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A particle swarm optimization algorithm for optimization of thermal performance of a smooth flat plate solar air heater

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ENERGY 2012年第1期38卷 406-413页

作者： Siddhartha Sharma, Naveen Varun Natl Inst Technol Dept Mech Engn Hamirpur 177005 India

This work is undertaken with an objective to develop and implement a trained particle swarm optimization (PSO) algorithm for prediction of an optimized set of design and operating parameters for a smooth flat plate solar air heater (SFPSAH). The simulation is carried out based on the basis of the algorithm developed for three different cases using the climatic condition data of the city Hamirpur, India situated between (latitude) 31 degrees 25'-31 degrees 52'N and (longitude) 76 degrees 18' to 76 degrees 44' E. The final results obtained from this algorithm are compared with experimental results and found to be satisfactory as far as flexibility, speed and global convergence are concerned. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： Thermal performance Solar air heater particle swarm optimization algorithm

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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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Optical watermark scheme based on singular value decomposition ghost imaging and particle swarm optimization algorithm

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JOURNAL OF MODERN OPTICS 2020年第12期67卷 1059-1071页

作者： Wu, Jun-Yun Huang, Wei-Liang Zuo, Mei-Juan Gong, Li-Hua Nanchang Univ Dept Comp Sci & Technol Nanchang Jiangxi Peoples R China Nanchang Univ Dept Elect Informat Engn Nanchang 330031 Jiangxi Peoples R China

An optical image watermarking algorithm, based on singular value decomposition (SVD) ghost imaging and multiple transforms, is designed. The watermark image is first encrypted by applying an SVD ghost imaging system, then the encrypted watermark is embedded into the cover image with the help of multiple transforms, including lifting wavelet transform (LWT), discrete cosine transform (DCT), discrete fractional angular transform (DFAT) and SVD. Four sub-band images are produced from the host image by LWT and DCT. The improved DFAT, whose scaling factors and parameter are optimized by particle swarm optimization algorithm, is operated in the new matrix. Afterwards, SVD is executed in the two-part image and the encrypted watermark is embedded in the host image by mutual operation of different matrices. Simulation results validate that the proposed watermark scheme is superior in the aspects of security, robustness and imperceptibility.

关键词： Optical watermarking SVD ghost imaging lifting wavelet transform discrete fractional angular transform particle swarm optimization algorithm

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Acoustic radiation optimization using the particle swarm optimization algorithm

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JSME INTERNATIONAL JOURNAL SERIES C-MECHANICAL SYSTEMS MACHINE ELEMENTS AND MANUFACTURING 2004年第2期47卷 560-567页

作者： Jeon, JY Okuma, M Tokyo Inst Technol Dept Mech & Aerosp Engn Meguro Ku Tokyo 1528552 Japan

The present paper describes a fundamental study on structural bending design to reduce noise using a new evolutionary population-based heuristic algorithm called the particle swarm optimization algorithm (PSOA). The particle swarm optimization algorithm is a parallel evolutionary computation technique proposed by Kennedy and Eberhart in 1995. This algorithm is based on the social behavior models for bird flocking, fish schooling and other models investigated by zoologists. Optimal structural design problems to reduce noise are highly nonlinear, so that most conventional methods are difficult to apply. The present paper investigates the applicability of PSOA to such problems. Optimal bending design of a vibrating plate using PSOA is performed in order to minimize noise radiation. PSOA can be effectively applied to such nonlinear acoustic radiation optimization.

