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Improved Fruit Fly Optimization Algorithm for Solving Lot-Streaming Flow-Shop Scheduling Problem 认领 引用 被引量：2
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作者 张鹏 王凌 《Journal of Donghua University(English Edition)》 EI CAS 2014年第2期165-170,共6页
An improved fruit fly optimization algorithm( iFOA) is proposed for solving the lot-streaming flow-shop scheduling problem( LSFSP) with equal-size sub-lots. In the proposed iFOA,a solution is encoded as two vectors to... An improved fruit fly optimization algorithm( iFOA) is proposed for solving the lot-streaming flow-shop scheduling problem( LSFSP) with equal-size sub-lots. In the proposed iFOA,a solution is encoded as two vectors to determine the splitting of jobs and the sequence of the sub-lots simultaneously. Based on the encoding scheme,three kinds of neighborhoods are developed for generating new solutions. To well balance the exploitation and exploration,two main search procedures are designed within the evolutionary search framework of the iFOA,including the neighborhood-based search( smell-vision-based search) and the global cooperation-based search. Finally,numerical testing results are provided,and the comparisons demonstrate the effectiveness of the proposed iFOA for solving the LSFSP. 展开更多
关键词 fruit fly optimization algorithm(FOA) lot-streaming flowshop scheduling job splitting neighborhood-based search cooperation-based search
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Seasonal Least Squares Support Vector Machine with Fruit Fly Optimization Algorithm in Electricity Consumption Forecasting 认领 引用
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作者 WANG Zilong XIA Chenxia 《Journal of Donghua University(English Edition)》 CAS 2019年第1期67-76,共10页
Electricity is the guarantee of economic development and daily life. Thus, accurate monthly electricity consumption forecasting can provide reliable guidance for power construction planning. In this paper, a hybrid mo... Electricity is the guarantee of economic development and daily life. Thus, accurate monthly electricity consumption forecasting can provide reliable guidance for power construction planning. In this paper, a hybrid model in combination of least squares support vector machine(LSSVM) model with fruit fly optimization algorithm(FOA) and the seasonal index adjustment is constructed to predict monthly electricity consumption. The monthly electricity consumption demonstrates a nonlinear characteristic and seasonal tendency. The LSSVM has a good fit for nonlinear data, so it has been widely applied to handling nonlinear time series prediction. However, there is no unified selection method for key parameters and no unified method to deal with the effect of seasonal tendency. Therefore, the FOA was hybridized with the LSSVM and the seasonal index adjustment to solve this problem. In order to evaluate the forecasting performance of hybrid model, two samples of monthly electricity consumption of China and the United States were employed, besides several different models were applied to forecast the two empirical time series. The results of the two samples all show that, for seasonal data, the adjusted model with seasonal indexes has better forecasting performance. The forecasting performance is better than the models without seasonal indexes. The fruit fly optimized LSSVM model outperforms other alternative models. In other words, the proposed hybrid model is a feasible method for the electricity consumption forecasting. 展开更多
