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Optimal Energy Consumption Optimization in a Smart House by Considering Electric Vehicles and Demand Response via a Hybrid Gravitational Search and Particle Swarm Optimization Algorithm 认领 引用
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作者 Rongxin Zhang Chengying Yang Xuetao Li 《Energy Engineering》 EI 2022年第6期2489-2511,共23页
Buildings are the main energy consumers across the world,especially in urban communities.Building smartization,or the smartification of housing,therefore,is a major step towards energy grid smartization too.By control... Buildings are the main energy consumers across the world,especially in urban communities.Building smartization,or the smartification of housing,therefore,is a major step towards energy grid smartization too.By controlling the energy consumption of lighting,heating,and cooling systems,energy consumption can be optimized.All or some part of the energy consumed in future smart buildings must be supplied by renewable energy sources(RES),which mitigates environmental impacts and reduces peak demand for electrical energy.In this paper,a new optimization algorithm is applied to solve the optimal energy consumption problem by considering the electric vehicles and demand response in smart homes.In this way,large power stations that work with fossil fuels will no longer be developed.The current study modeled and evaluated the performance of a smart house in the presence of electric vehicles(EVs)with bidirectional power exchangeability with the power grid,an energy storage system(ESS),and solar panels.Additionally,the solar RES and ESS for predicting solar-generated power prediction uncertainty have been considered in this work.Different case studies,including the sales of electrical energy resulting from PV panels’generated power to the power grid,time-variable loads such as washing machines,and different demand response(DR)strategies based on energy price variations were taken into account to assess the economic and technical effects of EVs,BESS,and solar panels.The proposed model was simulated in MATLAB.A hybrid particle swarm optimization(PSO)and gravitational search(GS)algorithm were utilized for optimization.Scenario generation and reduction were performed via LHS and backward methods,respectively.Obtained results demonstrate that the proposed model minimizes the energy supply cost by considering the stochastic time of use(STOU)loads,EV,ESS,and PV system.Based on the results,the proposed model markedly reduced the electricity costs of the smart house. 展开更多
关键词 Energy management smart house particle swarm optimization algorithm gravitational search algorithm demand response electric vehicle
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Hypersonic reentry trajectory planning by using hybrid fractional-order particle swarm optimization and gravitational search algorithm 认领 引用 被引量：15
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作者 Khurram SHAHZAD SANA Weiduo HU 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2021年第1期50-67,共18页
This paper proposes a novel hybrid algorithm called Fractional-order Particle Swarm optimization Gravitational Search Algorithm(FPSOGSA)and applies it to the trajectory planning of the hypersonic lifting reentry fligh... This paper proposes a novel hybrid algorithm called Fractional-order Particle Swarm optimization Gravitational Search Algorithm(FPSOGSA)and applies it to the trajectory planning of the hypersonic lifting reentry flight vehicles.The proposed method is used to calculate the control profiles to achieve the two objectives,namely a smoother trajectory and enforcement of the path constraints with terminal