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A bilevel improved fruit fly optimization algorithm for the nonlinear bilevel programming problem

KNOWLEDGE-BASED SYSTEMS 2017年第Dec.15期138卷 113-123页

作者： Wang, Guangmin Ma, Linmao Chen, Jiawei China Univ Geosci Sch Econ & Management Wuhan 430074 Peoples R China Southwest Univ Sch Math & Stat Chongqing 400715 Peoples R China

This paper proposes a bilevel improved fruit fly optimization algorithm (BIFOA) to address the nonlinear bilevel programming problem (NBLPP). Considering the hierarchical nature of the problem, this algorithm is constructed by combining two sole improved fruit fly optimization algorithms. In the proposed algorithm, the lower level problem is treated as a common nonlinear programming problem rather than being transformed into the constraints of the upper level problem. Eventually, 10 test problems are selected involving low-dimensional and high-dimensional problems to evaluate the performance of BIFOA from the aspects of the accuracy and stability of the solutions. The results of extensive numerical experiments and comparisons reveal that the proposed algorithm outperforms the compared algorithms and is significantly better than the methods presented in the literature;the proposed algorithm is an effective and comparable algorithm for NBLPP. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Bilevel programming problem Nonlinear bilevel programming problem fruit fly optimization algorithm improved fruit fly optimization algorithm

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Echo state network based on improved fruit fly optimization algorithm for chaotic time series prediction

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JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2020年第7期13卷 3483-3502页

作者： Tian, Zhongda Shenyang Univ Technol Coll Informat Sci & Engn Shenyang 110870 Peoples R China

Chaos is a common phenomenon in nature and society. Chaotic system affects many fields. It is of great significance to find out the regularity of chaotic time series from chaotic system. Chaotic system has extremely complex dynamic characteristics and unpredictability. The traditional prediction methods for chaotic time series have some problems, such as low accuracy, slow convergence speed and complex model structure. In this paper, an echo state network prediction method based on improved fruit fly optimization algorithm for chaotic time series is proposed. The phase space reconstruction is introduced for the prediction of chaotic time series. The C-C method is used to determine the delay time. The embedding dimension is obtained by the G-P method. After reconstructing the phase space of the chaotic time series, an improved echo state network is proposed as the prediction model. In order to improve the prediction accuracy, an improved fruit fly optimization algorithm is proposed to optimize the parameters of the prediction model. Three typical chaotic time series, including Lorenz, Mackey-Glass, and short-term wind speed, are selected as simulation objects. The simulation results show that the prediction method proposed in this paper has good prediction indicators. At the same time, the results of the reliability and Pearson's test also show the better predictive effect.

关键词： Chaotic time series Prediction Echo state network improved fruit fly optimization algorithm

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Solution method for ill-conditioned problems based on a new improved fruit fly optimization algorithm

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JOURNAL OF APPLIED GEODESY 2020年第1期14卷 55-64页

作者： Fan, Qian Meng, Xiaolin Xu, Chengquan Yu, Jiayong Fuzhou Univ Coll Civil Engn Fuzhou 350116 Fujian Peoples R China Univ Nottingham Fac Engn Nottingham NG7 2TU England Minjiang Univ Ocean Coll Fuzhou 350108 Fujian Peoples R China Hunan Univ Coll Civil Engn Changsha 410082 Hunan Peoples R China

Based on deeply analysis for optimization process of basic fruit fly optimization algorithm (FOA), a new improved FOA (IFOA) method is proposed, which modifies random search direction, increases the adjustment coefficient of search radius, and establishes a multi-sub-population solution mechanism. The proposed method can process the nonlinear objective function that has non-zero and non-negative extreme points. In the paper, IFOA method is applied to ill-conditioned problem solution in the field of surveying data processing. Application of the proposed method on two practical examples show that solution accuracy of IFOA is superior to that of three well-known intelligent optimization algorithms and two existing improved FOA methods, and it is also better than truncated singular value decomposition method and ridge estimation method. In addition, compared with intelligent search method represented by particle swarm optimization algorithm, The IFOA method has the advantages of less parameter settings, simple optimization process and easy program implementation. So, IFOA method is feasible, effective and practical in solving ill-conditioned problems.

关键词： ill-conditioned problem solution improved fruit fly optimization algorithm random search direction multi-sub-population particle swarm optimization

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Economic Dispatch of Power System based on improved fruit fly optimization algorithm 14

Economic Dispatch of Power System based on Improved Fruit Fl...

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14th IEEE Conference on Industrial Electronics and Applications (ICIEA)

作者： Liang, Jiaxiang Zhang, Huizhi Wang, Kaiyan Jia, Rong Xian Univ Technol Elect Power Res Inst Xian Shaanxi Peoples R China Xian Univ Technol Xian Key Lab Intelligent Energy Xian Shaanxi Peoples R China

ISBN: (纸本)9781538694909

For solving the multidimensional, nonlinear, non convex and multi constrained economic dispatch (ED) problem, an improved fruit fly optimization algorithm is proposed. Due to the shortcomings of the traditional fruit fly optimization algorithm, this paper modified the FOA in some extent. In order to take into account both global and local search capability, a new search radius determining mechanism is designed to improve the search ability of the algorithm. For making the candidate solution be produced uniformly in the solution space, a new mechanism for determining the individual location of the fruit fly and the determination of the taste concentration is established, which makes the generation mechanism of the candidate solution more reasonable. Using penalty function to deal with constraint conditions will involve a large number of penalty factors which are difficult to choose, therefore, this paper designs different heuristic processing strategies for different dynamic and static test systems to handle constraints. Simulation results show that the improved fruit fly optimization algorithm has more advantages than other algorithms in solving the ED problem.