关键词 forecasting fruit fly optimization algorithm(FOA) least squares support vector machine(LSSVM) seasonal index
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An Adaptive Fruit Fly Optimization Algorithm for Optimization Problems 认领 引用
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作者 L. Q. Zhang J. Xiong J. K. Liu 《Journal of Applied Mathematics and Physics》 2023年第11期3641-3650,共10页
In this paper, we present a new fruit fly optimization algorithm with the adaptive step for solving unconstrained optimization problems, which is able to avoid the slow convergence and the tendency to fall into local ... In this paper, we present a new fruit fly optimization algorithm with the adaptive step for solving unconstrained optimization problems, which is able to avoid the slow convergence and the tendency to fall into local optimum of the standard fruit fly optimization algorithm. By using the information of the iteration number and the maximum iteration number, the proposed algorithm uses the floor function to ensure that the fruit fly swarms adopt the large step search during the olfactory search stage which improves the search speed;in the visual search stage, the small step is used to effectively avoid local optimum. Finally, using commonly used benchmark testing functions, the proposed algorithm is compared with the standard fruit fly optimization algorithm with some fixed steps. The simulation experiment results show that the proposed algorithm can quickly approach the optimal solution in the olfactory search stage and accurately search in the visual search stage, demonstrating more effective performance. 展开更多
关键词 Swarm Intelligent Optimization Algorithm Fruit Fly Optimization Algorithm Adaptive Step Local Optimum Convergence Speed
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A Cooperative Fruit Fly Optimization Algorithm for Energy-Efficient Scheduling of Distributed Permutation Flow-Shop with Limited Buffers 认领 引用 被引量：1
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作者 Cai Zhao Lianghong Wu +3 位作者 Weihua Tan Cili Zuo Hongqiang Zhang Matthias Rätsch 《Tsinghua Science and Technology》 SCIE EI CAS CSCD 2026年第1期16-42,共27页
The scheduling problem of distributed permutation flow shop with limited buffer aiming at production efficiency measures has attracted widespread attention due to its closer alignment with real manufacturing environme... The scheduling problem of distributed permutation flow shop with limited buffer aiming at production efficiency measures has attracted widespread attention due to its closer alignment with real manufacturing environments.However,the energy efficiency metric is often ignored.The Energy-Efficient scheduling of Distributed Permutation Flow Shop Problem with Limited Buffer(EEDPFSP-LB)with the objectives of Makespan(Cmax)and Total Energy Consumption(TEC)is studied,and a Cooperative Fruit fly Optimization Algorithm(CFOA)is