accuracy.The smoothness of the trajectory is achieved by scheduling the bank angle with the aid of a modified scheme known as a Quasi-Equilibrium Glide(QEG)scheme.The aerodynamic load factor and the dynamic pressure path constraints are enforced by further planning of the bank angle with the help of a constraint enforcement scheme.The maximum heating rate path constraint is enforced through the angle of attack parameterization.The Common Aero Vehicle(CAV)flight vehicle is used for the simulation purpose to test and compare the proposed method with that of the standard Particle Swarm Optimization(PSO)method and the standard Gravitational Search Algorithm(GSA).The simulation results confirm the efficiency of the proposed FPSOGSA method over the standard PSO and the GSA methods by showing its better convergence and computation efficiency. 展开更多
关键词 Fractional-order Gravitational search algorithm Particle swarm optimization Reentry gliding vehicle Trajectory optimization
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Development of hybrid optimization algorithm for structures furnished with seismic damper devices using the particle swarm optimization method and gravitational search algorithm 认领 引用 被引量：2
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作者 Najad Ayyash Farzad Hejazi 《Earthquake Engineering and Engineering Vibration》 SCIE EI CSCD 2022年第2期455-474,共20页
Previous studies about optimizing earthquake structural energy dissipation systems indicated that most existing techniques employ merely one or a few parameters as design variables in the optimization process,and ther... Previous studies about optimizing earthquake structural energy dissipation systems indicated that most existing techniques employ merely one or a few parameters as design variables in the optimization process,and thereby are only applicable only to simple,single,or multiple degree-of-freedom structures.The current approaches to optimization procedures take a specific damper with its properties and observe the effect of applying time history data to the building;however,there are many different dampers and isolators that can be used.Furthermore,there is a lack of studies regarding the optimum location for various viscous and wall dampers.The main aim of this study is hybridization of the particle swarm optimization(PSO) and gravitational search algorithm(GSA) to optimize the performance of earthquake energy dissipation systems(i.e.,damper devices) simultaneously with optimizing the characteristics of the structure.Four types of structural dampers device are considered in this study:(ⅰ) variable stiffness bracing(VSB) system,(ⅱ) rubber wall damper(RWD),(ⅲ) nonlinear conical spring bracing(NCSB) device,(iv) and multi-action stiffener(MAS) device.Since many parameters may affect the design of seismic resistant structures,this study proposes a hybrid of PSO and GSA to develop a hybrid,multi-objective optimization method to resolve the aforementioned problems.The characteristics of the above-mentioned damper devices as well as the section size for structural beams and columns are considered as variables for development of the PSO-GSA optimization algorithm to minimize structural seismic response in terms of nodal displacement(in three directions) as well as plastic hinge formation in structural members simultaneously with the weight of the structure.After that,the optimization algorithm is implemented to identify the best position of the damper device in the structural frame to have the maximum effect and minimize the seismic structure response.To examine the performance of the proposed PSO-GSA optimization method,it has been applied to a three-story reinforced structure equipped