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Effective long short-term memory with fruit fly optimization algorithm for time series forecasting

SOFT COMPUTING 2020年第19期24卷 15059-15079页

作者： Peng, Lu Zhu, Qing Lv, Sheng-Xiang Wang, Lin Huazhong Univ Sci & Technol Sch Management Wuhan 430074 Peoples R China Shaanxi Normal Univ Int Business Sch Xian 710000 Peoples R China

A number of recent studies have adopted long short-term memory (LSTM) in extensive applications, such as handwriting recognition and time series prediction, with considerable success. However, the parameters of LSTM have greatly influenced its accuracy and performance. In this study, LSTM with fruit fly optimization algorithm (FOA), called FOA-LSTM, is designed to solve time series problems. As a novel intelligent algorithm, FOA is applied to decide on the optimal hyper-parameter of LSTM. Experiments under the NN3 time series, three comparative experiments and the monthly energy consumption of the USA are conducted to verify the effectiveness of the FOA-LSTM model. The results indicate that the symmetric mean absolute percentage error (SMAPE) is reduced by up to 11.44% in the last 11 monthly series in the NN3 dataset. Four comparative experiments and the real-life series verify further that the FOA-LSTM model obtains a better result compared with other forecasting models.

关键词： Long short-term memory fruit fly optimization algorithm Time series forecasting

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RWFOA: a random walk-based fruit fly optimization algorithm

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SOFT COMPUTING 2020年第16期24卷 12681-12690页

作者： Chen, Chong Shandong Polytech Jinan Peoples R China

As one kind of the classic swarm intelligence (SI) algorithms, fruit fly optimization algorithm has been widely used in many aspects, such as multiple objective optimization and service computing. However, due to the limitation that introduced by the initial population location, its global optimization ability is limited. Therefore, we propose a random walk-based fruit fly optimization algorithm, namely RWFOA, to enhance its global optimization ability. RWFOA employs the random walk mechanism to dynamically adjust the position of the fruit fly population, which can reduce the impact of the initial population location and thus enhance the global optimization ability. We conduct a comprehensive experimental evaluation of RWFOA by comparing it with three representative algorithms, i.e., the OFOA, CFOA and IFFO, over 29 widely used benchmark functions published in CEC 2015. Experimental results demonstrate that RWFOA can find better solutions in most of the selected benchmark functions.

关键词： fruit fly optimization algorithm Random work Probability graph Roulette scheme Swarm intelligence algorithm

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Optimal Chiller Loading for Energy Conservation Using an Improved fruit fly optimization algorithm

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ENERGIES 2020年第15期13卷 3760-3760页

作者： Qi, Min-Yong Li, Jun-Qing Han, Yu-Yan Dong, Jin-Xin Liaocheng Univ Coll Comp Sci Liaocheng 252059 Shandong Peoples R China Shandong Normal Univ Sch Informat Sci & Engn Jinan 250014 Peoples R China

In the multi-chiller of the air conditioning system, the optimal chiller loading (OCL) is an important research topic. This research is to find the appropriate partial load ratio (PLR) for each chiller in order to minimize the total energy consumption of the multi-chiller under the system cooling load (CL) requirements. However, this optimization problem has not been well studied. In this paper, in order to solve the OCL problem, we propose an improved fruit fly optimization algorithm (IFOA). A linear generation mechanism is developed to uniformly generate candidate solutions, and a new dynamic search radius method is employed to balance the local and global search ability of IFOA. To empirically evaluate the performance of the proposed IFOA, a number of comparative experiments are conducted on three well-known cases. The experimental results show that IFOA found 14 optimal values (the optimal values among all algorithms) under a total of 17CLs in three cases, and the ratio of the optimal values found was 82.4%, which was the highest among all algorithms. In addition, the mean value of all objective functions of IFOA is smaller and the standard deviation is equal to or close to 0, which proves that the algorithm has high stability. It can be concluded that IFOA is an ideal method to solve the OCL problem.

关键词： optimal chiller loading energy conservation fruit fly optimization algorithm

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Soft sensor for the moisture content of crude oil based on multi-kernel Gaussian process regression optimized by an adaptive variable population fruit fly optimization algorithm

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TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL 2020年第4期42卷 770-785页

作者： Li, Kun Xu, Wensu Han, Ying Ge, Fawei Wang, Yi'an Bohai Univ Coll Engn 19 Keji Rd Jinzhou 121013 Liaoning Peoples R China

