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
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
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
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
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(C_(max))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
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
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
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
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(R^(2)),training time,and prediction time.Results show that the ASSFOA-GRNN model exhibits optimal performance regarding RMSE,MAE,and R^(2) 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
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.
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.
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 Key Research and Development Program of China(No.2023YFC3011100)the National Natural Science Foundation of China(No.62373146)+3 种基金the Natural Science Foundation of Hunan Province(No.2022JJ30265)the Young Talent of Lifting Engineering for Science and Technology in Hunan Province(No.2022TJ-Q03)the Outstanding Youth Project of Education Department of Hunan Province(No.22B0476)the Key Project of Education Department of Hunan Province of China(No.23A0382).
文摘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(C_(max))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.
基金supported by the National Natural Science Foundation of China(Nos.61472159 and 61373051)
文摘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.
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This work was supported in part by the National Natural Science Foundation of China under Grant61503132 and Grant51477047the Hunan Provincial Natural Science Foundation of China under Grant2015JJ5029.
文摘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.
基金National Key Research and Development Program of China(2022YFB4602200)。
文摘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(R^(2)),training time,and prediction time.Results show that the ASSFOA-GRNN model exhibits optimal performance regarding RMSE,MAE,and R^(2) 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.
