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Multi-objective reservoir operation using particle swarm optimization with adaptive random inertia weights 被引量:9
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作者 Hai-tao Chen Wen-chuan Wang +1 位作者 Xiao-nan Chen Lin Qiu 《Water Science and Engineering》 EI CAS CSCD 2020年第2期136-144,共9页
Based on conventional particle swarm optimization(PSO),this paper presents an efficient and reliable heuristic approach using PSO with an adaptive random inertia weight(ARIW)strategy,referred to as the ARIW-PSO algori... Based on conventional particle swarm optimization(PSO),this paper presents an efficient and reliable heuristic approach using PSO with an adaptive random inertia weight(ARIW)strategy,referred to as the ARIW-PSO algorithm,to build a multi-objective optimization model for reservoir operation.Using the triangular probability density function,the inertia weight is randomly generated,and the probability density function is automatically adjusted to make the inertia weight generally greater in the initial stage of evolution,which is suitable for global searches.In the evolution process,the inertia weight gradually decreases,which is beneficial to local searches.The performance of the ARIWPSO algorithm was investigated with some classical test functions,and the results were compared with those of the genetic algorithm(GA),the conventional PSO,and other improved PSO methods.Then,the ARIW-PSO algorithm was applied to multi-objective optimal dispatch of the Panjiakou Reservoir and multi-objective flood control operation of a reservoir group on the Luanhe River in China,including the Panjiakou Reservoir,Daheiting Reservoir,and Taolinkou Reservoir.The validity of the multi-objective optimization model for multi-reservoir systems based on the ARIW-PSO algorithm was verified. 展开更多
关键词 particle swarm optimization Genetic algorithm Random inertia weight multi-objective reservoir operation Reservoir group Panjiakou Reservoir
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Research on Optimization of Freight Train ATO Based on Elite Competition Multi-Objective Particle Swarm Optimization 被引量:1
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作者 Lingzhi Yi Renzhe Duan +3 位作者 Wang Li Yihao Wang Dake Zhang Bo Liu 《Energy and Power Engineering》 2021年第4期41-51,共11页
<div style="text-align:justify;"> In view of the complex problems that freight train ATO (automatic train operation) needs to comprehensively consider punctuality, energy saving and safety, a dynamics ... <div style="text-align:justify;"> In view of the complex problems that freight train ATO (automatic train operation) needs to comprehensively consider punctuality, energy saving and safety, a dynamics model of the freight train operation process is established based on the safety and the freight train dynamics model in the process of its operation. The algorithm of combining elite competition strategy with multi-objective particle swarm optimization technology is introduced, and the winning particles are obtained through the competition between two elite particles to guide the update of other particles, so as to balance the convergence and distribution of multi-objective particle swarm optimization. The performance comparison experimental results verify the superiority of the proposed algorithm. The simulation experiments of the actual line verify the feasibility of the model and the effectiveness of the proposed algorithm. </div> 展开更多
关键词 Freight Train Automatic Train Operation Dynamics Model Competitive multi-objective particle swarm optimization algorithm (CMOPSO) multi-objective optimization
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A hybrid discrete particle swarm optimization-genetic algorithm for multi-task scheduling problem in service oriented manufacturing systems 被引量:4
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作者 武善玉 张平 +2 位作者 李方 古锋 潘毅 《Journal of Central South University》 SCIE EI CAS CSCD 2016年第2期421-429,共9页