proposed in this paper.First,the critical path of EEDPFSP-LB is identified,and energy-efficient operation is applied to non-critical paths to reduce the system’s energy consumption.Second,five acceptance criteria for multi-objective optimization are introduced to enhance the diversity of the population.Third,to select a superior next-generation population,a new congestion calculation method is introduced to resolve the issue of indeterminate positional relationships among non-dominated solutions with identical crowding distances at the same dominance level.Finally,CFOA is extensively tested and compared with state-of-the-art algorithms across 360 instances,demonstrating CFOA’s strong competitiveness in solving EEDPFSP-LB. 展开更多
关键词 limited buffer energy efficient scheduling crowding distances Fruit fly Optimization Algorithm(FOA)
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Predicting Academic Performance Levels in Higher Education:A Data-Driven Enhanced Fruit Fly Optimizer Kernel Extreme Learning Machine Model 认领 引用 被引量：1
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作者 Zhengfei Ye Yongli Yang +1 位作者 Yi Chen Huiling Chen 《Journal of Bionic Engineering》 SCIE EI CSCD 2025年第4期1940-1962,共23页
Teacher–student relationships play a vital role in improving college students’academic performance and the quality of higher education.However,empirical studies with substantial data-driven insights remain limited.T... Teacher–student relationships play a vital role in improving college students’academic performance and the quality of higher education.However,empirical studies with substantial data-driven insights remain limited.To address this gap,this study collected 3278 questionnaires from seven universities across four provinces in China to analyze the key factors affecting college students’academic performance.A machine learning framework,CQFOA-KELM,was developed by enhancing the Fruit Fly Optimization Algorithm(FOA)with Covariance Matrix Adaptation Evolution Strategy(CMAES)and Quadratic Approximation(QA).CQFOA significantly improved population diversity and was validated on the IEEE CEC2017 benchmark functions.The CQFOA-KELM model achieved an accuracy of 98.15%and a sensitivity of 98.53%in predicting college students’academic performance.Additionally,it effectively identified the key factors influencing academic performance through the feature selection process. 展开更多
关键词 Academic achievement Machine learning Teacher-student relationships Swarm intelligence algorithms Fruit fly optimization algorithm
Binary Fruit Fly Swarm Algorithms for the Set Covering Problem 认领 引用 被引量：2
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作者 Broderick Crawford Ricardo Soto +7 位作者 Hanns de la Fuente Mella Claudio Elortegui Wenceslao Palma Claudio Torres-Rojas Claudia Vasconcellos-Gaete Marcelo Becerra Javier Pena Sanjay Misra 《Computers, Materials & Continua》 SCIE EI 2022年第6期4295-4318,共24页
