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
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
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
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
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
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
果蝇优化算法(Fruit Fly Optimization Algorithm)是一种群体智能算法,其灵感来源于果蝇群体觅食行为。该算法通过模拟果蝇利用敏锐的嗅觉搜索食物源及利用视觉飞向食物位置的过程,实现对优化问题解空间的高效搜索。FOA算法具有原理简...果蝇优化算法(Fruit Fly Optimization Algorithm)是一种群体智能算法,其灵感来源于果蝇群体觅食行为。该算法通过模拟果蝇利用敏锐的嗅觉搜索食物源及利用视觉飞向食物位置的过程,实现对优化问题解空间的高效搜索。FOA算法具有原理简单、易于实现、参数较少等优点,在函数优化、机器学习、图像处理、工程设计等多个领域展现出了良好的应用潜力,为解决复杂的实际优化问题提供了一种有效的新途径,然而其在收敛速度和求解精度方面仍存在一定的改进空间,二维正态分布果蝇优化算法(Fruit Fly Optimization Algorithm based on Two-Dimensional Normal Distribution,简称2D-NDFOA)是一种结合了果蝇优化算法与正态分布特性的优化策略,提高果蝇群体的全局搜索能力。展开更多
针对BP-PID控制器的控制性能受参数初值影响大、易陷入局部极值、对噪声敏感,且低信噪比条件下控制稳定性差等问题,提出了一种基于改进果蝇优化算法(Improved Fruit Fly Optimization Algorithm,IFOA)和径向基神经网络-卡尔曼滤波(Radia...针对BP-PID控制器的控制性能受参数初值影响大、易陷入局部极值、对噪声敏感,且低信噪比条件下控制稳定性差等问题,提出了一种基于改进果蝇优化算法(Improved Fruit Fly Optimization Algorithm,IFOA)和径向基神经网络-卡尔曼滤波(Radial Basis Function Network-Kalman Filter,RBF-KF)的噪声稳健BP-PID控制方法。首先提出了一种IFOA随机搜索算法对BP-PID初值进行全局寻优,自动获得全局最优解,提升系统控制精度。然后利用所提RBF-KF对观测数据进行滤波平滑,降低量测和控制噪声对系统的影响,提升低信噪比条件下的控制稳定性。基于某智能车车速控制真实数据开展试验,结果表明,所提方法相对于传统方法控制精度提升超过50%,控制稳定性提升超过60%,并且在低信噪比条件下优势更加明显,更适合实际工程应用场景。展开更多
文摘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.
基金National Key Basic Research and Development Program of China(No.2013CB329503)National Natural Science Foundation of China(No.61174189)the Doctoral Program Foundation of Institutions of Higher Education of China(No.20130002110057)
文摘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.
文摘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.
基金supported by the National Natural Science Foundation of China under Grant Nos.71701156,71390331 and 71690242the Natural Science Foundation of Hubei Province of China under Grant No.2017CFB427+5 种基金Key Research Program of Frontier Sciences for Chinese Academy of Sciences under Grant No.QYZDB-SSW-SYS020Humanity and Social Science Youth Foundation of Ministry of Education of China under Grant No.16YJCZH056Hubei Province Department of Education Humanities and Social Sciences Research Project under Grant No.17Q034Open Funding of Center for Service Science and Engineering,Wuhan University of Science and Technology under Grant No.CSSE2017KA01Open Funding of Intelligent Information Processing and Real-time Industrial System under Grant No.2016znss18BYoung Incubation Program of Wuhan University of Science and Technology under Grant No.2016xz017 and 2017xz031
文摘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.
基金National Social Science Foundation of China(No.18AGL028)Social Science Foundation of the Higher Education Institutions Jiangsu Province,China(No.2018SJZDI070)Social Science Foundation of the Jiangsu Province,China(Nos.16ZZB004,17ZTB005)
文摘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.
文摘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.
文摘果蝇优化算法(Fruit Fly Optimization Algorithm)是一种群体智能算法,其灵感来源于果蝇群体觅食行为。该算法通过模拟果蝇利用敏锐的嗅觉搜索食物源及利用视觉飞向食物位置的过程,实现对优化问题解空间的高效搜索。FOA算法具有原理简单、易于实现、参数较少等优点,在函数优化、机器学习、图像处理、工程设计等多个领域展现出了良好的应用潜力,为解决复杂的实际优化问题提供了一种有效的新途径,然而其在收敛速度和求解精度方面仍存在一定的改进空间,二维正态分布果蝇优化算法(Fruit Fly Optimization Algorithm based on Two-Dimensional Normal Distribution,简称2D-NDFOA)是一种结合了果蝇优化算法与正态分布特性的优化策略,提高果蝇群体的全局搜索能力。
文摘针对BP-PID控制器的控制性能受参数初值影响大、易陷入局部极值、对噪声敏感,且低信噪比条件下控制稳定性差等问题,提出了一种基于改进果蝇优化算法(Improved Fruit Fly Optimization Algorithm,IFOA)和径向基神经网络-卡尔曼滤波(Radial Basis Function Network-Kalman Filter,RBF-KF)的噪声稳健BP-PID控制方法。首先提出了一种IFOA随机搜索算法对BP-PID初值进行全局寻优,自动获得全局最优解,提升系统控制精度。然后利用所提RBF-KF对观测数据进行滤波平滑,降低量测和控制噪声对系统的影响,提升低信噪比条件下的控制稳定性。基于某智能车车速控制真实数据开展试验,结果表明,所提方法相对于传统方法控制精度提升超过50%,控制稳定性提升超过60%,并且在低信噪比条件下优势更加明显,更适合实际工程应用场景。