To cope with the task scheduling problem under multi-task and transportation consideration in large-scale service oriented manufacturing systems(SOMS), a service allocation optimization mathematical model was establis... To cope with the task scheduling problem under multi-task and transportation consideration in large-scale service oriented manufacturing systems(SOMS), a service allocation optimization mathematical model was established, and then a hybrid discrete particle swarm optimization-genetic algorithm(HDPSOGA) was proposed. In SOMS, each resource involved in the whole life cycle of a product, whether it is provided by a piece of software or a hardware device, is encapsulated into a service. So, the transportation during production of a task should be taken into account because the hard-services selected are possibly provided by various providers in different areas. In the service allocation optimization mathematical model, multi-task and transportation were considered simultaneously. In the proposed HDPSOGA algorithm, integer coding method was applied to establish the mapping between the particle location matrix and the service allocation scheme. The position updating process was performed according to the cognition part, the social part, and the previous velocity and position while introducing the crossover and mutation idea of genetic algorithm to fit the discrete space. Finally, related simulation experiments were carried out to compare with other two previous algorithms. The results indicate the effectiveness and efficiency of the proposed hybrid algorithm. 展开更多
关键词 service-oriented architecture (SOA) cyber physical systems (CPS) multi-task scheduling service allocation multi-objective optimization particle swarm algorithm
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Particle Swarm Optimization Algorithm Based on Chaotic Sequences and Dynamic Self-Adaptive Strategy
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作者 Mengshan Li Liang Liu +4 位作者 Genqin Sun Keming Su Huaijin Zhang Bingsheng Chen Yan Wu 《Journal of Computer and Communications》 2017年第12期13-23,共11页
To deal with the problems of premature convergence and tending to jump into the local optimum in the traditional particle swarm optimization, a novel improved particle swarm optimization algorithm was proposed. The se... To deal with the problems of premature convergence and tending to jump into the local optimum in the traditional particle swarm optimization, a novel improved particle swarm optimization algorithm was proposed. The self-adaptive inertia weight factor was used to accelerate the converging speed, and chaotic sequences were used to tune the acceleration coefficients for the balance between exploration and exploitation. The performance of the proposed algorithm was tested on four classical multi-objective optimization functions by comparing with the non-dominated sorting genetic algorithm and multi-objective particle swarm optimization algorithm. The results verified the effectiveness of the algorithm, which improved the premature convergence problem with faster convergence rate and strong ability to jump out of local optimum. 展开更多
关键词 particle swarm algorithm CHAOTIC SEQUENCES SELF-ADAPTIVE STRATEGY multi-objective optimization
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Dynamic Self-Adaptive Double Population Particle Swarm Optimization Algorithm Based on Lorenz Equation
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作者 Yan Wu Genqin Sun +4 位作者 Keming Su Liang Liu Huaijin Zhang Bingsheng Chen Mengshan Li 《Journal of Computer and Communications》 2017年第13期9-20,共12页
In order to improve some shortcomings of the standard particle swarm optimization algorithm, such as premature convergence and slow local search speed, a double population particle swarm optimization algorithm based o... In order to improve some shortcomings of the standard particle swarm optimization algorithm, such as premature convergence and slow local search speed, a double population particle swarm optimization algorithm based on Lorenz equation and dynamic self-adaptive strategy is proposed. Chaotic sequences produced by Lorenz equation are used to tune the acceleration coefficients for the balance between exploration and exploitation, the dynamic self-adaptive inertia weight factor is used to accelerate the converging speed, and the double population purposes to enhance convergence accuracy. The experiment was carried out with four multi-objective test functions compared with two classical multi-objective algorithms, non-dominated sorting genetic algorithm and multi-objective particle swarm optimization algorithm. The results show that the proposed algorithm has excellent performance with faster convergence rate and strong ability to jump out of local optimum, could use to solve many optimization problems. 展开更多