Currently,the industry is experiencing an exponential increase in dealing with binary-based combinatorial problems.In this sense,metaheuristics have been a common trend in the field in order to design approaches to so... Currently,the industry is experiencing an exponential increase in dealing with binary-based combinatorial problems.In this sense,metaheuristics have been a common trend in the field in order to design approaches to solve them successfully.Thus,a well-known strategy consists in the use of algorithms based on discrete swarms transformed to perform in binary environments.Following the No Free Lunch theorem,we are interested in testing the performance of the Fruit Fly Algorithm,this is a bio-inspired metaheuristic for deducing global optimization in continuous spaces,based on the foraging behavior of the fruit fly,which usually has much better sensory perception of smell and vision than any other species.On the other hand,the Set Coverage Problem is a well-known NP-hard problem with many practical applications,including production line balancing,utility installation,and crew scheduling in railroad and mass transit companies.In this paper,we propose different binarization methods for the Fruit Fly Algorithm,using Sshaped and V-shaped transfer functions and various discretization methods to make the algorithm work in a binary search space.We are motivated with this approach,because in this way we can deliver to future researchers interested in this area,a way to be able to work with continuous metaheuristics in binary domains.This new approach was tested on benchmark instances of the Set Coverage Problem and the computational results show that the proposed algorithm is robust enough to produce good results with low computational cost. 展开更多
关键词 Set covering problem fruit fly swarm algorithm metaheuristics binarization methods combinatorial optimization problem
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Temperature Prediction of Laser Directed Energy Deposition Based on ASSFOA-GRNN Model 认领 引用
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作者 Li Dianqi Chai Yuanxin +1 位作者 Miao Liguo Tang Jinghu 《稀有金属材料与工程》 SCIE EI CAS CSCD 北大核心 2025年第10期2470-2482,共13页
To address the issues of low accuracy,long time consumption,and high cost of the traditional temperature prediction methods for laser directed energy deposition(LDED),a machine learning model combined with numerical s... To address the issues of low accuracy,long time consumption,and high cost of the traditional temperature prediction methods for laser directed energy deposition(LDED),a machine learning model combined with numerical simulation was proposed to predict the temperature during LDED.A finite element(FE)thermal analysis model was established.The model's accuracy was verified through in-situ monitoring experiments,and a basic database for the predictive model was obtained based on FE simulations.Temperature prediction was performed using a generalized regression neural network(GRNN).To reduce dependence on human experience during GRNN parameter tuning and to enhance model prediction performance,an improved adaptive step-size fruit fly optimization algorithm(ASSFOA)was introduced.Finally,the prediction performance of ASSFOA-GRNN model was compared with that of back-propagation neural network model,GRNN model,and fruit fly optimization algorithm(FOA)-GRNN model.The evaluation metrics included the root mean square error(RMSE),mean absolute error(MAE),coefficient of determination(R2),training time,and prediction time.Results show that the ASSFOA-GRNN model exhibits optimal performance regarding RMSE,MAE,and R2 indexes.Although its prediction efficiency is slightly lower than that of the FOA-GRNN model,its prediction accuracy is significantly better than that of the other models.This proposed method can be used for temperature prediction in LDED process and also provide a reference for similar methods. 展开更多