with a seismic damper device.The results revealed that the method successfully optimized the earthquake energy dissipation systems and reduced the effects of earthquakes on structures,which significantly increase the building’s stability and safety during seismic excitation.The analysis results showed a reduction in the seismic response of the structure regarding the formation of plastic hinges in structural members as well as the displacement of each story to approximately 99.63%,60.5%,79.13% and 57.42% for the VSB device,RWD,NCSB device,and MAS device,respectively.This shows that using the PSO-GSA optimization algorithm and optimized damper devices in the structure resulted in no structural damage due to earthquake vibration. 展开更多
关键词 hybrid optimization algorithm structures earthquake seismic damper devices particle swarm optimization method gravitational search algorithm
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Optimization of Thermal Aware VLSI Non-Slicing Floorplanning Using Hybrid Particle Swarm Optimization Algorithm-Harmony Search Algorithm 认领 引用 被引量：1
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作者 Sivaranjani Paramasivam Senthilkumar Athappan +1 位作者 Eswari Devi Natrajan Maheswaran Shanmugam 《Circuits and Systems》 2016年第5期562-573,共12页
Floorplanning is a prominent area in the Very Large-Scale Integrated (VLSI) circuit design automation, because it influences the performance, size, yield and reliability of the VLSI chips. It is the process of estimat... Floorplanning is a prominent area in the Very Large-Scale Integrated (VLSI) circuit design automation, because it influences the performance, size, yield and reliability of the VLSI chips. It is the process of estimating the positions and shapes of the modules. A high packing density, small feature size and high clock frequency make the Integrated Circuit (IC) to dissipate large amount of heat. So, in this paper, a methodology is presented to distribute the temperature of the module on the layout while simultaneously optimizing the total area and wirelength by using a hybrid Particle Swarm Optimization-Harmony Search (HPSOHS) algorithm. This hybrid algorithm employs diversification technique (PSO) to obtain global optima and intensification strategy (HS) to achieve the best solution at the local level and Modified Corner List algorithm (MCL) for floorplan representation. A thermal modelling tool called hotspot tool is integrated with the proposed algorithm to obtain the temperature at the block level. The proposed algorithm is illustrated using Microelectronics Centre of North Carolina (MCNC) benchmark circuits. The results obtained are compared with the solutions derived from other stochastic algorithms and the proposed algorithm provides better solution. 展开更多
关键词 VLSI Non-Slicing Floorplan Modified Corner List (MCL) Algorithm Hybrid Particle Swarm Optimization-Harmony Search Algorithm (HPSOHS)
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A composite particle swarm algorithm for global optimization of multimodal functions 认领 引用 被引量：7
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作者 谭冠政 鲍琨 Richard Maina Rimiru 《Journal of Central South University》 SCIE EI CAS 2014年第5期1871-1880,共10页