关键词 Improved particle swarm optimization algorithm Double POPULAtionS multi-objective Adaptive Strategy CHAOTIC SEQUENCE
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Milling Parameters Optimization of Al-Li Alloy Thin-Wall Workpieces Using Response Surface Methodology and Particle Swarm Optimization 被引量:2
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作者 Haitao Yue Chenguang Guo +2 位作者 Qiang Li Lijuan Zhao Guangbo Hao 《Computer Modeling in Engineering & Sciences》 SCIE EI 2020年第9期937-952,共16页
To improve the milling surface quality of the Al-Li alloy thin-wall workpieces and reduce the cutting energy consumption.Experimental research on the milling processing of AA2195 Al-Li alloy thin-wall workpieces based... To improve the milling surface quality of the Al-Li alloy thin-wall workpieces and reduce the cutting energy consumption.Experimental research on the milling processing of AA2195 Al-Li alloy thin-wall workpieces based on Response Surface Methodology was carried out.The single factor and interaction of milling parameters on surface roughness and specific cutting energy were analyzed,and the multi-objective optimization model was constructed.The Multiobjective Particle Swarm Optimization algorithm introducing the Chaos Local Search algorithm and the adaptive inertial weight was applied to determine the optimal combination of milling parameters.It was observed that surface roughness was mainly influenced by feed per tooth,and specific cutting energy was negatively correlated with feed per tooth,radial cutting depth and axial cutting depth,while cutting speed has a non-significant influence on specific cutting energy.The optimal combination of milling parameters with different priorities was obtained.The experimental results showed that the maximum relative error of measured and predicted values was 8.05%,and the model had high reliability,which ensured the low surface roughness and cutting energy consumption.It was of great guiding significance for the success of Al-Li alloy thin-wall milling with a high precision and energy efficiency. 展开更多
关键词 Al-Li alloy thin-wall workpieces response surface methodology surface roughness specific cutting energy multi-objective particle swarm optimization algorithm
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Dynamic Multi-objective Optimization of Chemical Processes Using Modified BareBones MOPSO Algorithm
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作者 杜文莉 王珊珊 +1 位作者 陈旭 钱锋 《Journal of Donghua University(English Edition)》 EI CAS 2014年第2期184-189,共6页
Dynamic multi-objective optimization is a complex and difficult research topic of process systems engineering. In this paper,a modified multi-objective bare-bones particle swarm optimization( MOBBPSO) algorithm is pro... Dynamic multi-objective optimization is a complex and difficult research topic of process systems engineering. In this paper,a modified multi-objective bare-bones particle swarm optimization( MOBBPSO) algorithm is proposed that takes advantage of a few parameters of bare-bones algorithm. To avoid premature convergence,Gaussian mutation is introduced; and an adaptive sampling distribution strategy is also used to improve the exploratory capability. Moreover, a circular crowded sorting approach is adopted to improve the uniformity of the population distribution.Finally, by combining the algorithm with control vector parameterization,an approach is proposed to solve the dynamic optimization problems of chemical processes. It is proved that the new algorithm performs better compared with other classic multiobjective optimization algorithms through the results of solving three dynamic optimization problems. 展开更多
关键词 dynamic multi-objective optimization bare-bones particle swarm optimization(PSO) algorithm chemical process
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Design Optimization of Permanent Magnet Eddy Current Coupler Based on an Intelligence Algorithm
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作者 Dazhi Wang Pengyi Pan Bowen Niu 《Computers, Materials & Continua》 SCIE EI 2023年第11期1535-1555,共21页
