with least cost.

关键词： Imperfect test Test point selection particle swarm optimization algorithm Heuristic function

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optimization of Reservoir Operation using New Hybrid algorithm

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KSCE JOURNAL OF CIVIL ENGINEERING 2018年第11期22卷 4668-4680页

作者： Yaseen, Zaher Mundher Karami, Hojat Ehteram, Mohammad Mohd, Nuruol Syuhadaa Mousavi, Sayed Farhad Hin, Lai Sai Kisi, Ozgur Farzin, Saeed Kim, Sungwon El-Shafie, Ahmed Ton Duc Thang Univ Fac Civil Engn Ho Chi Minh City Vietnam Semnan Univ Dept Water Engn & Hydraul Struct Fac Civil Engn Semnan Iran Univ Malaya Civil Engn Dept Fac Engn Kuala Lumpur 50603 Malaysia Ilia State Univ Sch Nat Sci & Engn Tbilisi Georgia Dongyang Univ Dept Railrd Construct & Safety Engn Yeongju 36040 South Korea

Due to the scarcity of fresh water resources, exploiting dams' reservoirs, based on their optimal operation, obviates construction of extra dams and high costs and satisfies downstream consumers' water needs with high reliability. In this research, a new hybrid approach of Artificial Fish swarm algorithm (AFSA) and particle swarm optimization algorithm (PSOA) is used to optimize Karun-4 reservoir, increase energy production and minimize downstream water shortages. This Hybrid algorithm (HA) brings about diversity of responses in PSOA, prevents entrapment of AFSA in local optimum traps and increases convergence speed and balances between the abilities to scan and make profit in the AFSA. This method was assessed based on reliability, vulnerability and resilience indices. In addition, based on a multi-criteria decision-making model, it was evaluated by comparing it with other evolutionary algorithms. To verify the HA, it was tested on few mathematical functions. Results indicated that the HA features performed higher reliability, lower vulnerability and resiliency, as compared with AFSA and PSOA. In addition, HA is ranked first according to the multi criteria decision making model. Further, among all the tested evolutionary methods, this new algorithm yielded the best answer for dam power plant's objective function.

关键词： optimization of reservoir operation artificial intelligence artificial fish algorithm particle swarm optimization algorithm

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Inverse Analysis of Various Surface Emissivity Distributions with particle swarm optimization Method

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TRANSACTIONS OF THE KOREAN SOCIETY OF MECHANICAL ENGINEERS B 2018年第9期42卷 573-582页

作者： Lee, Kyun Ho Sejong Univ Dept Aerosp Engn Sch Mech & Aerosp Engn Seoul South Korea

Generally, thermal radiation heat transfer can be classified into two forms, surface and gas radiation, depending on the participation of the medium in the heat transfer process. The objective of the present study is to estimate the unknown surface emissivities of various profiles by the inverse analysis of the pure surface radiation problem. To do this, an axisymmetric cylindrical enclosure is considered and the particle swarm optimization (PSO) method is employed as an inverse solver to find the exact solutions efficiently. By comparing the convergence rates and accuracies of the obtained results, the present study intends to discuss the feasibility of the PSO method as the inverse solver of the surface radiation problem involving multi-parameter unknowns.

关键词： Inverse Analysis Surface Radiation Surface Emissivity particle swarm optimization algorithm

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The State of Charge Estimation of Power Lithium Battery Based on RBF Neural Network Optimized by particle swarm optimization

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JOURNAL OF APPLIED SCIENCE AND ENGINEERING 2017年第4期20卷 483-490页

作者： Hou, Zhixiang Xie, Ping Hou, Jiqiang Changsha Univ Sci & Technol Coll Automobile & Mech Engn Changsha 410004 Hunan Peoples R China

In order to ensure the safe and stable operation of electric vehicles (EV), it is necessary to accurately estimate the state of charge (SOC) of power lithium battery for electric vehicle. Because of the nonlinear relationship between SOC and its influencing factors, RBF neural network has obvious advantages in solving nonlinear problems, so in this paper, an SOC estimation method of power battery based on RBF neural network is proposed. In order to improve the accuracy of SOC estimation, we use particle swarm optimization (PSO) to optimize the RBF neural network model and identify the value of RBF network center vector and the weights through global optimal searching ability of PSO algorithm. The results simulation show that the SOC model based on PSO-RBF neural network has good estimation accuracy.