关键词： electric power system economic dispatch improved fruit fly optimization algorithm search capability candidate solution Heuristic processing strategy

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Droop Control optimization Strategy for Parallel Inverters in a Microgrid Based on an improved Population Division fruit fly algorithm

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IEEE ACCESS 2022年 10卷 24877-24894页

作者： Tao, Xinkun Zhang, Li Wang, Fuzhong Henan Polytech Univ Sch Elect Engn & Automat Jiaozuo 454003 Henan Peoples R China

When the microgrid topology changes, the traditional droop control strategy affects the dynamic performance and steady-state accuracy of the inverter. To this end, this paper is based on an improved population division fruit fly algorithm. An optimization strategy for grid-connected inverter droop control is proposed in this paper, and then, the PI parameters of microgrid droop control are optimized in real time. This strategy divides the fruit fly population into three zones according to the inverter output and then automatically updates the multistrategy mode according to the difference in fruit fly performance in each zone. Among them, in zone I, a local fine search is conducted to ensure that the population does not degenerate;in zone II, adaptive adjustment is performed, ensuring the diversity and convergence of the algorithm;and in zone III, fruit flies are guided to accelerate convergence. The effectiveness and feasibility of this strategy is verified by this article according to simulation experiments and actual application cases. The results show that the proposed control strategy can make the inverter output follow the changes in the system for adaptive adjustment. The inverter response speed is increased 40-fold, and the steady-state error is reduced by 4.3%.

关键词： optimization Convergence Heuristic algorithms Microgrids Inverters Statistics Sociology improved fruit fly optimization algorithm population division multistrategy adaptive droop control online optimization

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A Dissolved Oxygen Prediction Model for the Yangtze River Basin Based on VMD-IFOA-Attention-GRU

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WATER 2025年第9期17卷 1278-1278页

作者： Zhu, Zhengyu Cao, Shouqi Shanghai Ocean Univ Coll Engn Sci & Technol Shanghai 201306 Peoples R China

Water ecological security is one of the key directions of current environmental protection. With the acceleration of urbanization and industrialization, the Shanghai region of the Yangtze River Basin faces various aquatic ecological issues, such as eutrophication and declining benthic biodiversity. Dissolved oxygen (DO), as a critical indicator for measuring water self-purification capacity and ecological health status, has been widely applied in water quality monitoring and early warning systems. Therefore, accurate prediction of dissolved oxygen concentration is of significant importance for the ecological and environmental protection of river basins. This study introduces a hybrid prediction model combining Variational Mode Decomposition (VMD), improved fruit fly optimization algorithm (IFOA), and Attention-based Gated Recurrent Unit (Attention-GRU). The model first decomposes preprocessed dissolved oxygen data through VMD to extract multiple intrinsic mode functions, reducing non-stationarity and high-frequency noise interference. It then utilizes the improved fruit fly optimization algorithm to adaptively optimize key parameters of the Attention-GRU network, enhancing the model's fitting capability. Experiments demonstrate that the VMD-IFOA-Attention-GRU model achieves 0.286, 0.302, and 0.915 for Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and coefficient of determination (R2), respectively, significantly outperforming other comparative models. The results indicate that this method can provide a reference for intelligent water quality prediction in typical regions such as the Yangtze River Basin.

关键词： dissolved oxygen prediction variational mode decomposition attention mechanism gated recurrent unit improved fruit fly optimization algorithm Yangtze River Basin

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A Study of Cutting Tool Wear Prediction Utilizing Generalized Regression Neural Network with improved fruit fly optimization

A Study of Cutting Tool Wear Prediction Utilizing Generalize...

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Prognostics and System Health Management Conference (PHM-Chongqing)

作者： Kang, Ling Xiong, Xin Yi, Lili Guo, Yijun Chongqing Univ State Key Lab Mech Transmiss Chongqing Peoples R China

ISBN: (纸本)9781538653807

Tool wear prediction becomes increasingly important due to the growing demand for finished quality and the improvement of productivity. In this case, it is necessary to establish a well-designed monitoring system to obtain the relationship between tool wear and cutting process. Generalized regression neural network (GRNN) is able to handle non-linear problems with its memory-based character. However, it was rarely used for tool wear prediction in the past several decades. Therefore, in this paper, it was employed to tackle this problem. In addition, in order to tune the smooth parameter of the GRNN, a newly proposed evolutionary algorithm called fruit fly optimization algorithm (FOA) was adopted. Meanwhile, an improved fruit fly optimization algorithm (IFOA), in which escaping and distance control parameters were introduced to prevent FOA from falling into local optimum, was presented to enhance the search ability. Two cutting experiments showed that the IFOA-GRNN provided a comparable regression ability to the GRNN with particle swarm optimization(PSO), the least squares support vector machines(LS-SVM) and the BP neural network.