The permanent magnet eddy current coupler(PMEC)solves the problem of flexible connection and speed regulation between the motor and the load and is widely used in electrical transmission systems.It provides torque to ... The permanent magnet eddy current coupler(PMEC)solves the problem of flexible connection and speed regulation between the motor and the load and is widely used in electrical transmission systems.It provides torque to the load and generates heat and losses,reducing its energy transfer efficiency.This issue has become an obstacle for PMEC to develop toward a higher power.This paper aims to improve the overall performance of PMEC through multi-objective optimization methods.Firstly,a PMEC modeling method based on the Levenberg-Marquardt back propagation(LMBP)neural network is proposed,aiming at the characteristics of the complex input-output relationship and the strong nonlinearity of PMEC.Then,a novel competition mechanism-based multi-objective particle swarm optimization algorithm(NCMOPSO)is proposed to find the optimal structural parameters of PMEC.Chaotic search and mutation strategies are used to improve the original algorithm,which improves the shortcomings of multi-objective particle swarm optimization(MOPSO),which is too fast to converge into a global optimum,and balances the convergence and diversity of the algorithm.In order to verify the superiority and applicability of the proposed algorithm,it is compared with several popular multi-objective optimization algorithms.Applying them to the optimization model of PMEC,the results show that the proposed algorithm has better comprehensive performance.Finally,a finite element simulation model is established using the optimal structural parameters obtained by the proposed algorithm to verify the optimization results.Compared with the prototype,the optimized PMEC has reduced eddy current losses by 1.7812 kW,increased output torque by 658.5 N·m,and decreased costs by 13%,improving energy transfer efficiency. 展开更多
关键词 Competition mechanism Levenberg-Marquardt back propagation neural network multi-objective particle swarm optimization algorithm permanent magnet eddy current coupler
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Interactive Multi-objective Optimization Design for the Pylon Structure of an Airplane 被引量:4
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作者 An Weigang Li Weiji 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2007年第6期524-528,共5页
The pylon structure of an airplane is very complex, and its high-fidelity analysis is quite time-consuming. If posterior preference optimization algorithm is used to solve this problem, the huge time consumption will ... The pylon structure of an airplane is very complex, and its high-fidelity analysis is quite time-consuming. If posterior preference optimization algorithm is used to solve this problem, the huge time consumption will be unacceptable in engineering practice due to the large amount of evaluation needed for the algorithm. So, a new interactive optimization algorithm-interactive multi-objective particle swarm optimization (IMOPSO) is presented. IMOPSO is efficient, simple and operable. The decision-maker can expediently determine the accurate preference in IMOPSO. IMOPSO is used to perform the pylon structure optimization design of an airplane, and a satisfactory design is achieved after only 12 generations of IMOPSO evolutions. Compared with original design, the maximum displacement of the satisfactory design is reduced, and the mass of the satisfactory design is decreased for 22%. 展开更多
关键词 pylon structure multi-objective optimization algorithm interactive algorithm multi-objective particle swarm optimization neural network
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Conductor Arrangement and Phase Sequence Optimization Scheme for 500 kV Four-Circuit Transmission Lines on Same Tower
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作者 Deng Lu Xujun Lang +2 位作者 Bo Yang Ziyang Li Hang Geng 《Energy Engineering》 EI 2023年第10期2287-2306,共20页
