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Optimal Scheduling of Cascade Reservoirs Based on an Integrated Multistrategy particle swarm algorithm

WATER 2023年第14期15卷 2593页

作者： Liu, Yixuan Mo, Li Yang, Yuqi Tao, Yitao China Yangtze Power Co Ltd Hubei Key Lab Intelligent Yangtze & Hydroelect Sci Yichang 443000 Peoples R China Huazhong Univ Sci & Technol Sch Civil & Hydraul Engn Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Hubei Key Lab Digital River Basin Sci & Technol Wuhan 430074 Peoples R China

The optimal scheduling of cascade reservoirs is an important water resource management and regulation method. In the actual operation process, its nonlinear, high-dimensional, and coupled characteristics become increasingly apparent under the influence of multiple constraints. In this study, an integrated multistrategy particle swarm optimization (IMPSO) algorithm is proposed to realize the optimal operation of mid- and long-term power generation in cascade reservoirs according to the solution problem in the scheduling process of cascade reservoirs. In IMPSO, a variety of effective improvement strategies are used, which are combined with the standard PSO algorithm in different steps, among which beta distribution initialization improves population diversity, parameter adaptive adjustment accelerates convergence speed, and the Levy flight mechanism and adaptive variable spiral search strategy balance the global and local search capabilities of the algorithm. To handle complex constraints effectively, an explicit-implicit coupled constraint handling technique based on constraint normalization is designed to guide the update process into the feasible domain of the search space. The feasibility of the proposed method is verified in the mid- and long-term power generation optimization scheduling of the lower reaches of the Jinsha River-Three Gorges cascade hydropower reservoirs. The results show that the proposed method outperforms the other methods in terms of search accuracy and has the potential to improve hydropower resource utilization and power generation efficiency significantly.

关键词： optimal scheduling cascade reservoirs particle swarm algorithm constraint handling technique

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Prediction of rockburst hazard based on particle swarm algorithm and neural network

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NEURAL COMPUTING & APPLICATIONS 2022年第4期34卷 2649-2659页

作者： Zhang, Meichang Liaoning Univ Engn & Technol Sch Min Dept Min Engn Fuxing 123000 Liaoning Peoples R China

Rockburst is a typical dynamic phenomenon of mine destruction. With the expansion of mining scale and mining depth, its harm is becoming more and more serious. This has become an important problem to be solved urgently in the mining industry. This paper aims to study the prediction and prediction of rockburst risk based on particle swarm algorithm and neural network. This paper proposes a shock risk assessment method based on BP neural network. It uses existing shock pressure data to establish a regression model through BP network, and uses PSO algorithm to optimize connection weights to evaluate the slow convergence of BP network and easy to fall into local optimality. The shortcomings of and the degree of convergence was evaluated. In addition, this paper proposes a rockburst risk prediction method. In order to improve the accuracy of rockburst prediction, regional prevention and control measures and certain risk mitigation methods can also be quickly adopted. It not only identifies the mechanical properties of coal and rock mass in the laboratory, but also judges the possibility of rockbursts during mining. The experimental results in this paper show that 10 main factors that affect rockbursts are selected, 20 standard mechanical data sets are used, and a PSO-BP-based mine explosion risk assessment model is established, and the model is compared with the standard BP model Make a comparison. The results show that compared with the standard BP model, the evaluation accuracy of the PSO-BP model is increased by 15%. Finally, an example of mine risk assessment verifies the accuracy and overall applicability of the method.