During the last decade, many variants of the original particle swarm optimization （PSO） algorithm have been proposed for global numerical optimization, hut they usually face many challenges such as low solution qual... During the last decade, many variants of the original particle swarm optimization （PSO） algorithm have been proposed for global numerical optimization, hut they usually face many challenges such as low solution quality and slow convergence speed on multimodal function optimization. A composite particle swarm optimization （CPSO） for solving these difficulties is presented, in which a novel learning strategy plus an assisted search mechanism framework is used. Instead of simple learning strategy of the original PSO, the proposed CPSO combines one particle＇s historical best information and the global best information into one learning exemplar to guide the particle movement. The proposed learning strategy can reserve the original search information and lead to faster convergence speed. The proposed assisted search mechanism is designed to look for the global optimum. Search direction of particles can be greatly changed by this mechanism so that the algorithm has a large chance to escape from local optima. In order to make the assisted search mechanism more efficient and the algorithm more reliable, the executive probability of the assisted search mechanism is adjusted by the feedback of the improvement degree of optimal value after each iteration. According to the result of numerical experiments on multimodal benchmark functions such as Schwefel, Rastrigin, Ackley and Griewank both with and without coordinate rotation, the proposed CPSO offers faster convergence speed, higher quality solution and stronger robustness than other variants of PSO. 展开更多
关键词 particle swarm algorithm global numerical optimization novel learning strategy assisted search mechanism feedbackprobability regulation
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A Hybrid Optimizer Based On Firefly Algorithm And Particle Swarm Optimization Algorithm 认领 引用
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作者 Xuewen Xia Ling Gui 《江西公路科技》 2020年第1期55-73,共19页
As two widely used evolutionary algorithms,particle swarm optimization(PSO)and firefly algorithm(FA)have been successfully applied to diverse difficult applications.And extensive experiments verify their own merits an... As two widely used evolutionary algorithms,particle swarm optimization(PSO)and firefly algorithm(FA)have been successfully applied to diverse difficult applications.And extensive experiments verify their own merits and characteristics.To efficiently utilize different advantages of PSO and FA,three novel operators are proposed in a hybrid optimizer based on the two algorithms,named as FAPSO in this paper.Firstly,the population of FAPSO is divided into two sub-populations selecting FA and PSO as their basic algorithm to carry out the optimization process,respectively.To exchange the information of the two sub-populations and then efficiently utilize the merits of PSO and FA,the sub-populations share their own optimal solutions while they have stagnated more than a predefined threshold.Secondly,each dimension of the search space is divided into many small-sized sub-regions,based on which much historical knowledge is recorded to help the current best solution to carry out a detecting operator.The purposeful detecting operator enables the population to find a more promising sub-region,and then jumps out of a possible local optimum.Lastly,a classical local search strategy,i.e.,BFGS QuasiNewton method,is introduced to improve the exploitative capability of FAPSO.Extensive simulations upon different functions demonstrate that FAPSO is not only outperforms the two basic algorithm,i.e.,FA and PSO,but also surpasses some state-of-the-art variants of FA and PSO,as well as two hybrid algorithms. 展开更多
关键词 Firefly algorithm Particle swarm optimization Knowledge-based detecting Local search operator
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A hybrid constriction coefficientbased particle swarm optimization and gravitational search algorithm for training multi-layer perceptron 认领 引用 被引量：3