The four-circuit parallel line on the same tower effectively solves the problems faced by the line reconstruction and construction under the condition of the increasing shortage of transmission corridors.Optimizing th... The four-circuit parallel line on the same tower effectively solves the problems faced by the line reconstruction and construction under the condition of the increasing shortage of transmission corridors.Optimizing the conductor and phase sequence arrangement of multiple transmission lines is conducive to improving electromagnetic and electrostatic coupling caused by electromagnetic problems.This paper uses the ATP-EMTP simulation software to build a 500 kV multi-circuit transmission line on the same tower.It stimulates the induced voltage and current values of different line lengths,tower spacing,vertical and horizontal spacing between different circuits,phase sequence arrangement,and nominal tower height.Moreover,use the BP neural network optimized by a genetic algorithm to predict the induced voltage and current under the unknown conductor and phase sequence arrangement.Finally,based on multi-objective particle swarm algorithm to construct the optimization model of conductor arrangement scheme of overhead transmission line,combined with electromagnetic environment control index,determine the optimal conductor arrangement scheme by the size of particle fitness function,a significant reduction in induced voltages and currents between transmission lines and the four-circuit conductor layout scheme meeting the requirements of the electromagnetic environment is obtained,which provides a reference for the tower design of the transmission station project. 展开更多
关键词 Spatial arrangement phase sequence arrangement genetic algorithm BP neural network multi-objective particle swarm algorithm
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An Optimization Capacity Design Method of Wind/Photovoltaic/Hydrogen Storage Power System Based on PSO-NSGA-II
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作者 Lei Xing Yakui Liu 《Energy Engineering》 EI 2023年第4期1023-1043,共21页
The optimal allocation of integrated energy systemcapacity based on the heuristic algorithms can reduce economic costs and achieve maximum consumption of renewable energy,which has attracted many attentions.However,th... The optimal allocation of integrated energy systemcapacity based on the heuristic algorithms can reduce economic costs and achieve maximum consumption of renewable energy,which has attracted many attentions.However,the optimization results of heuristic algorithms are usually influenced by the choice of hyperparameters.To solve the above problem,the particle swarm algorithm is introduced to find the optimal hyperparameters of the heuristic algorithms.Firstly,an integrated energy system consisting of the photovoltaic,wind turbine,electrolysis cell,hydrogen storage tank,and energy storage is established.Meanwhile,the minimum economic cost,the maximum wind and PV power consumption rate,and the minimum load shortage rate are considered to be the objective functions.Then,a hybrid method combined the particle swarm combined with non-dominated sorting genetic algorithms-II is proposed to solve the optimal allocation problem.According to the optimal result,the economic cost is 6.3 million RMB,and the load shortage rate is 9.83%.Finally,four comparative experiments are conducted to verify the superiority-seeking ability of the proposed method.The comparative results indicate that the proposed method possesses a strongermerit-seeking ability,resulting in a solution satisfaction rate of 87.37%,which is higher than that of the unimproved non-dominated sorting genetic algorithms-II. 展开更多
关键词 multi-objective optimization wind/photovoltaic/hydrogen power system particle swarm algorithm non-dominated sorting genetic algorithms-II
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Performance Evaluation and Comparison of Multi - Objective Optimization Algorithms for the Analytical Design of Switched Reluctance Machines
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作者 Shen Zhang Sufei Li +1 位作者 Ronald G.Harley Thomas G.Habetler 《CES Transactions on Electrical Machines and Systems》 2017年第1期58-65,共8页
