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Evaluation of multi-objective inverse heat conduction problem based on particle swarm optimization algorithm, normal distribution and finite element method

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INTERNATIONAL JOURNAL OF HEAT AND MASS TRANSFER 2018年第PartA期127卷 1114-1127页

作者： Wang, Xiaowei Li, Huiping He, Lianfang Li, Zhichao Shandong Univ Sci & Technol Sch Mat Sci & Engn 579 Qianwangang Rd Qingdao 266590 Shandong Peoples R China

The boundary heat transfer coefficient (BHTC) and thermal conductivity are the most essential thermal physical parameters, which have a significant effect on the calculation accuracy of physical fields in the numerical simulation. To simultaneously estimate the temperature-dependent BHTC and thermal conductivity by solving the inverse heat conduction problem (IHCP), a hybrid method (ZPSO) based on particle swarm optimization algorithm (PSO), normal distribution method and finite element method (FEM) is presented, where normal distribution method representing the transient solution space is used to improve the convergence speed. A two-dimensional direct heat conduction problem (DHCP) with the temperature-dependent thermal-physical parameters is simulated based on FEM. Some temperature curves at certain position of FEM model are attained in the simulation. The temperature-dependent BHTC and thermal conductivity are simultaneously evaluated by PSO-FEM and ZPSO-FEM according to the temperature curves attained in DHCP. Comparing the temperature-dependent thermal-physical parameters attained in the IHCP to that used in the DHCP, the results show that the BHTC and thermal conductivity evaluated by the ZPSO are well consistent with those used in the DHCP. The evaluation results show that the convergence of the hybrid method is well, and the convergence speed is accelerated by the ZPSO. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Inverse heat conduction Multi-objective problem particle swarm optimization algorithm Finite element method

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On the Constellation Design of Multi-GNSS Reflectometry Mission Using the particle swarm optimization algorithm

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ATMOSPHERE 2019年第12期10卷 807-807页

作者： Han, Yi Luo, Jia Xu, Xiaohua Wuhan Univ Sch Geodesy & Geomat 129 Luoyu Rd Wuhan 430079 Peoples R China Wuhan Univ State Key Lab Informat Engn Surveying Mapping & R Wuhan 430079 Peoples R China Minist Educ Key Lab Geospace Environm & Geodesy 129 Luoyu Rd Wuhan 430079 Peoples R China

Due to the great success of the CYclone Global Navigation Satellite System (CYGNSS) mission, the follow-on GNSS Reflectometry (GNSS-R) missions are being planned. In the perceivable future, signal sources for GNSS-R missions can originate from multiple global navigation satellite systems (GNSSs) including Global Positioning System (GPS), Galileo, GLONASS, and BeiDou. On the other hand, to facilitate the operational capability for sensing ocean, land, and ice features globally, multi-satellite low Earth orbit (LEO) constellations with global coverage and high spatio-temporal resolutions should be considered in the design of the follow-on GNSS-R constellation. In the present study, the particle swarm optimization (PSO) algorithm was applied to seek the optimal configuration parameters of 2D-lattice flower constellations (2D-LFCs) composed of 8, 24, 60, and 120 satellites, respectively, for global GNSS-R observations, and the fitness function was defined as the length of the time for the percentage coverage of the reflection observations reaches 90% of the globe. The configuration parameters for the optimal constellations are presented, and the performances of the optimal constellations for GNSS-R observations including the visited and the revisited coverages, and the spatial and temporal distributions of the reflections were further compared. Although the results showed that all four optimized constellations could observe GNSS reflections with proper temporal and spatial distributions, we recommend the optimal 24- and 60-satellite 2D-LFCs for future GNSS-R missions, taking into account both the performance and efficiency for the deployment of the GNSS-R missions.

关键词： multi-GNSS GNSS reflectometry constellation design particle swarm optimization algorithm

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particle swarm optimization algorithm based on parameter improvements

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JOURNAL OF COMPUTATIONAL METHODS IN SCIENCES AND ENGINEERING 2017年第3期17卷 557-568页

作者： Wu, Qinghua Song, Tao Liu, Hanmin Yan, Xuesong Wuhan Inst Technol Fac Comp Sci & Engn Wuhan 430205 Hubei Peoples R China China Univ Geosci Ctr Network & Educ Technol Wuhan 430074 Hubei Peoples R China Wuhan Inst Ship Bldg Technol Wuhan 430050 Hubei Peoples R China China Univ Geosci Sch Comp Sci Wuhan 430074 Hubei Peoples R China

Through the researches on various item improvements of particle swarm optimization algorithm, the improved particle swarm optimization algorithm is proposed for the insufficient global searching ability. This paper has improved the parameters of velocity formula and introduced new distance calculation formula to modify various parameters in the velocity formula. The improved particle swarm optimization algorithm has been tested by adopting standard test function, and the result has been compared with other particle swarm optimization algorithms. The experimental result indicated that the proposed improved particle swarm optimization algorithm in this paper got the better result for function optimization problems.

关键词： particle swarm optimization algorithm parameter improvement particle learning factor inertia weight

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A new algorithm based on the Gbest of particle swarm optimization algorithm to improve Estimation of Distribution algorithm

A new Algorithm based on the Gbest of Particle Swarm Optimiz...