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作者 Sajad Ahmad Rather P.Shanthi Bala 《International Journal of Intelligent Computing and Cybernetics》 EI 2020年第2期129-165,共37页
Purpose-In this paper,a newly proposed hybridization algorithm namely constriction coefficient-based particle swarm optimization and gravitational search algorithm(CPSOGSA)has been employed for training MLP to overcom... Purpose-In this paper,a newly proposed hybridization algorithm namely constriction coefficient-based particle swarm optimization and gravitational search algorithm(CPSOGSA)has been employed for training MLP to overcome sensitivity to initialization,premature convergence,and stagnation in local optima problems of MLP.Design/methodology/approach-In this study,the exploration of the search space is carried out by gravitational search algorithm(GSA)and optimization of candidate solutions,i.e.exploitation is performed by particle swarm optimization(PSO).For training the multi-layer perceptron(MLP),CPSOGSA uses sigmoid fitness function for finding the proper combination of connection weights and neural biases to minimize the error.Secondly,a matrix encoding strategy is utilized for providing one to one correspondence between weights and biases of MLP and agents of CPSOGSA.Findings-The experimental findings convey that CPSOGSA is a better MLP trainer as compared to other stochastic algorithms because it provides superior results in terms of resolving stagnation in local optima and convergence speed problems.Besides,it gives the best results for breast cancer,heart,sine function and sigmoid function datasets as compared to other participating algorithms.Moreover,CPSOGSA also provides very competitive results for other datasets.Originality/value-The CPSOGSA performed effectively in overcoming stagnation in local optima problem and increasing the overall convergence speed of MLP.Basically,CPSOGSA is a hybrid optimization algorithm which has powerful characteristics of global exploration capability and high local exploitation power.In the research literature,a little work is available where CPSO and GSA have been utilized for training MLP.The only related research paper was given by Mirjalili et al.,in 2012.They have used standard PSO and GSA for training simple FNNs.However,the work employed only three datasets and used the MSE performance metric for evaluating the efficiency of the algorithms.In this paper,eight different standard datasets and five performance metrics have been utilized for investigating the efficiency of CPSOGSA in training MLPs.In addition,a non-parametric pair-wise statistical test namely the Wilcoxon rank-sum test has been carried out at a 5%significance level to statistically validate the simulation results.Besides,eight state-of-the-art metaheuristic algorithms were employed for comparative analysis of the experimental results to further raise the authenticity of the experimental setup. 展开更多
关键词 Neural network Feedforward neural network(FNN) Gravitational search algorithm(GSA) Particle swarm optimization(PSO) Hybridization CPSOGSA Multi-layer perceptron(MLP)
Binary Gravitational Search based Algorithm for Optimum Siting and Sizing of DG and Shunt Capacitors in Radial Distribution Systems 认领 引用
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作者 N. A. Khan S. Ghosh S. P. Ghoshal 《Energy and Power Engineering》 2013年第4期1005-1010,共6页