关键词： Electric Vehicle State-of-charge Estimation Radial Basis Function Neural Network particle swarm optimization algorithm

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Enhanced Bat algorithm for Analyzing the Reliability and Contingency Issue in Radial Distribution Power System

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ELECTRIC POWER COMPONENTS AND SYSTEMS 2018年第19-20期46卷 2071-2083页

作者： Kumar, V. Swapnil, Shubham Ranjan, R. Singh, V. R. Maharshi Dayanand Univ Dept Elect Engn Rohtak Haryana India Univ Florida Dept Comp & Informat Sci & Engn Gainesville FL 32611 USA PDM Coll Engn Bhadurgarh India MDU Dept Elect Engn PDMCE Bahadurgarh Haryana India

The paper proposes the hybrid technique to observe the contingency and improve the reliability of radial distribution system (RDS). The hybrid algorithm used is interface of Bat algorithm and particle swarm optimization that is used to examine the contingency and reliability indices in the RDS. The feeder power loss, system node voltage deviation, system average interruption frequency index, system average interruption unavailability index and the energy not supplied at every load position in a sharing scheme are used to resolve the contingencies to minimize the power quality and reliability indices. The proposed techniques are implemented on MATLAB/Simulink platform and experienced on the IEEE 33 bus RDS. To establish the effectiveness of the proposed algorithm, results are compared with the reported Bat and Firefly algorithm.

关键词： IEEE 33 bus radial distribution system reliability contingency system node voltage deviation system average interruption frequency index system average interruption unavailability index enhanced bat algorithm particle swarm optimization algorithm power loss

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SSI effects on seismic behavior of smart base-isolated structures

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GEOMECHANICS AND ENGINEERING 2018年第2期14卷 161-174页

作者： Shourestani, Saeed Soltani, Fazlollah Ghasemi, Mojtaba Etedali, Sadegh Grad Univ Adv Technol Dept Civil & Surveying Engn Kerman Iran Birjand Univ Technol Dept Civil Engn Birjand Iran

The present study investigates the soil-structure interaction (SST) effects on the seismic performance of smart base-isolated structures. The adopted control algorithm for tuning the control force plays a key role in successful implementation of such structures;however, in most studied carried out in the literature, these algorithms are designed without considering the SSI effect. Considering the SSI effects, a linear quadratic regulator (LQR) controller is employed to seismic control of a smart base-isolated structure. A particle swarm optimization (PSO) algorithm is used to tune the gain matrix of the controller in both cases without and with SSI effects. In order to conduct a parametric study, three types of soil, three well-known earthquakes and a vast range of period of the superstructure are considered for assessment the SSI effects on seismic control process of the smart-base isolated structure. The adopted controller is able to make a significant reduction in base displacement. However, any attempt to decrease the maximum base displacement results in slight increasing in superstructure accelerations. The maximum and RMS base displacements of the smart base-isolated structures in the case of considering SSI effects are more than the corresponding responses in the case of ignoring SSI effects. Overall, it is also observed that the maximum and RMS base displacements of the structure are increased by increasing the natural period of the superstructure. Furthermore, it can be concluded that the maximum and RMS superstructure accelerations are significant influenced by the frequency content of earthquake excitations and the natural frequency of the superstructure. The results show that the design of the controller is very influenced by the SSI effects. In addition, the simulation results demonstrate that the ignoring the SSI effect provides an unfavorable control system, which may lead to decline in the seismic performance of the smart-base isolated structure incl

关键词： base isolation soil-structure interaction smart base-isolated structures LQR controller particle swarm optimization algorithm

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Novel optical efficiency formulas for parabolic trough solar collectors: Computing method and applications

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APPLIED ENERGY 2018年 224卷 682-697页

作者： Cheng, Ze-Dong Zhao, Xue-Ru He, Ya-Ling Xi An Jiao Tong Univ Sch Energy & Power Engn Key Lab Thermofluid Sci & Engn Minist Educ Xian 710049 Shaanxi Peoples R China

This paper presents a novel computing method of optical efficiency fitting formulas and its applications for parabolic trough solar collectors, by combining the Monte Carlo ray-tracing methodology with the population based particle swarm optimization algorithm. The expressions of the optical efficiency fitting formulas are deduced by means of analyzing the Monte Carlo ray-tracing data samples with a variable reduction technique, while the fitting formula coefficients are obtained using the particle swarm optimization algorithm. The computing time of the annual optical efficiency reduces to less than 10(-7) s, from about a day as previously calculated by a once powerful Monte Carlo ray-tracing model. Thus, it makes the once time-consuming long-time average optical efficiency calculations or corresponding population-based optimizations much more feasible and efficient. A dynamic particle swarm size factor is also proposed to improve the particle swarm optimization performance, which reduces nearly 90% of the particle swarm optimization computing time. Numerical results calculated by the Monte Carlo ray-tracing model and the obtained optical efficiency fitting formulas were compared with the corresponding reference data. Good agreements were obtained, proving that the computing method and model are reliable. The obtained optical efficiency fitting formula of parabolic trough solar collectors is then applied successfully to efficiently study the characteristics of long-time average optical efficiencies under different geometrical configurations, geographical locations, tracking models and corresponding optical or geometrical optimizations. This proposed novel method offers a useful option of high potential for further applications on efficient optical efficiency related analyses and corresponding population-based optimizations for parabolic trough solar collectors.