关键词 laser directed energy deposition temperature prediction FE simulation generalized regression neural network fruit fly optimization algorithm
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An Inverse Power Generation Mechanism Based Fruit Fly Algorithm for Function Optimization 认领 引用 被引量：4
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作者 LIU Ao DENG Xudong +2 位作者 REN Liang LIU Ying LIU Bo 《Journal of Systems Science & Complexity》 SCIE EI CSCD 2019年第2期634-656,共23页
As a novel population-based optimization algorithm, fruit fly optimization(FFO) algorithm is inspired by the foraging behavior of fruit flies and possesses the advantages of simple search operations and easy implement... As a novel population-based optimization algorithm, fruit fly optimization(FFO) algorithm is inspired by the foraging behavior of fruit flies and possesses the advantages of simple search operations and easy implementation. Just like most population-based evolutionary algorithms, the basic FFO also suffers from being trapped in local optima for function optimization due to premature convergence.In this paper, an improved FFO, named IPGS-FFO, is proposed in which two novel strategies are incorporated into the conventional FFO. Specifically, a smell sensitivity parameter together with an inverse power generation mechanism(IPGS) is introduced to enhance local exploitation. Moreover,a dynamic shrinking search radius strategy is incorporated so as to enhance the global exploration over search space by adaptively adjusting the searching area in the problem domain. The statistical performance of FFO, the proposed IPGS-FFO, three state-of-the-art FFO variants, and six metaheuristics are tested on twenty-six well-known unimodal and multimodal benchmark functions with dimension 30, respectively. Experimental results and comparisons show that the proposed IPGS-FFO achieves better performance than three FFO variants and competitive performance against six other meta-heuristics in terms of the solution accuracy and convergence rate. 展开更多
关键词 Evolutionary algorithms fruit fly optimization function optimization meta-heuristics
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An improved fruit fly optimization algorithm for solving traveling salesman problem 认领 引用 被引量：9
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作者 Lan HUANG Gui-chao WANG +1 位作者 Tian BAI Zhe WANG 《Frontiers of Information Technology & Electronic Engineering》 SCIE EI CSCD 2017年第10期1525-1533,共9页