This paper presents a binary gravitational search algorithm (BGSA) is applied to solve the problem of optimal allotment of DG sets and Shunt capacitors in radial distribution systems. The problem is formulated as a no... This paper presents a binary gravitational search algorithm (BGSA) is applied to solve the problem of optimal allotment of DG sets and Shunt capacitors in radial distribution systems. The problem is formulated as a nonlinear constrained single-objective optimization problem where the total line loss (TLL) and the total voltage deviations (TVD) are to be minimized separately by incorporating optimal placement of DG units and shunt capacitors with constraints which include limits on voltage, sizes of installed capacitors and DG. This BGSA is applied on the balanced IEEE 10 Bus distribution network and the results are compared with conventional binary particle swarm optimization. 展开更多
关键词 Normal Load Flow Radial Distribution System Distributed Generation Shunt Capacitors Binary Particle Swarm Optimization Binary Gravitational Search Algorithm Total line Loss Total Voltage Deviation
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基于禁忌搜索与粒子群优化算法的地下水污染源信息辨识 认领 引用 被引量：1
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作者 徐津 伍梦天 +3 位作者 李凯 王玲玲 朱海 王明辉 《河海大学学报（自然科学版）》 CAS CSCD 北大核心 2026年第1期36-42,共7页
为准确辨识地下水污染源位置、污染物释放过程等关键信息,采用模拟-优化理论框架,将需要同步辨识多种污染源信息的地下水反演问题概化为包含离散型、连续型变量的混合变量优化问题,并提出了一种基于禁忌搜索与粒子群优化算法的两阶段组... 为准确辨识地下水污染源位置、污染物释放过程等关键信息,采用模拟-优化理论框架,将需要同步辨识多种污染源信息的地下水反演问题概化为包含离散型、连续型变量的混合变量优化问题,并提出了一种基于禁忌搜索与粒子群优化算法的两阶段组合优化(TS-PSO)算法,该算法采用禁忌搜索策略确定污染源位置,利用粒子群优化算法识别污染物的释放强度及释放过程。算例验证结果表明:与传统演化算法(GA、PSO算法)相比,TS-PSO算法的求解效率更高,计算结果更可靠,计算精度更高;对于多个污染源的反演问题,TS-PSO算法可快速、有效地辨识污染源位置、污染物释放强度和释放过程。 展开更多
关键词 地下水污染 信息辨识 优化算法 禁忌搜索 粒子群优化算法
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基于系统辨识的微型涡喷发动机动态特性建模 认领 引用
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作者 于军力 李泉明 +3 位作者 付宇 范承志 林瀚 左洪福 《推进技术》 EI CAS CSCD 北大核心 2026年第3期295-310,共16页
针对微型涡喷发动机动态特性建模精度不足、实时性较差等问题,提出一种基于粒子群算法和麻雀算法相结合(PSO-SSA)改进非线性自回归外源(NARX)神经网络的动态辨识建模方法。通过自主搭建的试验台,采集推杆加速、拉杆减速及稳态过程的试... 针对微型涡喷发动机动态特性建模精度不足、实时性较差等问题,提出一种基于粒子群算法和麻雀算法相结合(PSO-SSA)改进非线性自回归外源(NARX)神经网络的动态辨识建模方法。通过自主搭建的试验台,采集推杆加速、拉杆减速及稳态过程的试验数据,建立了以燃油流量和进气温度为输入,转速和推力为输出的动态特性模型。同时,采用NARX,PSO-NARX,SSA-NARX三种方法作为对照组进行对比验证。验证结果表明,本文提出方法显著优于另外三种。其中,推杆加速阶段转速预测的均方误差(MSE)和平均相对误差(MRE)分别降至5.1486×10-5和1.4393%,推力预测的MSE和MRE降至4.2309×10-5和1.5825%;拉杆减速阶段转速预测的MSE和MRE分别降至1.040×10-4和2.1946%,推力预测的MSE和MRE分别降至9.3202×10-5和3.2614%。同时,加减速阶段的平均响应时间(ART)分别降至8.738 ms,7.586 ms。模型的精度、实时性和鲁棒性均满足仿真需求,为微型涡喷发动机性能优化、故障诊断及健康管理提供了理论支持。 展开更多
关键词 微型涡喷发动机 动态特性 系统辨识 非线性自回归外源神经网络 粒子群算法 麻雀算法
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融合密度峰值决策的粒子群优化算法 认领 引用
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作者 赵晨颖 袁书娟 +2 位作者 孔闪闪 杨爱民 魏佳妹 《广西师范大学学报（自然科学版）》 CAS 北大核心 2026年第2期145-163,共19页
粒子群优化算法(PSO)作为群智能优化的一种经典算法得到广泛应用,但其面对不同问题时不能根据群体状态进行实时调整,缺乏一定灵活性。为此,本文提出一种融合密度峰值决策的粒子群优化算法(DVPSO)。针对初始化,设计精英佳点集双型映射,... 粒子群优化算法(PSO)作为群智能优化的一种经典算法得到广泛应用,但其面对不同问题时不能根据群体状态进行实时调整,缺乏一定灵活性。为此,本文提出一种融合密度峰值决策的粒子群优化算法(DVPSO)。针对初始化,设计精英佳点集双型映射,提升不同类型粒子分布质量;针对速度更新,构建基于密度峰值的信息交互机制,平衡粒子搜索倾向;针对位置更新,提出步长搜索算子的动态双邻域搜索策略,结合种群状态及寻优范围变化,调控粒子移动的同时兼顾搜索灵活性。仿真实验选用12个测试函数,将DVPSO与PSO及其他5种较新的群智能优化算法进行对比,并在2个工程问题上与5种新兴智能算法对比。结果表明,DVPSO算法具备较好的搜索精度和稳定性,验证了算法的适应性和良好性能。 展开更多
关键词 粒子群优化算法 密度峰值 精英佳点集 信息交互 动态邻域搜索
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基于BAS-IPSO的工业机器人运动轨迹规划 认领 引用
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作者 姚江云 王宽田 +1 位作者 刘斌 陈国庆 《机械传动》 北大核心 2026年第5期163-169,共7页
【目的】针对在复杂装配场景中机器人的装配效率与装配质量相互制约的问题,将天牛须搜索(Beetle Antennae Search,BAS)策略与粒子群优化(Particle Swarm Optimization,PSO)算法相结合,提出一种基于BAS‑改进粒子群优化(Improved Particle... 【目的】针对在复杂装配场景中机器人的装配效率与装配质量相互制约的问题,将天牛须搜索(Beetle Antennae Search,BAS)策略与粒子群优化(Particle Swarm Optimization,PSO)算法相结合,提出一种基于BAS‑改进粒子群优化(Improved Particle Swarm Optimization,IPSO)算法(BAS‑IPSO算法)的最优轨迹求解方法。【方法】首先,采用4-5-4多项式插值方法建立轨迹规划模型,以时间-脉动冲击最优建立目标函数;其次,采用将动态学习因子与自适应权重相结合的方式改进传统的PSO算法,克服了PSO算法容易陷入局部最优与收敛速度慢的问题;最后,将BAS策略与IPSO算法相融合,增强全局搜索和局部搜索的并行计算能力。【结果】结果表明,与改进前相比,利用所提BAS‑IPSO算法得到的各关节的平均运行时间、冲击分别减少了26.5%和24.2%,说明了所提算法在装配机器人控制方面的有效性和优越性。 展开更多