关键词： acoustics noise reduction particle swarm optimization algorithm finite element method Rayleigh integral method sound power level modal analysis genetic algorithm noise

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Game-relationship-based remanufacturing scheduling model with sequence-dependent setup times using improved discrete particle swarm optimization algorithm

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CONCURRENT ENGINEERING-RESEARCH AND APPLICATIONS 2022年第4期30卷 424-441页

作者： Zhang, Shuai Xu, Huifen Zhang, Hua Yang, Sihan Zhejiang Univ Finance & Econ Sch Informat Management & Artificial Intelligence 18 Xueyuan St Hangzhou 310018 Peoples R China

Remanufacturing has become a Frontier technology in sustainable manufacturing and enables end-of-life products to be restored to their new conditions. Although remanufacturing scheduling has been widely investigated, the relationship between remanufacturers and customers is rarely examined. Therefore, a new game-relationship-based remanufacturing scheduling model with sequence-dependent setup times is proposed herein. In the model, the relationship between the remanufacturer and customers is constructed as a non-cooperative game, and the interval due dates are set based on the uncertain product quality to achieve effective remanufacturing and improve customer satisfaction. Multiple remanufacturing lines differentiated based on the quality grade of products are integrated into the proposed model. In addition, sequence-dependent setup times are considered in the model, which depend on the similarity between two adjacent tasks processed on a reprocessing unit. An improved discrete particle swarm optimization algorithm is proposed to obtain Nash equilibrium solutions via an efficient global search structure and a local search strategy. The algorithm is embedded with the Nash equilibrium solution evaluation method and integrated with multiple genetic operators to update the particles. The performance of the proposed algorithm in solving the proposed model is verified via a comparison with three baseline algorithms for managing different problem instances.

关键词： remanufacturing scheduling game theory quality grade sequence-dependent setup times due date particle swarm optimization algorithm

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An effective and distributed particle swarm optimization algorithm for flexible job-shop scheduling problem

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JOURNAL OF INTELLIGENT MANUFACTURING 2018年第3期29卷 603-615页

作者： Nouiri, Maroua Bekrar, Abdelghani Jemai, Abderezak Niar, Smail Ammari, Ahmed Chiheb Fac Sci Tunis Lab LIP2 Tunis Tunisia Carthage Univ INSAT Inst MMA Lab Tunis 1080 Tunisia King Abdulaziz Univ Fac Engn Dept Elect & Comp Engn Renewable Energy Grp Jeddah 21589 Saudi Arabia UVHC LAMIH Lab F-59313 Valenciennes France Univ Lille Nord France F-59000 Lille France

Flexible job-shop scheduling problem (FJSP) is very important in many research fields such as production management and combinatorial optimization. The FJSP problems cover two difficulties namely machine assignment problem and operation sequencing problem. In this paper, we apply particle swarm optimization (PSO) algorithm to solve this FJSP problem aiming to minimize the maximum completion time criterion. Various benchmark data taken from literature, varying from Partial FJSP and Total FJSP, are tested. Experimental results proved that the developed PSO is enough effective and efficient to solve the FJSP. Our other objective in this paper, is to study the distribution of the PSO-solving method for future implementation on embedded systems that can make decisions in real time according to the state of resources and any unplanned or unforeseen events. For this aim, two multi-agent based approaches are proposed and compared using different benchmark instances.

关键词： Flexible job-shop scheduling problem Makespan particle swarm optimization algorithm Routing and scheduling Multi-agent system Enmbeded system

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Estimating soot volume fraction and temperature in flames using stochastic particle swarm optimization algorithm

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INTERNATIONAL JOURNAL OF HEAT AND MASS TRANSFER 2011年第1-3期54卷 217-224页

作者： Sun, Yi-Peng Lou, Chun Zhou, Huai-Chun Huazhong Univ Sci & Technol State Key Lab Coal Combust Wuhan 430074 Hubei Peoples R China

A simulation investigation for simultaneous reconstruction of distributions of temperature and soot volume fraction from multi-wavelength emission in a sooting flame using the stochastic particle swarm optimizer (PSO) algorithm is presented. The self-absorption of the flame is considered. The selection of parameters of the stochastic PSO algorithm and detection wavelengths is analyzed. The effects of measurement errors and optical thickness of the flame on the accuracy of the reconstruction are investigated. It proved that the stochastic PSO algorithm is robust and can obtain accurate distributions of temperature and soot volume fraction from line-of-sight intensities in only several wavelengths, especially in the flame with large optical thickness, while other methods neglecting self-attenuation of the flame will take some errors. (C) 2010 Elsevier Ltd. All rights reserved.