The traveling salesman problem(TSP), a typical non-deterministic polynomial(NP) hard problem, has been used in many engineering applications. As a new swarm-intelligence optimization algorithm, the fruit fly optimizat... The traveling salesman problem(TSP), a typical non-deterministic polynomial(NP) hard problem, has been used in many engineering applications. As a new swarm-intelligence optimization algorithm, the fruit fly optimization algorithm(FOA) is used to solve TSP, since it has the advantages of being easy to understand and having a simple implementation. However, it has problems, including a slow convergence rate for the algorithm, easily falling into the local optimum, and an insufficient optimization precision. To address TSP effectively, three improvements are proposed in this paper to improve FOA. First, the vision search process is reinforced in the foraging behavior of fruit flies to improve the convergence rate of FOA. Second, an elimination mechanism is added to FOA to increase the diversity. Third, a reverse operator and a multiplication operator are proposed. They are performed on the solution sequence in the fruit fly's smell search and vision search processes, respectively. In the experiment, 10 benchmarks selected from TSPLIB are tested. The results show that the improved FOA outperforms other alternatives in terms of the convergence rate and precision. 展开更多
关键词 Traveling salesman problem Fruit fly optimization algorithm Elimination mechanism Vision search Operator
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基于改进的FOA-SVM导水裂隙带高度预测研究 认领 引用 被引量：38
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作者 张宏伟 朱志洁 +1 位作者 霍丙杰 宋卫华 《中国安全科学学报》 CAS CSCD 北大核心 2013年第10期9-14,共6页
为准确预测导水裂隙带高度,提出一种新的预测方法。在对部分矿井的导水裂隙带发育情况统计分析的基础上,应用支持向量机(SVM)建立导水裂隙带高度预计模型。采用改进的果蝇优化算法(FOA)优化参数,避免SVM的参数选取对预测准确性的影响。... 为准确预测导水裂隙带高度,提出一种新的预测方法。在对部分矿井的导水裂隙带发育情况统计分析的基础上,应用支持向量机(SVM)建立导水裂隙带高度预计模型。采用改进的果蝇优化算法(FOA)优化参数,避免SVM的参数选取对预测准确性的影响。选取统计样本,检验该模型的预测性能。并将该模型的预测结果与未改进的3种方法(FOA优化的SVM、遗传算法(GA)优化的SVM和粒子群算法(PSO)优化的SVM模型)分别进行比较。结果表明:改进的FOA-SVM模型有较高的预测精度和较强的泛化能力,能够相对准确、高效地预测导水裂隙带高度。 展开更多
关键词 导水裂隙带 支持向量机(SVM) 果蝇优化算法(FOA) 回归 仿真预测
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基于改进FOA-SVM的矿井火灾图像识别 认领 引用 被引量：18
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作者 苗续芝 陈伟 +2 位作者 毕方明 房卫东 张武雄 《计算机工程》 CAS CSCD 北大核心 2019年第4期267-274,共8页
为解决矿井下传统火灾识别方法准确率较低的问题,提出一种基于改进果蝇优化算法(FOA)-支持向量机(SVM)的火灾图像识别算法。利用YCrCb颜色空间对捕获的图像进行分割,根据早期的火灾图像特征从图像序列中提取多个火灾特征值。用基于分群... 为解决矿井下传统火灾识别方法准确率较低的问题,提出一种基于改进果蝇优化算法(FOA)-支持向量机(SVM)的火灾图像识别算法。利用YCrCb颜色空间对捕获的图像进行分割,根据早期的火灾图像特征从图像序列中提取多个火灾特征值。用基于分群体融合的改进FOA算法搜索SVM最优核参数和惩罚因子,将提取的火灾图像特征值作为SVM的输入对样本数据进行分类。实验结果表明,采用该方法对矿井火灾进行识别时准确率达97.2%,其分类效果显著优于FOA方法、粒子群优化算法等。 展开更多
关键词 矿井火灾 火灾特征 图像处理 支持向量机 果蝇优化算法
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基于改进深度稀疏自编码器及FOA-ELM的电力负荷预测 认领 引用 被引量：32
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作者 张淑清 要俊波 +2 位作者 张立国 姜安琦 穆勇 《仪器仪表学报》 EI CAS CSCD 北大核心 2020年第4期49-57,共9页
智能电网的发展使得电网获取的数据逐渐增多,为了从多维大数据中获取有用信息并对短期内电力负荷进行准确的预测,提出了一种基于改进的深度稀疏自编码器(IDSAE)降维及果蝇优化算法(FOA)优化极限学习机(ELM)的短期电力负荷预测方法。将L... 智能电网的发展使得电网获取的数据逐渐增多,为了从多维大数据中获取有用信息并对短期内电力负荷进行准确的预测,提出了一种基于改进的深度稀疏自编码器(IDSAE)降维及果蝇优化算法(FOA)优化极限学习机(ELM)的短期电力负荷预测方法。将L1正则化加入到深度稀疏自编码器(DSAE)中能够诱导出更好的稀疏性,用IDSAE对影响电力负荷预测精度的高维数据进行特征降维,消除了指标间的多重共线性,实现高维数据向低维空间的压缩编码。采用FOA优化算法优化ELM的权值和阈值,得到最优值,能够克服因极限学习机随机选择权值和阈值导致预测精度低的缺点。首先将气象因素通过IDSAE降维,得到稀疏后的综合气象因素特征指标,协同电力负荷数据作为FOA优化的ELM预测模型的输入向量进行电力负荷预测。通过与DSAE-FOAELM、DSAE-ELM和IDSAE-ELM等模型的对比实验,证明了提出的预测模型能有效提高预测精度,经计算得出预测精度提升大约8%。 展开更多
关键词 短期电力负荷预测 深度稀疏自编码器(DSAE) 降维 果蝇优化算法 极限学习机