关键词 装配机器人 轨迹规划 时间-脉动冲击最优 粒子群优化算法 天牛须搜索策略
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基于SSA-PSO混合算法的CO2泄漏源参数反演及地表扩散模拟 认领 引用
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作者 贾惠芹 张胜男 《科学技术与工程》 EI 北大核心 2026年第18期7961-7968,共8页
针对碳捕集、利用与封存(carbon capture,utilization,and storage,CCUS)项目中CO2泄漏源参数反演精度不足的问题,提出一种融合麻雀搜索算法(sparrow search algorithm,SSA)与粒子群算法(particle swarm optimization,PSO)的混合优... 针对碳捕集、利用与封存(carbon capture,utilization,and storage,CCUS)项目中CO2泄漏源参数反演精度不足的问题,提出一种融合麻雀搜索算法(sparrow search algorithm,SSA)与粒子群算法(particle swarm optimization,PSO)的混合优化方法。首先,基于高斯烟羽扩散模型构建泄漏正演模型,分析泄漏速率、风速和大气稳定度对地表CO2浓度分布的影响规律;其次,结合SSA的全局探索能力与PSO的局部开发优势,建立泄漏源位置与泄漏速率的反演模型。仿真结果表明:SSA-PSO算法反演的泄漏速率相对误差仅为0.01%,泄漏源位置坐标的相对误差均为0.10%,相较于PSO、SSA、GA及GA-PSO算法,其泄漏速率反演精度显著提升,最大相对误差降幅达27.99%,泄漏源定位精度最大提升22.3%。实例验证进一步表明,该算法在泄漏速率与位置反演中的相对误差分别为10.50%、4.00%和3.00%,均优于对比算法,且计算效率更高。研究表明,SSA-PSO混合算法能够实现CCUS项目CO2泄漏源的快速精准定位,为泄漏应急响应与风险防控提供有效技术支撑。 展开更多
关键词 CCUS CO2泄漏 高斯烟羽模型 粒子群算法 麻雀搜索算法
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工业园区循环流化床锅炉低热值煤掺烧优化 认领 引用
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作者 邓拓宇 董智鑫 《热力发电》 CAS CSCD 北大核心 2026年第3期19-27,共9页
在“双碳”目标的推动下,工业园区综合能源系统的建立,以及大规模可再生能源并网对火电机组配煤灵活性要求更高。工业园区循环流化床锅炉的配煤过程分为炉外与炉内两个阶段。在炉外配煤阶段,针对低热值煤掺烧,建立了最小煤质偏差模型,... 在“双碳”目标的推动下,工业园区综合能源系统的建立,以及大规模可再生能源并网对火电机组配煤灵活性要求更高。工业园区循环流化床锅炉的配煤过程分为炉外与炉内两个阶段。在炉外配煤阶段,针对低热值煤掺烧,建立了最小煤质偏差模型,采用基于混沌搜索的自适应变异粒子群算法将低热值煤掺配为符合锅炉煤质要求的入炉煤。在炉内配煤阶段,为保证负荷稳定的同时降低燃料成本,需动态调整不同负荷下入炉煤的掺烧比例。针对循环流化床锅炉炉内配煤问题,建立两阶段配煤优化模型:第一阶段根据化工厂周最大日负荷需求及典型光伏场景下机组出力需求,选择给煤机组合方式;第二阶段根据负荷需求以及给煤机优化结果建立负荷平衡约束,考虑炉内脱硫过程建立混煤含硫量约束,进行给煤量优化。对比春季典型辐照条件下不同配煤策略下的锅炉燃料成本,结果显示火电机组通过双煤种炉内掺烧可以使日燃烧成本降低43.6万元;对比煤仓改造前后掺烧三煤种燃料成本,结果显示改造后机组日燃料成本进一步降低。 展开更多
关键词 工业园区 低热值煤 粒子群优化算法 混沌搜索 煤仓改造
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基于雾计算的分布式网络优化方法 认领 引用
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作者 彭永倩 刘鸿 《高技术通讯》 CAS 北大核心 2026年第6期602-610,共9页
针对智慧茶园环境中分布式网络面临的高延迟与低能效问题,本文提出一种基于雾计算的分布式网络优化算法。本文建立了综合考虑计算能力、通信带宽与能量消耗的多层级网络模型,提出了一个优化框架,能够高效调度任务并进行计算迁移。在此... 针对智慧茶园环境中分布式网络面临的高延迟与低能效问题,本文提出一种基于雾计算的分布式网络优化算法。本文建立了综合考虑计算能力、通信带宽与能量消耗的多层级网络模型,提出了一个优化框架,能够高效调度任务并进行计算迁移。在此基础上,设计了融合自适应惯性权重、局部搜索、遗传交叉和约束惩罚机制的混合粒子群优化(hybrid particle swarm optimization,Hybrid-PSO)算法,该算法显著提升了全局搜索能力与解的可行性。实验结果表明,Hybrid-PSO算法在多目标优化问题上,能够有效减少系统总延迟、能耗和任务完成时间,收敛速度也显著优于传统的粒子群优化(particle swarm optimization,PSO)算法、遗传算法(genetic algorithm,GA)和PSO-GA算法。进一步分析表明,计算迁移与能量感知路由策略的引入,不仅优化了负载均衡,还显著延长了网络的寿命。 展开更多
关键词 雾计算 自适应惯性权重 局部搜索 遗传交叉 约束惩罚机制 改进粒子群算法
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基于PSO-SSA-LSTM模型的股价预测研究 认领 引用
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作者 张庭溢 林佳濠 《洛阳理工学院学报（自然科学版）》 2026年第1期74-80,91,共7页
为提高股票收盘价预测的精度,提出了一种PSO-SSA-LSTM算法模型。采用粒子群算法中速度与位置的特性改进麻雀搜索算法(Sparrow Search Algorithm,SSA),构建混合麻雀搜索算法(PSO-SSA);使用单峰和多峰测试函数验证PSO-SSA的性能,相比单一... 为提高股票收盘价预测的精度,提出了一种PSO-SSA-LSTM算法模型。采用粒子群算法中速度与位置的特性改进麻雀搜索算法(Sparrow Search Algorithm,SSA),构建混合麻雀搜索算法(PSO-SSA);使用单峰和多峰测试函数验证PSO-SSA的性能,相比单一的SSA,PSO-SSA的寻优能力和收敛速度有明显提升;利用PSO-SSA算法优化LSTM模型的两个隐含层神经元数量、学习率和迭代次数,并选取3支股票的时序数据训练PSO-SSA-LSTM模型。实验结果表明:相较于简单的单一模型,该模型的预测误差更小,具有更高的预测精度。 展开更多
关键词 股价预测 麻雀搜索算法 长短期记忆网络 粒子群优化
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基于分区和多子种群的多模态多目标粒子群优化算法 认领 引用 被引量：1
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作者 袁浩 黄文焘 +1 位作者 姜庆超 范勤勤 《控制工程》 CSCD 北大核心 2026年第4期744-756,共13页
寻找一个好的近似帕累托前沿和找到足够多的等效帕累托最优解是多模态多目标优化的两个重要目标。为此,提出一种基于分区搜索和多子种群的多模态多目标粒子群优化算法。首先,为了降低搜索难度,采用分区搜索对整个决策空间进行划分,获得... 寻找一个好的近似帕累托前沿和找到足够多的等效帕累托最优解是多模态多目标优化的两个重要目标。为此,提出一种基于分区搜索和多子种群的多模态多目标粒子群优化算法。首先,为了降低搜索难度,采用分区搜索对整个决策空间进行划分,获得多个子空间;然后,对种群进行聚类,将种群划分为不同的子种群;最后,在每个子空间中独立使用改进的多模态多目标粒子群优化算法进行搜索,从而找到更多的等效帕累托最优解。实验选取了22个多模态多目标测试函数对所提算法进行验证,并将其与7种较先进的多模态多目标进化算法进行对比。实验结果表明,相比于对比算法,所提算法可以在决策空间中找到更多的等效帕累托最优解,在目标空间中找到逼近性和多样性更好的近似帕累托前沿。 展开更多