关键词： Machine Learning particle swarm algorithm Neural Network Risk Prediction Rockburst Phenomenon

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Critical evaluation of infill well placement and optimization of well spacing using the particle swarm algorithm

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JOURNAL OF PETROLEUM EXPLORATION AND PRODUCTION TECHNOLOGY 2019年第4期9卷 3113-3133页

作者： Annan Boah, Evans Kwami Senyo Kondo, Obed Aidoo Borsah, Abraham Brantson, Eric Thompson All Nations Univ Coll Oil & Gas Engn Dept Koforidua Ghana BSD Min Serv Ltd Mineral Proc Dept Pmb Asiakwa Ghana Univ Mines & Technol Dept Petr Engn Tarkwa Ghana

Optimizing the placement of new wells and well spacing are vital issues in oilfield development. In recent years, the use of particle swarm algorithm (PSA) in many reservoir applications has gained wide acceptance. More importantly, the applications of PSA in determining optimal well placement and well spacing facilitate subsurface development in oil and gas fields. Due to the quest for hydrocarbons, there is the need to maximize oil recovery from petroleum reservoirs. Besides, drilling infill wells are one way to maximize oil recovery from reservoirs. However, the problem of infill well placement is very challenging. This is because many different well placement scenarios must be evaluated when undertaking the optimization program. Most often, the variables that affect the reservoir performance are nonlinearly correlated with some degree of uncertainty. Therefore, the use of computational algorithm has become increasingly common in handling well placement problems. In this paper, PSA has been efficiently used to determine optimal locations of infill wells and their spacings in a synthetic reservoir. The reservoir used in the optimization process is a two-dimensional implicit black-oil model. The objective function in this study is the net present value of the asset (reservoir). For optimal locations, 20-acre, 40-acre and 80-acre spacing were considered for maximization of the objective function. The spacing for optimal locations was varied between wells in the reservoir model. Multiple cases for infill well locations with six existing appraisal wells were considered. After various simulation runs, the optimum locations of infill wells, number of wells and the corresponding well spacings were determined. Consequently, 4 vertical infill wells located at 40-acre spacing predicted the optimum NPV of $3.973x10(9). Therefore, this infill design is recommended for field development. Pressure and saturation distribution maps were generated with the maximization of net pres

关键词： Reservoir development Field optimization particle swarm algorithm Infill well drilling Well placement Uncertainty analysis

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A nested particle swarm algorithm based on sphere mutation to solve bi-level optimization

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SOFT COMPUTING 2019年第21期23卷 11331-11341页

作者： Zhao, Long Wei, JingXuan Xidian Univ Sch Comp Sci & Technol Xian Shaanxi Peoples R China

The problem of bi-level optimization has always been a hot topic due to its extensive application. Increasing size and complexity have prompted theoretical and practical interest in the design of effective algorithm. This paper adopts particle swarm algorithm (PSO) at both level. First, given the nested nature of bi-level problem, we introduce a hyper-sphere search into PSO as mutation operator to maintain the swarms diversity. Second, for complex constraints processing, the proposed algorithm adopts a dynamic constraint handling strategy, which makes the solution located on the constraint boundary easier to be obtained. Third, a quadratic approximation mutation is introduced into PSO, which guides particles to a better search area. Finally, the convergence is proved and the simulation results show that the proposed algorithm is effective.

关键词： Bi-level optimization particle swarm algorithm Sphere search Radial basis function (RBF)

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Research and verification on parameter solution of mixed shock model for common cause failure based on particle swarm algorithm

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QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL 2024年第6期40卷 3340-3351页

作者： Hu, Yinxiao Ge, Hongjuan He, Pei Jin, Hui Li, Huang Zou, Chunran Nanjing Univ Aeronaut & Astronaut Coll Civil Aviat Nanjing 211106 Peoples R China China Acad Civil Aviat Sci & Technol Beijing Peoples R China Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol Nanjing Peoples R China

