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fruit fly optimization algorithm multi-objective control method for MMC traction power supply system with unbalanced distribution network

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INTERNATIONAL JOURNAL OF DYNAMICS AND CONTROL 2022年第6期10卷 1922-1933页

作者： Pan, Hongbin Qin, Kai Zhang, Jiale Yuan, Changmin Xiangta Univ Coll Automat & Elect Informat Xiangtan Peoples R China

When distribution voltage is unbalanced, the voltage and current on the AC side of the traction power supply system based on a modular multilevel converter (MMC) will have negative sequence components, causing reactive and active power fluctuations. At this time, the circulation current will also occur in the MMC, threatening the safety and operation of the system. Based on the analysis of the power fluctuation mechanism, this paper derives the expression of the multi-objective control function combined with the universal current calculation formula. The fruit fly optimization algorithm (FOA) minimizes the multi-objective function and achieves the purpose of suppressing the coordinated control of the three objectives of negative sequence current and active and reactive power fluctuations. The resonant vector controller (RVC) was used to suppress the circulation current in MMC system. A simulation model was built to verify the effectiveness of the proposed strategy and algorithm.

关键词： Modular multilevel converter fruit fly optimization algorithm Circulation suppression Coordinated control of power and current

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Balancing and scheduling human-robot collaborative assembly lines with heterogeneous robots and limited resources: Constraint programming approach and fruit fly optimization algorithm

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COMPUTERS & INDUSTRIAL ENGINEERING 2025年 203卷

作者： Zheng, Chenyu Li, Zixiang Janardhanan, Mukund Zhang, Zikai Zhang, Liping Wuhan Univ Sci & Technol Minist Educ Key Lab Met Equipment & Control Technol Wuhan Hubei Peoples R China Wuhan Univ Sci & Technol Hubei Key Lab Mech Transmiss & Mfg Engn Wuhan Hubei Peoples R China Univ Warwick Int Digital Lab Coventry England

Collaborative robots are increasingly utilized to assist the human workers to assemble tasks or complete the assembly tasks solely in assembly lines. This study considers the human-robot collaborative assembly lines with heterogeneous collaborative robots and limited resources to optimize cycle time where human workers and collaborative robots can operate the different tasks in parallel. A constraint programming model is formulated that was able to achieve the optimal solutions for small-sized instances. An improved fruit fly optimization algorithm is developed to tackle the large-sized instances. The proposed algorithm proposes two vectors for encoding, where task assignment vector tackles task allocation sub-problem and process alternative vector tackles process alternative allocation sub-problem. This algorithm utilizes a decoding procedure with a constraint programming approach to achieve an optimal scheduling scheme of the station with human worker and collaborative robot. The lower bound and upper bound of completion time of single station and the earliest and latest processing time of tasks are added to the constraint programming model to speed up the search process. Meanwhile, improved fruit fly optimization algorithm utilizes the improved olfactory phase, improved visual phase and restart phase to accelerate the evolution of the whole swarm and avoid being trapped in local optimum. Computational study demonstrates that constraint programming approach outperforms the current mixed integer programming approach in objective value and solution time. The decoding procedure with constraint programming outperforms the current decoding procedure with mixed integer programming. Comparative study demonstrates that the proposed method outperforms the original fruit fly optimization algorithm and achieves promising performance in comparison with other methods. Finally, the proposed method is applied on scheduling of a gear box assembly line.

关键词： Assembly line balancing Human-robot collaboration Constraint programming fruit fly optimization algorithm

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fruit fly optimization algorithm based on a novel fluctuation model and its application in band selection for hyperspectral image

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JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2021年第1期12卷 1517-1539页

作者： Ding, Guoshen Qiao, Yanli Yi, Weining Fang, Wei Du, Lili Chinese Acad Sci Anhui Inst Opt & Fine Mech Key Lab Opt Calibrat & Characterizat Hefei 230031 Peoples R China Univ Sci & Technol China Hefei 230026 Peoples R China