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基于IFOA的MEMS加速度计无转台标定 认领 引用 被引量：5
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作者 戴洪德 郑伟伟 +2 位作者 郑百东 戴邵武 王瑞 《北京航空航天大学学报》 EI CAS CSCD 北大核心 2021年第10期1959-1968,共10页
为提高微机电系统(MEMS)加速度计的标定效率并降低对高精度转台的依赖,提出一种基于改进果蝇优化算法(IFOA)的MEMS加速度计无转台标定方法。首先,根据模观测标定法原理将加速度计标定问题转化为非线性函数优化问题。然后,针对经典果蝇... 为提高微机电系统(MEMS)加速度计的标定效率并降低对高精度转台的依赖,提出一种基于改进果蝇优化算法(IFOA)的MEMS加速度计无转台标定方法。首先,根据模观测标定法原理将加速度计标定问题转化为非线性函数优化问题。然后,针对经典果蝇优化算法存在的只能搜索正参数及搜索步长固定的不足,对味道浓度判定值及搜索步长进行改进,使改进后的算法具有全局参数搜索及可变步长2种性能,并利用Rosenbrock函数进行测试,结果表明,IFOA相比于经典果蝇优化算法具有全局参数寻优范围及更高的寻优精度。最后,将IFOA应用于求解加速度计待标定参数的非线性函数优化问题,并将结果与牛顿迭代法和粒子群优化(PSO)算法进行对比。仿真结果表明:IFOA在求解精度方面比牛顿迭代法提高了1~3个数量级;在运行稳定性方面比牛顿迭代法和PSO算法分别提高了30%和34%,在运行时间方面分别减小了15.2%和43.6%;在加速度计无转台标定方面具有良好的应用价值。 展开更多
关键词 微机电系统(MEMS) 加速度计 标定 模观测法 果蝇优化算法(FOA)
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内腐蚀海底管道剩余强度的FOA-GRNN模型 认领 引用 被引量：12
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作者 毕傲睿 骆正山 +1 位作者 宋莹莹 张新生 《中国安全科学学报》 CAS CSCD 北大核心 2020年第6期78-83,共6页
为探究内腐蚀海底管道剩余强度,保证管道安全运营,基于管道壁厚、直径,腐蚀深度、长度、宽度和极限抗拉强度等影响因素,提出果蝇优化算法(FOA)优化广义回归神经网络(GRNN)的剩余强度计算方法,应用GRNN构建剩余强度预测模型;采用FOA优化... 为探究内腐蚀海底管道剩余强度,保证管道安全运营,基于管道壁厚、直径,腐蚀深度、长度、宽度和极限抗拉强度等影响因素,提出果蝇优化算法(FOA)优化广义回归神经网络(GRNN)的剩余强度计算方法,应用GRNN构建剩余强度预测模型;采用FOA优化模型,人为设置光滑因子的负面影响;通过有限元模拟生成影响因素和剩余强度数据库,并采用FOA-GRNN模型训练和预测;以巴西国家石油研究中心的极限强度爆破试验数据为例,分析验证预测模型。结果表明:FOAGRNN模型对有限元模拟数据的剩余强度预测平均相对误差(ARE)为16.53%,对试验数据预测ARE为7.81%,预测结果合理、准确。 展开更多
关键词 内腐蚀海底管道 剩余强度 果蝇优化算法(FOA) 广义回归神经网络(GRNN) 有限元
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基于FOA-RBF神经网络的机械类实验课程目标达成度评价 认领 引用 被引量：6
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作者 宋鹍 刘立堃 +2 位作者 杨涛 杨瑜 路世青 《实验室研究与探索》 CAS 北大核心 2022年第5期216-221,257,共6页
在本科机械类专业教学体系中,面向解决复杂工程问题实验课程的课程目标达成度评价是工程教育认证中该类专业毕业要求、培养目标达成度评价的基石。针对重庆理工大学机械工程学院的机械类基础实验课程,构建了实验课程目标达成度评价体系... 在本科机械类专业教学体系中,面向解决复杂工程问题实验课程的课程目标达成度评价是工程教育认证中该类专业毕业要求、培养目标达成度评价的基石。针对重庆理工大学机械工程学院的机械类基础实验课程,构建了实验课程目标达成度评价体系,提出了一种基于果蝇优化算法(FOA)的径向基函数(RBF)神经网络评价模型,以学生实验项目成绩为输入,课程目标达成度为输出,通过实际学生样本对该评价模型及3种经典神经网络模型进行对比验证。结果表明,FOA-RBF评价模型的平均误差、均方差和最大相对误差均为最优,且其相对误差在49%以内,泛化能力强,对专业实验课程目标达成度预估效果理想,为专业工程教育认证工作提供了科学、量化的评估数据支撑。 展开更多
关键词 实验课程 课程目标 达成度评价 果蝇优化算法 径向基函数 神经网络
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基于因果时序网络的FOA-GRNN电网故障诊断方法 认领 引用 被引量：6
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作者 薛毓强 李宗辉 《电力系统及其自动化学报》 CSCD 北大核心 2014年第11期72-77,共6页
针对电网故障诊断过程常受到警报信息畸变以及保护设备误动或拒动等不确定因素的影响而导致误诊断的问题,提出了基于时序网络的果蝇优化算法-广义回归神经网络电网故障诊断方法。利用系统保护与设备之间存在的时序逻辑关系,对获得的电... 针对电网故障诊断过程常受到警报信息畸变以及保护设备误动或拒动等不确定因素的影响而导致误诊断的问题,提出了基于时序网络的果蝇优化算法-广义回归神经网络电网故障诊断方法。利用系统保护与设备之间存在的时序逻辑关系,对获得的电网故障警报信息甄别后再进行故障诊断。算例分析及测试结果说明,所提方法能够准确地实现电网的故障诊断,并适应电网拓扑结构的变化。 展开更多
关键词 电力系统 因果网络 神经网络 果蝇优化算法 广义回归神经网络
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Performance Prediction of Switched Reluctance Motor using Improved Generalized Regression Neural Networks for Design Optimization 认领 引用 被引量：11
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作者 Zhu Zhang Shenghua Rao Xiaoping Zhang 《CES Transactions on Electrical Machines and Systems》 CSCD 2018年第4期371-376,共6页