Mixed shock model is an explicit construction method of failure probability model based on component independent failure, system nonfatal shock, and fatal shock failure, which considers common cause failure (CCF) in redundant system. For aerospace systems, a modified mixed shock model is proposed, which considers several components may fail independently and simultaneously in operation. In order to solve the issue that the parameters of the mixed shock model cannot be solved directly based on the failure probability data, a parameter solving method based on particle swarm optimization (PSO) algorithm is proposed. Additionally, the relationship between the failure probability and the gradient of the parameter change is deduced, and the reduced-order (RO) solution based on the gradient of the parameter change is proposed to improve the efficiency of the solution. A fitness function construction method based on the relative error of the solution probability and the true probability is proposed to improve the probability solution accuracy of multicomponent failure. The nonlinear inertia factor optimization method combined with fitness change is studied to improve the particle swarm dynamics. The accuracy of the results of different parameters solving sequence and different PSO methods are compared, and the effectiveness of the RO solution is verified. The results of the mixed shock model before and after modification are compared with the different CCF data, which verifies the effectiveness and wide applicability of the modified mixed shock model. The results show that the modified mixed shock model for CCF and its parameter solution method can significantly improve the probability solution accuracy of all components failure, and also provide a new theoretical basis and solution method for the quantitative analysis of multiredundant system failure.

关键词： common cause failure mixed shock model particle swarm algorithm reduced-order solution redundant system

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Parametrical optimization of particle dampers based on particle swarm algorithm

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APPLIED ACOUSTICS 2020年 160卷

作者： Zhang, Renliang Zhang, Yantong Zheng, Zhanpeng Mo, Lei Wu, Chengjun Xi An Jiao Tong Univ Sch Mech Engn Xian 710049 Peoples R China

particle damping technology is widely used to suppress the structure vibration in different fields. It is thus important to develop an optimization method of the particle damper design to achieve good damping effect. Here, a theoretical model of particle damping based on multiphase flow theory of gas-particle is used to predicate the acceleration response of asymmetrical particle-damping plate through COMSOL-MATLAB co-simulation. On the basis of the accuracy of acceleration response prediction (i.e. simulation results vs. experimental results), particle swarm algorithm is introduced to optimize the parameters of particle dampers within different frequency ranges. The optimal results (i.e. acceleration response curves and total values of acceleration) are compared with the non-optimal results, showing its feasibility of particle-damping asymmetrical plate in various target frequency ranges. Comparing to the non-optimal case, the damping effect after optimization improves significantly by reducing 10 dB more within the frequency ranges. (C) 2019 Published by Elsevier Ltd.

关键词： particle damping Optimization method particle swarm algorithm COMSOL-MATLAB co-simulation

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Path-planning research in radioactive environment based on particle swarm algorithm

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PROGRESS IN NUCLEAR ENERGY 2014年 74卷 184-192页

作者： Liu, Yong-kuo Li, Meng-kun Xie, Chun-li Peng, Min-jun Xie, Fei Harbin Engn Univ Fundamental Sci Nucl Safety & Simulat Technol Lab Harbin 150001 Peoples R China Northeast Forestry Univ Traff Coll Harbin 150040 Peoples R China

During the design, maintenance and decommissioning of nuclear facilities, nuclear radiation protection is an important part. In recent years, researchers have explored a lot of radiation protection approach, and some radiation protection approach have been applied in practice, such as visualization technique of radiation environment, path-planning method, robotics and etc., in these techniques the path-planning in radiation environment technology has become an important radiation protection measure. In this paper, we addressed a staff walking path-planning approach in radiation environment based on particle swarm algorithm and introduced some key technologies of path-planning in radiation environment To obtain the optimal walking route to verify the operation of the proposed method, we carried out the simulation experiment in which dose and distance were as decision factors. The experiment results represented the probability and the effectiveness of path-planning in radiation environment based on particle swarm algorithm. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Radioactive environment Path-planning particle swarm algorithm Simulation

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A realistic approach for designing a single-mode Y-branch for weakly guiding material system using particle swarm algorithm

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OPTICAL AND QUANTUM ELECTRONICS 2020年第2期52卷 1-10页

作者： Avad, Jihad Demirtas, Mustafa Kosku Perkgoz, Nihan Ay, Feridun Eskisehir Tech Univ Dept Elect & Elect Engn Fac Engn TR-26555 Eskisehir Turkey

We designed and optimized a realistic single-mode Y-branch made of a relatively weakly guiding material system, with the aid of particle swarm algorithm combined with a 2D finite-difference time-domain solver. Design fabricability within a 1 mu m-resolution standard lithography was enforced. The proposed design has a mean total insertion loss of 0.45 dB over wavelengths ranging from 1450 to 1580 nm with a footprint of around 40 by 10 mu m. Finally, we investigated the effects of the structure's sidewalls angle on the fidelity of the simulation.