Spectral band selection is an important operation in the field of hyperspectral remote sensing. However, most of the techniques cannot satisfy the needs of efficiency and accuracy at the same time. In this paper, we present a novel spectral band selection method, fruit fly optimization algorithm (FOA). As yet, FOA has not been used to solve the problem of band selection in hyperspectral image. Through the study of the algorithm, we know that the advantages of FOA are its simple structure and fewer parameters to be adjusted, but the algorithm itself also has some drawbacks. Thus, we first analyze the shortcomings of the traditional FOA, and the corresponding proofs are given by mathematical method. Then, we separate the whole optimization process into two sub-processes, each of which plays a different role. According to the change of the current iteration information and historical optimum value, a fluctuation model is designed in sub-pro1, and its validity is analyzed and validated theoretically and experimentally. In sub-pro2, a control factor is defined to guide the change rate of the step size. These two sub-processes have their own emphasis, and they cooperate with each other, taking into account the global and local optimization capabilities of the algorithm. The test results on 26 benchmark functions also prove that the proposed algorithm is superior to various state-of-art comparison algorithms. Finally, we introduce the proposed algorithm into the band selection of hyperspectral remote sensing, the gratifying results indicate that the proposed algorithm has great potential in hyperspectral remote sensing field.

关键词： fruit fly optimization algorithm Subsection strategy Fluctuation model Band selection

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Design of fruit fly optimization algorithm based on Gaussian distribution and its application to image processing

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SYSTEMS AND SOFT COMPUTING 2024年 6卷

作者： Jia, Huiying Yantai Gold Coll Dept Informat Engn Yantai 265401 Peoples R China

The fruit fly optimization algorithm (FOA) has strong applicability, which can be optimized directly after the objective is determined not by building a complex model. Due to the problems of the algorithm such as easy prematureness, low solution accuracy, and easy to fall into local optimality. Therefore, the Gaussian Distribution fruit fly optimization algorithm (GaussFOA) based on Gaussian distribution was first proposed to solve the shortcomings of FOA. Then GaussFOA was applied to image segmentation processing. Finally, the experimental results were compared with FOA, the improved fruit fly optimization algorithm with Changing Step and Strategy (CSSFOA), and the Linear Generation Mechanism of Candidate Solution of fruit fly optimization algorithm (LGMSFOA). The results showed that GaussFOA had 100 % success rate compared with FOA, CSSFOA, and LGMSFOA under the same function. This algorithm also had the best finding mean and standard deviation. The low and high threshold division was compared in terms of the number of segmentation thresholds. The GaussFOA had the best value of both the average and the standard deviation of the search for merit. The segmentation results under high threshold were more obvious when compared with the segmentation results of low threshold GaussFOA. The image immunity of GaussFOA was 8.57 %, 10 %, and 29.97 % higher than that of FOA, Particle Swarm optimization (PSO), and Genetic algorithm (GA). This indicated that the model constructed based on GaussFOA had improved the image segmentation effect and stability compared with other algorithms. The findings of the research can offer a new path for the processing techniques of images.

关键词： Gaussian distribution fruit fly optimization algorithm Image segmentation Standard deviation Merit-seeking adaptation

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An improved fruit fly optimization algorithm with Q-learning for solving distributed permutation flow shop scheduling problems

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COMPLEX & INTELLIGENT SYSTEMS 2024年第5期10卷 5965-5988页

作者： Zhao, Cai Wu, Lianghong Zuo, Cili Zhang, Hongqiang Hunan Univ Sci & Technol Sch Mech Engn Xiangtan 411100 Hunan Peoples R China Hunan Univ Sci & Technol Sch Informat & Elect Engn Xiangtan 411100 Hunan Peoples R China

The distributed permutation flow shop scheduling problem (DPFSP) is one of the hottest issues in the context of economic globalization. In this paper, a Q-learning enhanced fruit fly optimization algorithm (QFOA) is proposed to solve the DPFSP with the goal of minimizing the makespan. First, a hybrid strategy is used to cooperatively initialize the position of the fruit fly in the solution space and the boundary properties are used to improve the operation efficiency of QFOA. Second, the neighborhood structure based on problem knowledge is designed in the smell stage to generate neighborhood solutions, and the Q-learning method is conducive to the selection of high-quality neighborhood structures. Moreover, a local search algorithm based on key factories is designed to improve the solution accuracy by processing sequences of subjobs from key factories. Finally, the proposed QFOA is compared with the state-of-the-art algorithms for solving 720 well-known large-scale benchmark instances. The experimental results demonstrate the most outstanding performance of QFOA.