关键词： Inverse radiation problem particle swarm optimization algorithm Flame emission spectrum Soot volume fraction Temperature reconstruction

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Acceleration harmonic identification for an electro-hydraulic shaking table based on the Simulated Annealing-particle swarm optimization algorithm

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JOURNAL OF VIBRATION AND CONTROL 2024年第1-2期30卷 193-204页

作者： Yao, Jianjun Li, Yingzhao Yu, Xinda Liu, Yuanming Sun, Shuanghai Yan, Yukun Harbin Engn Univ Coll Mech & Elect Engn Harbin Peoples R China Harbin Engn Univ Coll Mech & Elect Engn 145 Nantong St Harbin 150001 Heilongjiang Peoples R China

Nonlinear factors exist in electro-hydraulic shaking table and may distort the response signal when a sinusoidal acceleration is inputted. To obtain accurate harmonics, a harmonic identification strategy for the acceleration of the electro-hydraulic shaking table based on the SA-PSO (Simulated Annealing-particle swarm optimization) algorithm was proposed. First, the criterion function of harmonic identification was built with the least square method. Then, the PSO algorithm was introduced into the internal circulations of the SA algorithm to speed up the local search speed to construct the harmonic identification strategy. To verify the performance of the identification strategy, the SA algorithm, PSO algorithm, and SA-PSO algorithm were successively used to identify the response signal. And the convergence rate, accuracy, and stability of the three algorithms were compared. Finally, the harmonic regeneration was carried out after the amplitude and phase of each harmonic were obtained. Experimental results show that the harmonic identification strategy has fast identification speed and high identification accuracy.

关键词： electro-hydraulic shaking table Simulated Annealing algorithm particle swarm optimization algorithm harmonic identification criterion function least square method optimization harmonic regeneration

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Fault diagnosis of electrical automatic control system of hydraulic support based on particle swarm optimization algorithm

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JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2022年第9期14卷 12091-12097页

作者： Wang, Rui Sun, Wanting Jilin Inst Chem Technol Sch Aeronaut Engn Jilin 132102 Jilin Peoples R China

The hydraulic braking system of the hoist is an extremely important part of the hoist. According to incomplete statistics, its failure rate accounts for more than half of the total number of hoist failures. Once the brake fails, the brake cylinder is stuck, etc. The light ones will bring economic losses to the enterprise, and the serious ones are more likely to endanger the personal safety of the staff. Therefore, it is more and more important to improve the reliability of the braking system of the *** purpose of this paper is to study the fault diagnosis of hydraulic support electrical automatic control system based on particle swarm optimization algorithm. This paper first analyzes the manufacturing process of the oil seal skeleton and the working principle of the manipulator, summarizes the characteristics of the manipulator failure, analyzes the factors that cause the unstable operation of the manipulator and the possible failure types of the manipulator, and analyzes the possible types of failures of the manipulator. The fault characteristics and causes of stamping manipulators. The reasons for this were analyzed in depth. An adaptive particle swarm optimization algorithm is proposed to optimize the BP (back-propagation) neural network. The particle fitness value is better than the current best value. At the same time, the inertia factor is increased to stimulate the role of these better particles in the particle update, and conversely, the inertia factor is decreased to weaken the role of the poorer particles in the particle update, and the inertia and acceleration weighting factors are nonlinear throughout the iteration process. Dynamic fitting strategy to achieve a balance between global search and local search of particle swarms. The experimental results show that the intelligent diagnosis method proposed in this paper improves the efficiency of fault diagnosis of the transmission system, and the accuracy rate is increased by more than 50%. Provide ge

关键词： particle swarm optimization algorithm Hydraulic support Electrical automatic control system Fault diagnosis

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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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