This paper systematically evaluates and compares three well-engineered and popular multi-objective optimization algorithms for the design of switched reluctance machines.The multi-physics and multi-objective nature of... This paper systematically evaluates and compares three well-engineered and popular multi-objective optimization algorithms for the design of switched reluctance machines.The multi-physics and multi-objective nature of electric machine design problems are discussed,followed by benchmark studies comparing generic algorithms(GA),differential evolution(DE)algorithms and particle swarm optimizations(PSO)on a 6/4 switched reluctance machine design with seven independent variables and a strong nonlinear multi-objective Pareto front.To better quantify the quality of the Pareto fronts,five primary quality indicators are employed to serve as the algorithm testing metrics.The results show that the three algorithms have similar performances when the optimization employs only a small number of candidate designs or ultimately,a significant amount of candidate designs.However,DE tends to perform better in terms of convergence speed and the quality of Pareto front when a relatively modest amount of candidates are considered. 展开更多
关键词 Design methodology differential evolution(DE) generic algorithm(GA) multi-objective optimization algorithms particle swarm optimization(PSO) switched reluctance machines
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Dynamic Allocation of Manufacturing Tasks and Resources in Shared Manufacturing
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作者 Caiyun Liu Peng Liu 《Intelligent Automation & Soft Computing》 SCIE 2023年第6期3221-3242,共22页
Shared manufacturing is recognized as a new point-to-point manufac-turing mode in the digital era.Shared manufacturing is referred to as a new man-ufacturing mode to realize the dynamic allocation of manufacturing tas... Shared manufacturing is recognized as a new point-to-point manufac-turing mode in the digital era.Shared manufacturing is referred to as a new man-ufacturing mode to realize the dynamic allocation of manufacturing tasks and resources.Compared with the traditional mode,shared manufacturing offers more abundant manufacturing resources and flexible configuration options.This paper proposes a model based on the description of the dynamic allocation of tasks and resources in the shared manufacturing environment,and the characteristics of shared manufacturing resource allocation.The execution of manufacturing tasks,in which candidate manufacturing resources enter or exit at various time nodes,enables the dynamic allocation of manufacturing tasks and resources.Then non-dominated sorting genetic algorithm(NSGA-II)and multi-objective particle swarm optimization(MOPSO)algorithms are designed to solve the model.The optimal parameter settings for the NSGA-II and MOPSO algorithms have been obtained according to the experiments with various population sizes and iteration numbers.In addition,the proposed model’s efficiency,which considers the entries and exits of manufacturing resources in the shared manufacturing environment,is further demonstrated by the overlap between the outputs of the NSGA-II and MOPSO algorithms for optimal resource allocation. 展开更多
关键词 Shared manufacturing dynamic allocation variation of resources non-dominated sorting genetic algorithm(NSGA-II) multi-objective particle swarm optimization(MOPSO)algorithm
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Optimizing the lattice design of a diffraction-limited storage ring with a rational combination of particle swarm and genetic algorithms 被引量:6
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作者 焦毅 徐刚 《Chinese Physics C》 SCIE CAS CSCD 2017年第2期166-176,共11页
In the lattice design of a diffraction-limited storage ring(DLSR) consisting of compact multi-bend achromats(MBAs), it is challenging to simultaneously achieve an ultralow emittance and a satisfactory nonlinear pe... In the lattice design of a diffraction-limited storage ring(DLSR) consisting of compact multi-bend achromats(MBAs), it is challenging to simultaneously achieve an ultralow emittance and a satisfactory nonlinear performance, due to extremely large nonlinearities and limited tuning ranges of the element parameters. Nevertheless, in this paper we show that the potential of a DLSR design can be explored with a successive and iterative implementation of the multi-objective particle swarm optimization(MOPSO) and multi-objective genetic algorithm(MOGA). For the High Energy Photon Source, a planned kilometer-scale DLSR, optimizations indicate that it is feasible to attain a natural emittance of about 50 pm·rad, and simultaneously realize a sufficient ring acceptance for on-axis longitudinal injection, by using a hybrid MBA lattice. In particular, this study demonstrates that a rational combination of the MOPSO and MOGA is more effective than either of them alone, in approaching the true global optima of an explorative multi-objective problem with many optimizing variables and local optima. 展开更多