关键词： Parabolic trough solar collector Optical efficiency Fitting formula Monte Carlo ray-tracing particle swarm optimization algorithm

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Application of an improved minimum entropy deconvolution method for railway rolling element bearing fault diagnosis

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JOURNAL OF SOUND AND VIBRATION 2018年 425卷 53-69页

作者： Cheng, Yao Zhou, Ning Zhang, Weihua Wang, Zhiwei Southwest Jiaotong Univ State Key Lab Tract Power Chengdu 610031 Sichuan Peoples R China

Minimum entropy deconvolution is a widely-used tool in machinery fault diagnosis, because it enhances the impulse component of the signal. The filter coefficients that greatly influence the performance of the minimum entropy deconvolution are calculated by an iterative procedure. This paper proposes an improved deconvolutionmethod for the fault detection of rolling element bearings. The proposed method solves the filter coefficients by the standard particle swarm optimization algorithm, assisted by a generalized spherical coordinate transformation. When optimizing the filters performance for enhancing the impulses in fault diagnosis (namely, faulty rolling element bearings), the proposed method outperformed the classical minimum entropy deconvolution method. The proposed method was validated in simulation and experimental signals from railway bearings. In both simulation and experimental studies, the proposed method delivered better deconvolution performance than the classical minimum entropy deconvolutionmethod, especially in the case of low signal-to-noise ratio. (c) 2018 Published by Elsevier Ltd.

关键词： Minimum entropy deconvolution particle swarm optimization algorithm Fault diagnosis Railway Rolling element bearing

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Multi-Objective Optimal Sensor Placement for Low-Pressure Gas Distribution Networks

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IEEE SENSORS JOURNAL 2018年第16期18卷 6660-6668页

作者： Zan, Thaw Tar Thein Gupta, Payal Wang, Mengmeng Dauwels, Justin Ukil, Abhisek Nanyang Technol Univ ERIAN Energy Res Inst Singapore 639798 Singapore Digital Money Myanmar Yangon Myanmar Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore Univ Auckland Dept Elect & Comp Engn Auckland 1010 New Zealand

Natural gas distribution systems are inherently vulnerable to accidental or intentional intrusion. Such events lead to financial losses and endanger the environmental and public safety. Therefore, it is crucial to adequately monitor the gas distribution systems. An important step toward this goal is to optimize the placement of sensors in the network. In this paper, we propose three design objectives including time-to-detection (TTD), sensitivity, and impact propagation (IP) and implement five multi-objective optimization algorithms (greedy, greedy randomized adaptive search procedure, non-dominated sorting genetic algorithm II, FrameSense, and particle swarm optimization (PSO)) to strategically place the sensors. From the results on an artificial network with 37 nodes and 50 branches and a real network in Singapore with 148 nodes and 150 branches, we find that Greedy and PSO algorithms are almost 10 times faster than the other algorithms in computational time. We also investigate the tradeoff between the design objectives and the number of sensors. Since TTD, sensitivity, and IP have different measurement units, we normalize their values within 0 to 1 (0%-100%) and consider the average of those three normalized values as the design cost. For 10% design cost, the number of required sensors is 5 and 8 for the artificial network and the real network, respectively. The results indicate that PSO yields the sensor configuration with the lowest design cost and the computational time.

关键词： Sensor placement multi-objective optimization FrameSense genetic algorithm GRASP greedy algorithm particle swarm optimization algorithm

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A machine learning technique toward generating minimum energy structures of small boron clusters

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INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY 2018年第17期118卷 e25672-e25672页

作者： Mitikiri, Praveen Jana, Gourhari Sural, Shamik Chattaraj, Pratim K. Indian Inst Technol Kharagpur Dept Comp Sci & Engn Kharagpur 721302 W Bengal India Indian Inst Technol Kharagpur Dept Chem Kharagpur 721302 W Bengal India Indian Inst Technol Kharagpur Ctr Theoret Studies Kharagpur 721302 W Bengal India

The search for a global minimum related to molecular electronic structure and chemical bonding has received wide attention based on some theoretical calculations at various levels of theory. particle swarm optimization (PSO) algorithm and modified PSO have been used to predict the energetically stable/metastable states associated with a given chemical composition. Out of a variety of techniques such as genetic algorithm, basin hopping, simulated annealing. PSO, and so on. PSO is considered to be one of the most suitable methods due to its various advantages over others. We use a swarm-intelligence based parallel code to improve a PSO algorithm in a multidimensional search space augmented by quantum chemical calculations on gas phase structures at 0 K without any symmetry constraint to obtain an optimal solution. Our currently employed code is interfaced with Gaussian software for single point energy calculations. The code developed here is shown to be efficient. Small population size (small cluster) in the multidimensional space is actually good enough to get better results with low computational cost than the typical larger population. But for larger systems also the analysis is possible. One can try with a large number of particles as well. We have also analyzed how arbitrary and random structures and the local minimum energy structures gravitate toward the target global minimum structure. At the same time, we compare our results with that obtained from other evolutionary techniques.

关键词： B-5 and B-6 clusters global optimal structure multi-threaded code optimization particle swarm optimization algorithm

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