关键词： fruit fly optimization algorithm Makespan Distributed permutation flow-shop scheduling Q-learning

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Prediction of CO2 emissions in China by generalized regression neural network optimized with fruit fly optimization algorithm

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ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH 2023年第33期30卷 80676-80692页

作者： Yue, Hui Bu, Liangtao Hunan Univ Coll Civil Engn Changsha Hunan Peoples R China

As global warming becomes more prominent, the need to reduce carbon emissions to achieve China's carbon peak target is increasing. It is imperative to seek effective methods to predict carbon emissions and propose targeted emission reduction measures. In this paper, a comprehensive model integrating grey relational analysis (GRA), generalized regression neural network (GRNN) and fruit fly optimization algorithm (FOA) is constructed with carbon emission prediction as the research objective. Firstly, GRA is used for feature selection to find out the factors that have a strong influence on carbon emissions. Secondly, the parameter of GRNN is optimized using FOA algorithm to improve the prediction accuracy. The results show that (1) fossil energy consumption, population, urbanization rate and GDP are important factors affecting carbon emissions;(2) FOA-GRNN outperforms GRNN and back propagation neural network (BPNN), verifying the effectiveness of FOA-GRNN model for CO2 emission prediction. Finally, by analyzing the key influencing factors and combining scenario analysis with forecasting algorithms, the carbon emission trends in China for 2020-2035 are forecasted. The results can provide guidance for policy makers to set reasonable carbon emission reduction targets and adopt corresponding energy saving and emission reduction measures.

关键词： Carbon emissions Grey relational analysis General regression neural network fruit fly optimization algorithm Scenario analysis

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A novel elitist fruit fly optimization algorithm

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SOFT COMPUTING 2023年第8期27卷 4823-4851页

作者： He, Jieguang Peng, Zhiping Qiu, Jinbo Cui, Delong Li, Qirui Guangdong Univ Petrochem Technol Coll Comp Sci Maoming Peoples R China Zhejiang Univ State Key Lab Ind Control Technol Hangzhou Peoples R China Jiangmen Polytech Sch Informat Engn Jiangmen Peoples R China Guangdong Univ Petrochem Technol Guangdong Prov Key Lab Petrochem Equipment Fault D Maoming Peoples R China Guangdong Univ Petrochem Technol Coll Elect Informat Engn Maoming Peoples R China

Aiming at the poor population diversity and serious imbalance between global exploration and local exploitation in the original fruit fly optimization algorithm (FOA), a novel elitist fruit fly optimization algorithm (EFOA) with elite guidance and population diversity maintenance is proposed. EFOA consists of two search phases: an osphresis search with elite and random individual guiding and a vision search with elite and boundary guiding in an iteration. The former contains two sub-stages: exploration with random individual guiding and exploitation with elite individual guiding. Randomly selected individual and flight control parameter constructed by the Sigmoid-based function are first introduced into the algorithm to improve the exploration. The elite guiding strategy with two position-update approaches is designed to augment the local ability of the proposed algorithm. With these stages, EFOA can search some areas of the problem space as much as possible. Finally, elite and boundary information is introduced into EFOA to enhance population diversity. The proposed EFOA is compared with other algorithms, including the original FOA, three outstanding FOA variants, and five state-of-the-art meta-heuristic algorithms. The validation tests are conducted based on the classical benchmark functions and CEC2017 benchmark functions. The Wilcoxon signed rank test and Friedman test are utilized to verify the significance of the results from the perspective of non-parametric statistics. The results demonstrate that the elite guiding strategy and the alternating execution of the three search stages can effectively balance the exploration and exploitation capabilities of the EFOA and enhance its convergence speed.

关键词： Swarm intelligence algorithm fruit fly optimization algorithm Elite guidance Boundary information Population diversity

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The short-term photovoltaic power generation forecasting based on fruit fly optimization algorithm optimized BP neural network 11

The short-term photovoltaic power generation forecasting bas...

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11th IEEE International Conference on Cybernetics and Intelligent Systems (CIS) / 11th IEEE International Conference on Robotics, Automation and Mechatronics (RAM)

作者： Zhang, Shaoru Zhu, Haoxuan Yang, Likun Huang, Zhanping Du, Xiuju Zhang, Jielu Luo, Fang Lin Hebei Normal Univ Coll Engn Shijiazhuang 050024 Hebei Peoples R China Jiangsu Tailong Reducer Co LTD Hebei Prov Innovat Ctr Wireless Sensor Network Da Taizhou 225400 Peoples R China Jiangsu Tailong Reducer Co LTD Taizhou 225400 Peoples R China Nanyang Technol Univ NTU Sch Elect & Elect Engn Nanyang Ave Singapore 639798 Singapore