关键词 多模态多目标优化 进化算法 分区搜索 子种群 粒子群优化算法
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混合粒子群优化算法求解带时间窗的车辆路径规划问题 认领 引用 被引量：2
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作者 周璐辉 岳雪芝 《计算机应用》 CSCD 北大核心 2026年第1期181-187,共7页
为了高效解决带时间窗的车辆路径规划问题(VRPTW),提出一种混合粒子群优化(HPSO)算法。该算法采用部分匹配交叉(PMX)替代传统粒子更新方式,结合最劣近邻粒子选择与轮盘赌机制增强多样性,并通过动态权重调整策略平衡全局探索与局部开发能... 为了高效解决带时间窗的车辆路径规划问题(VRPTW),提出一种混合粒子群优化(HPSO)算法。该算法采用部分匹配交叉(PMX)替代传统粒子更新方式,结合最劣近邻粒子选择与轮盘赌机制增强多样性,并通过动态权重调整策略平衡全局探索与局部开发能力;设计融合2-opt翻转、顺序插入和交换操作的变邻域搜索(VNS)优化解质量,并基于贪婪算法快速生成优质初始解。实验结果表明,在Solomon标准测试集上,HPSO算法在25和50个顾客的数据集中的69%的测试问题上的解与已知最优解差距保持在1%以内,在100个顾客的C类测试问题上几乎接近最优解结果,表明它在求解复杂VRPTW上的有效性和竞争力;在100个顾客的数据集上,相较于邻域综合学习粒子群(NCLPSO)算法,HPSO算法在RC102测试问题上标准差至少降低2.4%,在C101和R101测试问题上的收敛速度平均提升了41%(59%和23%)。HPSO算法通过多策略协同优化,能显著提升复杂VRPTW的求解精度、收敛效率与鲁棒性。 展开更多
关键词 粒子群优化算法 路径规划 时间窗 变邻域搜索 组合优化问题
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Enhanced Butterfly Optimization Algorithm for Large-Scale Optimization Problems 认领 引用 被引量：5
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作者 Yu Li Xiaomei Yu Jingsen Liu 《Journal of Bionic Engineering》 SCIE EI CSCD 2022年第2期554-570,共17页
To solve large-scale optimization problems,Fragrance coefficient and variant Particle Swarm local search Butterfly Optimization Algorithm(FPSBOA)is proposed.In the position update stage of Butterfly Optimization Algor... To solve large-scale optimization problems,Fragrance coefficient and variant Particle Swarm local search Butterfly Optimization Algorithm(FPSBOA)is proposed.In the position update stage of Butterfly Optimization Algorithm(BOA),the fragrance coefficient is designed to balance the exploration and exploitation of BOA.The variant particle swarm local search strategy is proposed to improve the local search ability of the current optimal butterfly and prevent the algorithm from falling into local optimality.192000-dimensional functions and 201000-dimensional CEC 2010 large-scale functions are used to verify FPSBOA for complex large-scale optimization problems.The experimental results are statistically analyzed by Friedman test and Wilcoxon rank-sum test.All attained results demonstrated that FPSBOA can better solve more challenging scientific and industrial real-world problems with thousands of variables.Finally,four mechanical engineering problems and one ten-dimensional process synthesis and design problem are applied to FPSBOA,which shows FPSBOA has the feasibility and effectiveness in real-world application problems. 展开更多
关键词 Butterfy optimization algorithm Fragrance coefcient Variant particle swarm local search Large-scale optimization problems Real-world application problems
基于LEA-RFR与MMOPSO的激光熔覆TaC/316L复合涂层工艺参数优化 认领 引用
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作者 陈泓西 杜彦斌 +1 位作者 梁强 马文生 《计算机集成制造系统》 EI CSCD 北大核心 2026年第6期2008-2017,共10页
为改善TaC/316L复合涂层表面质量,提出一种基于LEA-RFR(lotus effect optimization algorithm-random forest regression)与MMOPSO(multiple objective particle swarm optimization with multiple search strategies)的激光熔覆工艺参... 为改善TaC/316L复合涂层表面质量,提出一种基于LEA-RFR(lotus effect optimization algorithm-random forest regression)与MMOPSO(multiple objective particle swarm optimization with multiple search strategies)的激光熔覆工艺参数优化方法。以激光功率、扫描速度、送粉速率和搭接率作为实验因素,成形系数(η)、熔覆效率(φ)和表面平整度(τ)作为响应目标,进行四因素三水平的Box-Behnken设计。在304不锈钢表面利用激光熔覆技术制备TaC/316L复合涂层,构建LEA-RFR预测回归模型;通过MMOPSO算法对激光熔覆工艺参数进行多目标优化,确定最优的激光熔覆工艺参数组合。结果表明,最优工艺参数下复合涂层的成形系数为15.963,熔覆效率为96.10,表面平整度为0.847。优化预测结果和验证结果的误差分别为5.75%,1.36%和4.84%,均在10%以内;所提出的激光熔覆工艺参数优化方法可以显著改善TaC/316L复合涂层的表面质量。 展开更多
关键词 激光熔覆 参数优化 TaC/316L复合涂层 LEA-RFR MMOPSO
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