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International Conference on Smart Computing and Electronic Enterprise (ICSCEE)

作者： Zhao, Qiuyue Gao, Ying Guangzhou Univ Comp Sci & Educ Software Inst Guangzhou Peoples R China

ISBN: (纸本)9781538648384

In recent years, with the rise of artificial intelligence and deep learning, as an evolutionary algorithm based on probability model, estimation of distribution algorithm has been widely research and development. The estimation of distribution algorithm without the traditional genetic operation such as crossover and mutation, is a new kind of evolution model. As an algorithm based on probabilistic mode, the estimation of distribution algorithm establishes a probabilistic model describing the solution space of optimization problems. With the emergence for big data, the convergence of the algorithm and the requirements for solving precision are also increasing. This paper attempts to improve the distribution estimation algorithm. The optimal population of each iteration is found through the location update of each iteration of the particle swarm optimization (PSO) algorithm. The simulation test was carried out with ten benchmark test function. The proposed algorithm was compared with the GA_EDA9improved genetic algorithm) and the basic distribution estimation (EDA) algorithm. Experimental results show that the new algorithm is superior to GA_EDA and basic EDA in terms of convergence and accuracy.

关键词： Estimation of distribution algorithm particle swarm optimization algorithm simulation

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Data Classification Based on the Hybrid Versions of the particle swarm optimization algorithm 7

Data Classification Based on the Hybrid Versions of the Part...

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7th Mediterranean Conference on Embedded Computing (MECO)

作者： Demidova, Liliya Klyueva, Irina Ryazan State Radio Engn Univ Ryazan Russia

ISBN: (纸本)9781538656839

The paper presents two hybrid versions of the basic PSO algorithm, involving the use of the classical Grid Search (GS) algorithm and Design of Experiment (DOE) algorithm correspondingly. These hybrid versions have been applied to the problem of search of the parameters values of the SVM classifier. The results of experimental studies confirm the application efficiency of the hybrid versions of the basic PSO algorithm with the aim of reducing of the time expenditures for searching the optimum parameters of the SVM classifier while maintaining of high quality of its classification decisions.

关键词： classification particle swarm optimization algorithm grid search algorithm SVM classifier radial basis kernel function

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A particle swarm optimization algorithm with Time Varying Parameters 30

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

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30th Chinese Control and Decision Conference (CCDC)

作者： Hu, Zhen Zou, Dexuan Kong, Zhi Shen, Xin Jiangsu Normal Univ Sch Elect Engn & Automat Xuzhou 221000 Jiangsu Peoples R China Northeastern Univ Qinhuangdao Sch Elect Engn & Automat Qinhuangdao 066004 Hebei Peoples R China

ISBN: (纸本)9781538612446

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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Convergence Analysis of Self-adaptive Immune particle swarm optimization algorithm 15th

Convergence Analysis of Self-adaptive Immune Particle Swarm ...

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15th International Symposium on Neural Networks (ISNN)

作者： Jiang, Jingqing Song, Chuyi Ping, Huan Zhang, Chenggang Inner Mongolia Univ Nationalities Coll Comp Sci & Technol Tongliao 028043 Peoples R China Inner Mongolia Univ Nationalities Coll Math Tongliao 028043 Peoples R China

ISBN: (纸本)9783319925370;9783319925363

The self-adaptive immune particle swarm optimization (SAIPSO) algorithm is a hybrid algorithm based on immune algorithm and particle swarm optimization algorithm. SAIPSO algorithm has been implemented and achieved better result compared with the classical particle swarm optimization algorithm. However, the theoretical support of the algorithm is equally important as the implementation of the algorithm. Therefore, this paper mainly uses the convergence theorem of random search algorithm and the mathematical induction to prove the convergence of SAIPSO algorithm, which will help the improvement and application of the algorithm in the future.

关键词： Immune algorithm particle swarm optimization algorithm Self-adaptation Convergence

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An improved particle swarm optimization algorithm for AVO elastic parameter inversion problem

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2019年第9期31卷

作者： Wu, Qinghua Zhu, Zhixin Yan, Xuesong Gong, Wenyin Wuhan Inst Technol Fac Comp Sci & Engn Wuhan 430074 Peoples R China China Univ Geosci Sch Comp Sci Wuhan 430074 Peoples R China

The elastic parameter inversion technique for prestack seismic data, which combines the intelligent optimization algorithms with Amplitude Variation with Offset (AVO) technology, is an effective method for oil and gas exploration. However, when certain biological-evolution-based optimization algorithms, eg, genetic algorithms, are used to solve this problem, the computation exhibits fast convergence and a strong tendency to be trapped to a local optimum, thereby leading to unsatisfactory inversion results. To address this issue, this paper proposes a swarm-intelligence-based method-particle swarm optimization (PSO) algorithm to handle the elastic parameter inversion problem. Based on the Aki-Richards approximation to the Zoeppritz equations, the improved PSO algorithm adopts a special initialization strategy, which can enhance the smoothness of the initialization parametric curves. Extensive experimental research confirms the superiority of the proposed algorithm. Specifically, the improved PSO algorithm is able to not only markedly enhance inversion precision but also render remarkably high correlation coefficients associated with the elastic parameters.

关键词： correlation coefficient elastic parameters inversion particle swarm optimization algorithm prestack seismic

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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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