关键词： Tool wear generalized regression neural network improved fruit fly optimization algorithm smooth parameter escaping and distance control parameters

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Construct the prediction model for China agricultural output value based on the optimization neural network of fruit fly optimization algorithm

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2018年 86卷 663-669页

作者： Han, Shi-Zhuan Pan, Wen-Tsao Zhou, Ying-Ying Liu, Zong-Li East China Jiaotong Univ Sch Econ & Management Nanchang Jiangxi Peoples R China Guangdong Univ Foreign Studies Sch Business Guangzhou Guangdong Peoples R China

Since agriculture is the foundation of a country and the industry that people depend on for life, it is particularly important for the development of national economy, and it has a higher output value than forestry, fishery and animal husbandry, so it occupies a very important position in the economic development of a country. The aim of this paper is to strengthen the capacity of prediction mode for total agricultural output value. This paper provides relevant government departments a reference and solves the problem of the lack of predictive ability of prediction mode for total agricultural output value in previous study. Different from previous literature, this paper adopts the new CFOA to optimize the parameters of GRNN, which contains innovative and reference value in some degree. Besides the way to validate this new model is to take the agricultural output value of the past years as a research sample and test it repeatedly. The study results have indicated that the total agricultural production value accounts for a higher proportion of agriculture, forestry, fishery and animal husbandry and the proportion tends to decline year by year;it can be found through 4 evaluation indexes that the prediction model that optimizes the smoothing parameters of GRNN through CFOA has a better predictive ability than the other two prediction models. (C) 2018 Elsevier B.V. All rights reserved.

关键词： fruit fly optimization algorithm improved fruit fly optimization algorithm Chaos fruit fly optimization algorithm General Regression Neural Network Total agricultural production value

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Approach for Short-Term Traffic Flow Prediction Based on Empirical Mode Decomposition and Combination Model Fusion

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2021年第9期22卷 5566-5576页

作者： Tian, Zhongda Shenyang Univ Technol Coll Informat Sci & Engn Shenyang 110870 Peoples R China

Accurate prediction of the traffic state can help to address the issue of traffic congestion, providing guiding advices for people's travel and traffic regulation. In this paper, we propose a novel short-term traffic flow prediction approach based on empirical mode decomposition and combination model fusion. First, we explore the amplitude-frequency characteristics of short-term traffic flow series, and use empirical mode decomposition to decompose traffic flow to several components with different frequency. Second, based on the results of self-similarity analysis of each component, improved extreme learning machine, seasonal auto regressive integrated moving average and auto regressive moving average are selected to predict different components. Meanwhile, an improved fruit fly optimization algorithm is proposed to optimize the weight coefficient of the combination model. Third, the prediction results of each prediction model are multiplied by their respective weight coefficient to get the final prediction results. We evaluate our prediction approach by doing thorough experiment on a real traffic data set. Moreover, experimental results show that the proposed approach has superior performance than state-of-the-art prediction methods or models in short-term traffic flow prediction.

关键词： Predictive models Roads Training Empirical mode decomposition optimization Prediction algorithms Biological system modeling Short-term traffic flow prediction empirical mode decomposition combination model fusion improved fruit fly optimization algorithm

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Multi-objective optimization of injection molded parts with insert based on IFOA-GRNN-NSGA-II

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JOURNAL OF POLYMER ENGINEERING 2022年第6期42卷 563-574页

作者： Li, Chunxiao Fan, Xiying Guo, Yonghuan Liu, Xin Wang, Changjing Wang, Dezhao Jiangsu Normal Univ Sch Mech & Elect Engn Xuzhou 221116 Jiangsu Peoples R China

The physical properties of plastic products, such as local strength, wear resistance and electrical properties, can be improved by adding embedded parts in the appropriate position of the products, and the precision of plastic parts can also be improved. However, due to the addition of inserts, the flow and shrinkage around inserts will be affected. Compared with traditional injection molding products, the quality is difficult to predict. To solve this problem, the injection molded parts with inserts (electrostatic test box) was used as an example, according to the product structure, three objectives of volume shrinkage, warpage in the X direction, and warpage in the Z direction were optimized. A generalized regression neural network (GRNN) model was established with molding parameters as input and quality objectives as output. improved fruit fly optimization algorithm (IFOA) was proposed to select the optimal smoothing parameters dynamically. Through the prediction of samples, the experimental results show that the model is superior to two comparative models. Non-dominated sorting genetic algorithm (NSGA-II) was used to solve the model, and the Pareto-optimal front was obtained. The entropy TOPSIS method was used to evaluate the Pareto-optimal front, and the optimal solution was obtained. The results show that IFOA-GRNN-NSGA is a reliable multi-objective optimization method.

关键词： GRNN improved fruit fly optimization algorithm injection moulding inserts multi-objective optimization NSGA-II

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