In the practical oilfield production, it has great significance to realize timely and accurate measurement of the moisture content of crude oil. However, there are some drawbacks in the traditional measurement methods, such as: non-real time, high cost, labor-consume, vulnerability to environmental impacts, and so on. In order to solve these problems, a soft sensor model based on multi-kernel Gaussian process regression optimized by an adaptive variable population fruit fly optimization algorithm (APFOA-MKGPR) is presented in this paper. A multiple kernels-based Gaussian process regression method is utilized to deal with the practical production process characterised by multiple operating phases, noises, strong nonlinearity and dynamic. In the multi-kernel function, many parameters (five hyper-parameters in the multi-kernel function and three weights of each kernel function) need to be accurately given, which is difficult to be effectively optimized by the maximum likelihood estimation. So, a swarm intelligence-based adaptive variable population fruit fly optimization algorithm (APFOA) is proposed to train the best model parameters. A novel adaptive variable population mechanism is developed to adaptively adjust the population size and the random flight distance during the iterations, which can realize a combination of the global searching and the local searching for the optimal solutions. The proposed method is verified by four benchmark functions and the actual production data of one oil well, and experimental results show the effectiveness for accurate prediction of the moisture content of crude oil.

关键词： Moisture content of crude oil soft sensor Gaussian process regression Multiple kernels fruit fly optimization algorithm

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Finding Community Modules for Brain Networks Combined Uniform Design with fruit fly optimization algorithm

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INTERDISCIPLINARY SCIENCES-COMPUTATIONAL LIFE SCIENCES 2020年第2期12卷 178-192页

作者： Zhang, Jie Feng, Junhong Yang, Yifang Wang, Jian-Hong Yulin Normal Univ Sch Comp Sci & Engn Yulin 537000 Guangxi Peoples R China Xian Shiyou Univ Coll Sci Xian 710065 Shaanxi Peoples R China

There are a huge amount of neural units in brain networks. Some of the neural units have tight connection and form neural unit modules. These unit modules are helpful to the disease detection and target therapy. A good method can find neural unit modules accurately and effectively. The study proposes a new algorithm to analyze a brain network and obtain its neural unit modules. The proposed algorithm combines the uniform design and the fruit fly optimization algorithm (FOA);therefore, we called it as UFOA. It makes the utmost of their respective merits of the uniform design and the FOA, so as to acquire the feasible solutions scattered uniformly over the vector domain and find the optimal solution as quickly as possible. When compared with other existing methods, FOA and the uniform design are integrated first, and UFOA is first utilized to find unit modules from brain networks. 37 TD resting-state functional MRI brain networks are used to testify the performance of UFOA. The obtained experimental results manifest that UFOA is clearly superior to the other five methods in terms of modularity, and is comparable with the other five methods in terms of conductance. Additionally, the comparative analysis of UFOA and FOA also demonstrates that the uniform design brings benefit to the improvement of UFOA.

关键词： fruit fly optimization algorithm Uniform design Brain networks Neural unit modules

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A hierarchical guidance strategy assisted fruit fly optimization algorithm with cooperative learning mechanism

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EXPERT SYSTEMS WITH APPLICATIONS 2021年 183卷 115342-115342页

作者： Zhao, Fuqing Ding, Ruiqing Wang, Ling Cao, Jie Tang, Jianxin Lanzhou Univ Technol Sch Comp & Commun Technol Lanzhou 730050 Peoples R China Tsinghua Univ Dept Automat Beijing 10084 Peoples R China

The fruit fly optimization algorithm (FOA) has drawn enormous attention from researchers and practitioners in the computation intelligence domain for the benefits of simple implementation mechanism and few parameters tuning requirement of FOA. However, FOA is hard to adapt directly to address complex continuous problems. A hierarchical guidance strategy assisted fruit fly optimization algorithm with cooperative learning mechanism (HGCLFOA) is proposed in this study. The population is divided into elitist and inferior subpopulations with the fitness of objective function. The population center is re-designed as an elitist subpopulation to maintain the diversity of the population. In the olfaction search stage, the hierarchical guidance strategy is introduced for local search according to the difference of solution qualities to assign inferior individuals to elitist individuals on different levels. Meanwhile, the inferior information is applied by the inferior solutions repairing strategy to deflect the prediction of the elitist subpopulation for preventing HGCLFOA from falling into the local optimum. In the vision search stage, a hybrid Gaussian distribution estimation strategy is adopted to extract the elitist information of previous generations to predict the distribution of potential elitist individuals in the next generation. The exploration and exploitation of the HGCLFOA are balanced by the cooperation between elitist subpopulation and inferior subpopulation. A random walk strategy is activated to assist the elitist solutions to jump out the local optimal. The parameters of the HGCLFOA are calibrated by DOE and ANOVA methods. The experimental results demonstrated that the HGCLFOA outperformed the classical FOA and state-of-arts variants of FOA.