Since practical mathematical model for the design optimization of switched reluctance motor(SRM)is difficult to derive because of the strong nonlinearity,precise prediction of electromagnetic characteristics is of gre... Since practical mathematical model for the design optimization of switched reluctance motor(SRM)is difficult to derive because of the strong nonlinearity,precise prediction of electromagnetic characteristics is of great importance during the optimization procedure.In this paper,an improved generalized regression neural network(GRNN)optimized by fruit fly optimization algorithm(FOA)is proposed for the modeling of SRM that represent the relationship of torque ripple and efficiency with the optimization variables,stator pole arc,rotor pole arc and rotor yoke height.Finite element parametric analysis technology is used to obtain the sample data for GRNN training and verification.Comprehensive comparisons and analysis among back propagation neural network(BPNN),radial basis function neural network(RBFNN),extreme learning machine(ELM)and GRNN is made to test the effectiveness and superiority of FOA-GRNN. 展开更多
关键词 Fruit fly optimization algorithm generalized regression neural networks switched reluctance motor
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基于CEEMD-PSR-FOA-LSSVM的短期风电功率预测 认领 引用 被引量：4
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作者 田丽 凤志民 刘世林 《可再生能源》 CAS 北大核心 2016年第11期1632-1638,共7页
为提高短期风电功率预测精度,针对风电功率波动性大、非周期性和非线性强的特点,提出基于互补集合经验模态分解(complementary ensemble empirical mode decomposition,CEEMD)-相空间重构(phase space reconstruction,PSR)-果蝇优化算法... 为提高短期风电功率预测精度,针对风电功率波动性大、非周期性和非线性强的特点,提出基于互补集合经验模态分解(complementary ensemble empirical mode decomposition,CEEMD)-相空间重构(phase space reconstruction,PSR)-果蝇优化算法(fruit fly optimization algorithm,FOA)-最小二乘支持向量机(least squares support vector machine,LSSVM)的组合预测方法。首先,运用CEEMD算法把风电功率序列分解为若干个分量,并用PSR算法来确定LSSVM建模过程中各个分量的输入和输出;然后,采用FOA算法优化LSSVM建模中的参数,并用训练好的LSSVM对各个分量进行单独预测;最后,用某风电场的实测数据对该组合预测方法进行验证。结果表明,与单独的LSSVM方法和FOA-LSSVM方法预测结果相比,建立的组合模型预测方法精度更高,对风电功率的短期预测更为有效和适用。 展开更多
关键词 短期风电功率预测 互补集合经验模态分解 相空间重构 果蝇优化算法 最小二乘支持向量机
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LVFOA优化的GRNN在财务预警中的应用 认领 引用 被引量：2
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作者 赵男男 王艺星 王英博 《计算机系统应用》 2017年第6期17-25,共9页
针对企业财务数据复杂、非线性等特点,提出了一种基于混沌变步长果蝇算法(LVFOA)优化广义回归神经网络(GRNN)的财务预警模型.首先引入Logistic混沌映射修正FOA的初始值,然后在最优初始值的基础上修正FOA步长为动态步长,寻找最优Spread值... 针对企业财务数据复杂、非线性等特点,提出了一种基于混沌变步长果蝇算法(LVFOA)优化广义回归神经网络(GRNN)的财务预警模型.首先引入Logistic混沌映射修正FOA的初始值,然后在最优初始值的基础上修正FOA步长为动态步长,寻找最优Spread值,最后对预测数据进行分析,选取有代表性的指标.改进后的果蝇算法显示了更好的全局优化和快速收敛能力,提高了GRNN的预测精度.仿真结果表明,相对于GRNN模型和FOA-GRNN模型,LVFOA-GRNN模型提高了预警准确率,与财务数据的拟合度更高. 展开更多
关键词 财务预警 果蝇算法 广义回归神经网络 光滑参数 参数优化
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基于LLE-FOA-SVR模型的煤矿突水预测 认领 引用 被引量：1
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作者 唐守锋 史可 张晔 《传感器与微系统》 CSCD 北大核心 2023年第4期148-151,共4页
针对煤矿突水预测精度低、训练速度慢的问题,提出基于局部线性嵌入(LLE)—果蝇优化算法(FOA)—支持向量回归(SVR)的煤矿突水预测模型。首先,利用LLE在非线性数据特征提取方面的优势,提取煤矿突水影响因素原始数据的本质特征,形成重构因... 针对煤矿突水预测精度低、训练速度慢的问题,提出基于局部线性嵌入(LLE)—果蝇优化算法(FOA)—支持向量回归(SVR)的煤矿突水预测模型。首先,利用LLE在非线性数据特征提取方面的优势,提取煤矿突水影响因素原始数据的本质特征,形成重构因子,减少数据间的冗余信息和噪声。然后,利用FOA对SVR的参数进行迭代优化,并将最优参数代入SVR中,以解决传统SVR参数优化困难的问题。最后,结合实例并将LLE-FOA-SVR模型的预测结果与反向传播(BP)、SVR、LLE-SVR模型的预测结果进行对比。实验结果表明:该模型的预测精度高于其他3种模型,预测精度可达90%,且建模时间和运算时间更短。 展开更多
关键词 煤矿突水 局部线性嵌入 支持向量回归机 果蝇优化算法 LLE-FOA-SVR模型
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