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An Adaptive fruit fly optimization algorithm for optimization Problems

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Journal of Applied Mathematics and Physics 2023年第11期11卷 3641-3650页

作者： L. Q. Zhang J. Xiong J. K. Liu College of Computers and Engineering Chongqing Three Gorges University Chongqing China College of Mathematics and Statistics Chongqing Three Gorges University Chongqing China

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.

关键词： Swarm Intelligent optimization algorithm fruit fly optimization algorithm Adaptive Step Local Optimum Convergence Speed

A Discrete fruit fly optimization algorithm for the Capacitated Vehicle Routing Problem

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A Discrete Fruit Fly Optimization Algorithm for the Capacita...

第36届中国控制会议

作者： LU Jia-wen WANG Ling JIANG En-da Department of Automation Tsinghua University

ISBN: (纸本)9781538629185

In this paper, a discrete fruit fly optimization algorithm（DFOA） is proposed for solving the capacitated vehicle routing problem（CVRP）. Firstly, a two-part discrete array is presented to represent the solution. Secondly, the initialization based on K-means is proposed to take full use of the location information of the customers. The customers in each cluster are allocated to one vehicle. Meanwhile, the repair operation is designed for not taking the capacity constraint into account in the initialization. Due to the characteristics of the CVRP, it may be effective to exchange or reallocate the customers between the neighbor routes in the graph. Thus, the smell-based search and vision-based search with the specific problem feature are designed. Moreover, the elimination mechanism and the simulated annealing based search are used to balance the exploration and the exploitation capabilities. In addition, the effect of parameter setting is investigated by using the Taguchi method of design-of-experiment to obtain the suitable values. Finally, numerical tests with the benchmark instances are carried out, which demonstrate the effectiveness of the proposed algorithm.

关键词： capacitated vehicle routing problem fruit fly optimization algorithm specific feature elimination mechanism

A Self-Adaptive Modified fruit fly optimization algorithm

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A Self-Adaptive Modified Fruit Fly Optimization Algorithm

第36届中国控制会议

作者： Yingtong Tan Mei Zhang Jinhui Zhu Haiming Liu School of Automation Science and Control Engineering South China University of Technology Engineering Research Center for Precision Electronic Manufacturing Equipment of Ministry of Education South China University of Technology School of Software Engineering South China University of Technology

ISBN: (纸本)9781538629185

fruit fly optimization algorithm（FOA） is inspired by imitating the foraging activity of fruit flies. Aiming at its inability to search the entire solution space, a Self-Adaptive Modified fruit fly optimization algorithm（SAMFOA） is proposed. Firstly, a new calculation formula of the smell concentration judgment value is designed. With the use of the new formula, the smell concentration judgment value is no longer restricted to be non-negative value so the algorithm is able to search both the positive and negative part of the solution space. Secondly, a self-adaptive osphresis foraging radius is introduced to enhance the ability to break away from local optimum. Experiments on 20 numerical benchmark functions show that the algorithm has good performance in terms of global searching ability, optimize accuracy and stability.

关键词： Swarm intelligence optimization fruit fly optimization algorithm Numerical optimization

Water Quality Evolution Mechanism Modeling and Estimation Based on fruit fly optimization algorithm and Particle Filter

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Water Quality Evolution Mechanism Modeling and Estimation Ba...

第三十九届中国控制会议

作者： Yuqin Zhou Zhiyao Zhao Xiaoyi Wang Qian Sun School of Artificial Intelligence Beijing Technology and Business University China Light Industry Key Laboratory of Industrial Internet and Big Data Beijing Technology and Business University

Water Quality Evolution Mechanism（WQEM） modeling and Water quality estimation are important technical means for water pollution prevention and control of lakes and reservoirs. However, existing classical WQEM models usually contain unknown parameters with empirical values range, which brings difficulty of estimation water quality changes of specific lakes and reservoirs to meet the accuracy requirements. Furthermore, water quality indicator is susceptible to natural factors and human factors, which makes the water system complex and nonliner, and enhances the difficulty of water quality estimation. Therefore,combining water quality mechanism, this paper proposes a fruit fly optimization algorithm（FFOA） based WQEM modeling method and studies a method of water quality estimation based on Particle Filter（PF） algorithm. First, a more comprehensive WQEM model is established to characterize the water quality mechanism of lakes and reservoirs. Then, combining observed data of water quality indicator and WQEM, the unknown parameters of a WQEM model are estimated by using FFOA. Finally,PF algorithm is used to estimate the water quality status. Simulation results show that the method can effectively estimate the unknown parameters of the WQEM model and estimate the water quality status.

关键词： Water quality evolution mechanism fruit fly optimization algorithm particle filter modeling estimation

An improved fruit fly optimization algorithm for solving high dimensional problems

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An improved fruit fly optimization algorithm for solving hig...