关键词 diffraction-limited storage ring High Energy Photon Source multi-objective particle swarm optimization multi-objective genetic algorithm lattice design
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Modeling and multi-objective optimization of a gasoline engine using neural networks and evolutionary algorithms 被引量:6
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作者 JoséD. MARTíNEZ-MORALES Elvia R. PALACIOS-HERNáNDEZ Gerardo A. VELáZQUEZ-CARRILLO 《Journal of Zhejiang University-Science A(Applied Physics & Engineering)》 SCIE EI CAS CSCD 2013年第9期657-670,共14页
In this paper, a multi-objective particle swarm optimization (MOPSO) algorithm and a nondominated sorting genetic algorithm II (NSGA-II) are used to optimize the operating parameters of a 1.6 L, spark ignition (S... In this paper, a multi-objective particle swarm optimization (MOPSO) algorithm and a nondominated sorting genetic algorithm II (NSGA-II) are used to optimize the operating parameters of a 1.6 L, spark ignition (SI) gasoline engine. The aim of this optimization is to reduce engine emissions in terms of carbon monoxide (CO), hydrocarbons (HC), and nitrogen oxides (NOx), which are the causes of diverse environmental problems such as air pollution and global warming. Stationary engine tests were performed for data generation, covering 60 operating conditions. Artificial neural networks (ANNs) were used to predict exhaust emissions, whose inputs were from six engine operating parameters, and the outputs were three resulting exhaust emissions. The outputs of ANNs were used to evaluate objective functions within the optimization algorithms: NSGA-II and MOPSO. Then a decision-making process was conducted, using a fuzzy method to select a Pareto solution with which the best emission reductions can be achieved. The NSGA-II algorithm achieved reductions of at least 9.84%, 82.44%, and 13.78% for CO, HC, and NOx, respectively. With a MOPSO algorithm the reached reductions were at least 13.68%, 83.80%, and 7.67% for CO, HC, and NOx, respectively. 展开更多
关键词 Engine calibration multi-objective optimization Neural networks Multiple objective particle swarm optimization(MOPSO) Nondominated sorting genetic algorithm II (NSGA-II)
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XGRouter: high-quality global router in X-architecture with particle swarm optimization 被引量:2
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作者 Genggeng LIU Wenzhong GUO +2 位作者 Rongrong LI Yuzhen NIU Guolong CHEN 《Frontiers of Computer Science》 SCIE EI CSCD 2015年第4期576-594,共19页
This paper presents a high-quality very large scale integration (VLSI) global router in X-architecture, called XGRouter, that heavily relies on integer linear pro- gramming (ILP) techniques, partition strategy and... This paper presents a high-quality very large scale integration (VLSI) global router in X-architecture, called XGRouter, that heavily relies on integer linear pro- gramming (ILP) techniques, partition strategy and particle swarm optimization (PSO). A new ILP formulation, which can achieve more uniform routing solution than other formu- lations and can be effectively solved by the proposed PSO is proposed. To effectively use the new ILP formulation, a partition strategy that decomposes a large-sized problem into some small-sized sub-problems is adopted and the routing re- gion is extended progressively from the most congested re- git)n. In the post-processing stage of XGRouter, maze rout- ing based on new routing edge cost is designed to further optimize the total wire length and mantain the congestion uniformity. To our best knowledge, XGRouter is the first work to use a concurrent algorithm to solve the global rout- ing problem in X-architecture. Experimental results show that XGRouter can produce solutions of higher quality than other global routers. And, like several state-of-the-art global touters, XGRouter has no overflow. 展开更多