ISBN: (纸本)9798350364200;9798350364194

Accurately predicting photovoltaic power generation is crucial for ensuring the safe operation of power grids and advancing solar energy development and utilization. For the issue of large errors in current prediction methods, this paper introduces a forecasting method for photovoltaic power generation. The method utilizes fruit fly optimization algorithm (FOA) to optimize Back Propagation (BP) neural network. This involves analyzing the correlations among factors impacting photovoltaic power generation and selecting relevant meteorological data via the Pearson correlation coefficient method. fruit fly algorithm demonstrates rapid convergence, a minimal parameter set and ease of adjustment, rendering it applicable across various domains. Employing fruit fly algorithm to optimize weights and thresholds within BP neural network leads to the final prediction outcomes. Simulation results confirm the superior prediction accuracy of the FOA-BP model for photovoltaic power generation, particularly during spring, autumn and winter, showcasing its practical utility.

关键词： Photovoltaic power generation Power prediction fruit fly optimization algorithm BP neural network

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Locality sensitive hashing-aware fruit fly optimization algorithm and its application in edge server placement

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JOURNAL OF CLOUD COMPUTING-ADVANCES SYSTEMS AND APPLICATIONS 2022年第1期11卷 1-15页

作者： Cao, Qian Liu, Bo Jin, Ying Chaohu Univ Coll Informat Engn Hefei Peoples R China Macau Univ Sci & Technol Fac Innovat Engn Macau Peoples R China Hefei Univ Sch Artif Intelligence & Big Data Hefei Peoples R China

As is well known that the global optimization ability of the fruit fly optimization algorithm (FOA)is weak because it is easy to fall into local optimum. In this paper, a fruit fly optimization algorithm based on Locality Sensitive Hashing-aware (LSHFOA)was proposed. The locality sensitive hashing mechanism to optimize the generation mechanism for swarm population individuals was used, which can improve the individual diversity of the population. Meanwhile, when the fruit fly population falls into the local optimum, the locality sensitive hashing mechanism was adopted to change the population location, which is used for jumping out of local optimal limits. To verify the performance of LSHFOA, it was compared with FOA and its improvement algorithms CFOA, and IFFO with 8 representative benchmark functions. A large number of experimental results showed that LSHFOA has a faster convergence speed and higher precision of optimization for function optimization, especially in high-dimensional multi-peak functions. In addition to the theoretical evaluation, we also evaluate its performance in a real-world scenario. Generally, an edge computing environment, as an extension of cloud computing, can allow the users to access the network in a low-latency manner. In this way, to capture the high-speed convergence advantage, this paper makes the first attempt to tackle a classic research problem in the edge computing environment, i.e., the edge server placement problem. The experimental results show that the new algorithm has an excellent application effect.

关键词： fruit fly optimization algorithm Locality sensitive hashing Swarm intelligence algorithm Global optimization capacity Edge server placement problem Edge computing

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Short-Term Load Forecasting Model of Ameliorated CNN Based on Adaptive Mutation fruit fly optimization algorithm

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ELECTRIC POWER COMPONENTS AND SYSTEMS 2022年第1-2期50卷 1-10页

作者： Sun, Kai Dou, Zhenhai Zhang, Bo Zou, Hao Li, Shengtao Zhu, Yaling Liao, Qingling Shandong Univ Technol Sch Elect & Elect Engn Zibo Shandong Peoples R China Zibo Power Supply Co State Grid Corp China Zibo Shandong Peoples R China

In order to improve the accuracy and calculating speed of load forecasting for the strong nonlinear problem of short-term load, this article proposes a Short-term Load Forecasting Model of Ameliorated CNN Based on Adaptive Mutation fruit fly optimization algorithm. This method integrates the Extreme Learning Machine (ELM) algorithm into the Convolutional Neural Network (CNN): replace the fully connected layer in the original CNN network with ELM to form a CNN-ELM network. The purpose is to improve the calculation accuracy. An Adaptive Mutation fruit fly optimization algorithm (AMFOA) was proposed to reduce the probability that the fruit fly optimization algorithm (FOA) would easily fall into a local optimal value. And then AMFOA is used to optimize the parameters in CNN-ELM network. The above model is used to predict the grid load of a certain area in northern China. Compared with other prediction algorithms, it is proved that the model proposed in this article has higher prediction accuracy and also proved that the model has higher calculation speed than other models.

关键词： short-term load forecasting convolutional neural network extreme learning machine adaptive mutation fruit fly optimization algorithm

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