第37届中国控制会议

作者： Xiaohong Cheng Liding Chen Bugong Xu School of Automatic Science and Engineering South China University of Technology

As a new optimization algorithm,fruit fly optimization algorithm（FOA） attracts a lot of attentions.By analyzing the probability of FOA jumping out of the local optimal range,we verified that FOA is ineffective in solving complex optimization problems whose optimal solution is nonzero.In order to improve the performance of FOA,a Modified Global fruit fly optimization algorithm（MGFOA） is introduced in this paper.In MGFOA,a uniform mechanism to produce the candidate solution is used to improve the global searching ability,a self-adaptive way to control the flight range is adapted to increase the optimize accuracy,and a ladder growth way of population is introduced to imitate the detection behavior of fruit fly.The experiment on 12 benchmark functions shows that MGFOA is more effective and robust than basic FOA,Global Particle Swarm optimization algorithm（GPSO） and another improved FOA（LGMS-FOA）.

关键词： Meta-heuristic algorithms fruit fly optimization algorithm Global search

An Improved fruit fly optimization algorithm for Solving Multidimensional Knapsack Problem

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An Improved Fruit Fly Optimization Algorithm for Solving Mul...

第37届中国控制会议

作者： Hao Qian Qing-yong Zhang Wuhan university of technology

In this paper, a novel improved fruit fly optimization algorithm（IFOA） is proposed for solving the multidimensional knapsack problem（MKP）, which is characterized as high dimension and strong constraint. Initial swarms are generated according to the probability vector respectively. After the smell-based searching accomplishing, a repair operator granded on the pseudo-utility ratio, which is calculated by solving the dual problem of linear programming relaxion of MKP, is applied to guarantee the feasibility and enhance the quality of solutions. A swarm reduction strategy is used to balance the searching ability and convergence speed. Numerous tests and comparison with other algorithms based on two sets of benchmark problems demonstrate that IFOA is an efficient algorithm to solve MKP.

关键词： Multidimensional knapsack problem fruit fly optimization algorithm Swarm reduction

Prediction and determination of mildew grade in grain storage based on FOA-SVM algorithm

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Food Quality and Safety 2023年第1期7卷 145-151页

作者： Jianghao Yuan Fang Tang Zhihui Qi Huiyi Zhao Academy of National Food and Strategic Reserves Administration BeijingChina Hebei Agricultural University BaodingChina National Engineering Research Center of Grain Storage and Logistics BiingChina

Grain mildew is a significant hazard that causes food loss and poses a serious threat to human health when severe.Therefore,effective prediction and determination of mildew grade is essential for the prevention and control of mildew and global food security.In the present study,a model for predicting and determining the mildew grade of rice was constructed using logistic regression,back propagation neural network and GS-SVM(a grid search-based support vector machine algorithm)based on laboratory culture data and actual data from a granary,respectively.The results show that the GS-SVM model has a better prediction effect,but the model cannot automatically adjust the parameters and is more subjective,and the accuracy may decrease when the data set changes.Therefore,this paper establishes a new model for a support vector machine based on a fruit fly optimization algorithm(FOA-SVM),which can achieve automatic parameter search and automatically adjust its parameters to find the best result when the data set changes,with a strong ability of self-adjustment of parameters.In addition,the FOA-SVM converges quickly and the model is stable.The results of this study provide a technical method for early identification of mildew grade during grain storage,which is beneficial for the prevention and control of rice mildew during grain storage.

关键词： Identification of grain mildew support vector machine fruit fly optimization algorithm grain security

Performance Prediction of Switched Reluctance Motor using Improved Generalized Regression Neural Networks for Design optimization

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CES Transactions on Electrical Machines and Systems 2018年第4期2卷 371-376页

作者： Zhu Zhang Shenghua Rao Xiaoping Zhang School of Information and Electrical Engineering Hunan University of Science and TechnologyHunanChina Resources Exploration Equipment and Safety Technology Hunan University of Science and TechnologyHunanChina

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.

关键词： fruit fly optimization algorithm generalized regression neural networks switched reluctance motor

Stock Price Prediction based on Grey Relational Analysis and Support Vector Regression

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Stock Price Prediction based on Grey Relational Analysis and...

第30届中国控制与决策会议

作者： Xianxian Hou Shaohan Zhu Li Xia Gang Wu College of Computer Science and Engineering Northeastern University

Stock market data is extremely large and complicated. In stock prediction research, the selection of technical indicators has not a scientific theory as a guide. This paper proposes a novel method based on Grey Relational Analysis to select the technical indicators. Then make predictions by Support Vector Regression that optimized by improved fruit fly optimization algorithm. Firstly, the fruit fly optimization algorithm is improved by decreasing footstep and simulated annealing. Secondly, the improved fruit fly optimization algorithm is adopted to optimize the penalty factor c and the kernel function parameter g of the support vector regression. Finally, modeling and forecasting of the stock price with optimized support vector regression are conducted and some simulation experiments are carried out. The Support Vector Regression is adept at analyzing small size and multi-dimensional samples, so it is suitable for short-term stock prediction. By comparing with other three methods, the one this paper proposed could fast convergence and improve the accuracy of forecasting and is an efficient and feasible method.

关键词： prediction Grey Relational Analysis Support Vector Regression fruit fly optimization algorithm Simulated Annealing