关键词 global routing OVERFLOW total wire length con-gestion uniformity X-architecture particle swarm optimiza-tion integer linear programming
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考虑多源协同的主动配电网故障恢复策略 被引量:2
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作者 黄代雄 汪志军 +2 位作者 袁俑斌 余奕夫 周伟 《高压电器》 CAS CSCD 北大核心 2024年第2期210-215,222,共7页
随着科技的发展,主动配电网中面临了多项重要问题,首先,随着大量柔性负荷和电动汽车的接入,负荷种类显著多元化,增加了电网管理的复杂性。其次,可再生能源的随机性和波动性对电网的故障恢复提出了重要挑战。为了应对这些问题,研究提出... 随着科技的发展,主动配电网中面临了多项重要问题,首先,随着大量柔性负荷和电动汽车的接入,负荷种类显著多元化,增加了电网管理的复杂性。其次,可再生能源的随机性和波动性对电网的故障恢复提出了重要挑战。为了应对这些问题,研究提出了一种针对各种能源互动的主动配电网故障恢复策略。该策略建立了一个优化模型,以最大数量可控分布式电源行动和最小功率损失为目标,同时满足系统安全约束。为了建立该模型,使用了改进的二进制粒子群算法和遗传算法。通过在IEEE33节点配电网模型上的仿真分析,实验结果表明,该策略能够有效降低电网损失,并最大程度地恢复失电负荷,为面对可再生能源和多元化负荷挑战的电网管理提供了一种潜在的解决方案。 展开更多
关键词 主动配电网 柔性负荷 可再生能源 故障恢复 粒子群算法
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基于粒子群算法的瞬变电磁检测小车结构优化
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作者 卫伟 赵弘 《石油机械》 北大核心 2024年第3期117-125,共9页
利用瞬变电磁法进行管道移动检测的过程中,经常由于检测设备抖动造成检测信号的误差或检测失败。为此,设计了一款可搭载瞬变电磁检测设备的自动检测小车。基于检测小车和埋地管道检测的工作要求,进行可变线圈搭载平台和减震机构设计,并... 利用瞬变电磁法进行管道移动检测的过程中,经常由于检测设备抖动造成检测信号的误差或检测失败。为此,设计了一款可搭载瞬变电磁检测设备的自动检测小车。基于检测小车和埋地管道检测的工作要求,进行可变线圈搭载平台和减震机构设计,并利用解析法对弹簧阻尼器的刚度系数和阻尼系数进行优化。基于ANSYS Workbench软件对上摆臂和车轮连接件进行拓扑优化。基于减震机构的关键零件下摆臂对整体性能的影响,利用CCD中心组合试验法,进行仿真试验设计取得数据,并利用响应面法得出最大应力和质量与下摆臂设计参数的映射关系。采用粒子群算法对映射关系进行优化设计,获得最大应力和质量最小时的下摆臂参数。优化结果表明:采用Adams对优化后的检测小车进行运动学仿真,小车平台质心在竖直方向上位移和速度的峰值分别降低约55.7%和26.5%,均值分别降低约67.9%和24.2%,波动的次数也明显减少,检测小车性能显著增强。所得结论可为检测小车设计提供理论参考,对管道缺陷检测领域具有一定的工程意义。 展开更多
关键词 埋地管道 瞬变电磁检测 检测小车 粒子群算法 优化设计 仿真分析
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基于Bi-LSTM神经网络的室内可见光定位方法
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作者 王乐乐 秦岭 +1 位作者 胡晓莉 赵德胜 《光通信技术》 北大核心 2024年第2期36-41,共6页
双向长短时记忆(Bi-LSTM)神经网络由于超参数众多,难以获得最优系统模型。同时,考虑到灰狼优化(GWO)算法可能过早收敛的情况,提出了一种采用GWO结合粒子群(GWO-PSO)算法优化Bi-LSTM神经网络的单灯定位方法。通过优化网络中的学习率、隐... 双向长短时记忆(Bi-LSTM)神经网络由于超参数众多,难以获得最优系统模型。同时,考虑到灰狼优化(GWO)算法可能过早收敛的情况,提出了一种采用GWO结合粒子群(GWO-PSO)算法优化Bi-LSTM神经网络的单灯定位方法。通过优化网络中的学习率、隐藏神经元个数等超参数,提高系统的稳定性和定位精度。最后,采用加权K邻近(WKNN)算法对误差较大的点进行优化,以获得更精确的定位位置。仿真结果表明,在3 m×3.6 m×3 m的室内环境中,所提定位方法的平均定位误差为3.57 cm,其中90%的定位误差在6 cm内。 展开更多
关键词 可见光定位 双向长短时记忆 灰狼结合粒子群 加权K近邻
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Aerodynamic multi-objective integrated optimization based on principal component analysis 被引量:11
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作者 Jiangtao HUANG Zhu ZHOU +2 位作者 Zhenghong GAO Miao ZHANG Lei YU 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2017年第4期1336-1348,共13页
Based on improved multi-objective particle swarm optimization(MOPSO) algorithm with principal component analysis(PCA) methodology, an efficient high-dimension multiobjective optimization method is proposed, which,... Based on improved multi-objective particle swarm optimization(MOPSO) algorithm with principal component analysis(PCA) methodology, an efficient high-dimension multiobjective optimization method is proposed, which, as the purpose of this paper, aims to improve the convergence of Pareto front in multi-objective optimization design. The mathematical efficiency,the physical reasonableness and the reliability in dealing with redundant objectives of PCA are verified by typical DTLZ5 test function and multi-objective correlation analysis of supercritical airfoil,and the proposed method is integrated into aircraft multi-disciplinary design(AMDEsign) platform, which contains aerodynamics, stealth and structure weight analysis and optimization module.Then the proposed method is used for the multi-point integrated aerodynamic optimization of a wide-body passenger aircraft, in which the redundant objectives identified by PCA are transformed to optimization constraints, and several design methods are compared. The design results illustrate that the strategy used in this paper is sufficient and multi-point design requirements of the passenger aircraft are reached. The visualization level of non-dominant Pareto set is improved by effectively reducing the dimension without losing the primary feature of the problem. 展开更多
关键词 Aerodynamic optimization Dimensional reduction Improved multi-objective particle swarm optimization(MOPSO) algorithm multi-objective Principal component analysis
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