关键词： Y-branch Photonic design optimization particle swarm algorithm Al2O3 Waveguide Integrated optics

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Hydraulic Performance Optimization of Pump Impeller Based on a Joint of particle swarm algorithm and Least-Squares Support Vector Regression

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IEEE ACCESS 2020年 8卷 203645-203654页

作者： Fang, Hua Ma, Jianfeng Zhang, Wei Yang, Hui Chen, Feng Li, Xiaojun Zhejiang Sci Tech Univ Natl Prov Joint Engn Lab Fluid Transmiss Syst Tec Hangzhou 310018 Peoples R China ZheJiang Fuchunjiang Hydropower Equipment Co Ltd Hangzhou 311501 Peoples R China Zhejiang Inst Mech & Elect Engn Dept Mech Technol Hangzhou 310053 Peoples R China

The hydraulic performance of centrifugal pumps is considerably affected by the impeller, and an effective optimization method for centrifugal pump impeller has been developed in the current study. A combination of the least-squares support vector regression machine (LSSVR) and particle swarm optimization algorithm (PSO) is proposed to redesign the impeller and improve the hydraulic performance. In the case study, four key geometric parameters of the impeller, namely, inlet angle, outlet angle, wrap angle and number of blades are selected for optimization. Maximum efficiency and constant head are selected as the optimization targets. During the optimization design, the required database for the LSSVR agent model is designed according to design of experiments. The optimal solution is then found in the established agent model space by the particle swarm algorithm and then verified by computational fluid dynamics. Ultimately, an improved impeller structure with an improved efficiency is provided. Numerical results show that the optimized impeller's efficiency is increased by 1.29% under the condition that the head is essentially unchanged. Then, the reason for the improvement of impeller hydraulic efficiency is explained by the entropy production method. The conclusions show that the PSO-LSSVR method can be used to optimize the pump impeller and achieve higher pump performance.

关键词： Centrifugal pump design optimization least-squares support vector machine particle swarm algorithm

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Software fault prediction using particle swarm algorithm with genetic algorithm and support vector machine classifier

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SOFTWARE-PRACTICE & EXPERIENCE 2020年第4期50卷 407-427页

作者： Alsghaier, Hiba Akour, Mohammed Yarmouk Univ Dept Informat Syst Irbid Jordan Al Yamamah Univ Comp Engn Dept Riyadh Saudi Arabia

Software fault prediction is a process of developing modules that are used by developers in order to help them to detect faulty classes or faulty modules in early phases of the development life cycle and to determine the modules that need more refactoring in the maintenance phase. Software reliability means the probability of failure has occurred during a period of time, so when we describe a system as not reliable, it means that it contains many errors, and these errors can be accepted in some systems, but it may lead to crucial problems in critical systems like aircraft, space shuttle, and medical systems. Therefore, locating faulty software modules is an essential step because it helps defining the modules that need more refactoring or more testing. In this article, an approach is developed by integrating genetics algorithm (GA) with support vector machine (SVM) classifier and particle swarm algorithm for software fault prediction as a stand though for better software fault prediction technique. The developed approach is applied into 24 datasets (12-NASA MDP and 12-Java open-source projects), where NASA MDP is considered as a large-scale dataset and Java open-source projects are considered as a small-scale dataset. Results indicate that integrating GA with SVM and particle swarm algorithm improves the performance of the software fault prediction process when it is applied into large-scale and small-scale datasets and overcome the limitations in the previous studies.

关键词： fault prediction genetic algorithm machine learning particle swarm algorithm

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