关键词： fruit fly optimization algorithm Gaussian distribution estimation algorithm Hierarchical guidance Cooperative learning mechanism

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Water Quality Evolution Mechanism Modeling and Estimation Based on fruit fly optimization algorithm and Particle Filter 39

Water Quality Evolution Mechanism Modeling and Estimation Ba...

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39th Chinese Control Conference (CCC)

作者： Zhou, Yuqin Zhao, Zhiyao Wang, Xiaoyi Sun, Qian Beijing Technol & Business Univ Sch Artificial Intelligence 11-33 Fucheng Rd Beijing 100048 Peoples R China Beijing Technol & Business Univ China Light Ind Key Lab Ind Internet & Big Data 11-33 Fucheng Rd Beijing 100048 Peoples R China

ISBN: (纸本)9789881563903

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

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Application of the fruit fly optimization algorithm to an optimized neural network model in radar target recognition

COMPUTER OPTICS

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COMPUTER OPTICS 2021年第2期45卷 296-300页

作者： Liu, M. Sun, Z. H. Sichuan Vocat Coll Chem Technol Luzhou 646005 Sichuan Peoples R China Air Force Logist Univ Xuzhou 221000 Jiangsu Peoples R China

With the development of computer technology, there are more and more algorithms and models for data processing and analysis, which brings a new direction to radar target recognition. This study mainly analyzed the recognition of high resolution range profile (HRRP) in radar target recognition and applied the generalized regression neural network (GRNN) model for HRRP recognition. In order to improve the performance of HRRP, the fruit fly optimization algorithm (FOA) algorithm was improved to optimize the parameters of the GRNN model. Simulation experiments were carried out on three types of aircraft. The improved FOA-GRNN (IFOA-GRNN) model was compared with the radial basis function (RBF) and GRNN models. The results showed that the IFOA-GRNN model had a better convergence accuracy, the highest average recognition rate (96.4 %), the shortest average calculation time (275 s), and a good recognition rate under noise interference. The experimental results show that the IFOA-GRNN model has a good performance in radar target recognition and can be further promoted and applied in practice.

关键词： radar technology target recognition generalized regression neural network high-resolution range profile fruit fly optimization algorithm

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RNA secondary structure prediction using fruit fly optimization algorithm

RNA secondary structure prediction using Fruit Fly Optimizat...

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IEEE-Region-10 Symposium (TENSYMP) - Technology for Impactful Sustainable Development

作者： Chatterjee, Sajib Rabeya, Sayla Parvin Halder, Setu Mondal, Madhab Sujana, Farjana Yesmin North Western Univ Dept Comp Sci & Engn Khulna Bangladesh

ISBN: (纸本)9781728173665

RNA Secondary Structure Prediction (RSSP) is an optimization problem, where a stable secondary structure is acquired from an RNA primary sequence. Many exact, heuristic and metaheuristic algorithms established in recent years to solve the RSSP problem. We have resolved an accession based on metaheuristic algorithm named fruit fly optimization algorithm to solve the RNA secondary structure prediction problem. FOA is a population-based metaheuristic that works better than all other related existing algorithms and has been employed in different optimization problems. We have redesigned the operators of the FOA algorithm and calculated the minimum Gibbs free energy (Delta G) of the structure to solve the RNA secondary structure problem. We have a Repair function which is known as novel operator that is used to verify and expel the repeated stem from RNA sequence, which is very time-efficient. Every quality of the solutions and spending time are calculated in designing the operators and the repair function. The raised methodology gives efficiency, robustness, and effectiveness in solving the RSSP problem.

关键词： RNA secondary structure prediction fruit fly optimization algorithm Minimum Gibbs free energy Repair function efficiency

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A novel fruit fly optimization algorithm with Levi flight and challenge probability

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Procedia Computer Science 2021年 183卷 182-188页

作者： Tianlei Wang Jinzhao Xu Wenhui Luo Yanjiang Yu Zundi Huang Faculty of Intelligent Manufacturing Wuyi University Jiangmeng Guangdong 529000 China School of Mathematics and Computational Science Wuyi University Jiangmen Guangdong 529000 China School of Rail Transportation Wuyi University Jiangmen Guangdong 529000 China

Aiming at the problem of low convergence precision and premature convergence of fruit fly optimization algorithm (FOA), this paper proposes a novel fruit fly optimization algorithm with Levi flight and challenge probability (LCFOA). We introduce the Levi flight mechanism and challenge adjustment process to coordinate the global exploration and local exploitation capabilities of the algorithm. Then, we designed an adaptive challenge factor in the challenge adjustment process to improve the convergence precision of the algorithm. The convergence performance of the algorithm is verified by 6 different benchmark functions. The numerical experimental results comfirmed that the proposed LCFOA has better search precision and convergence speed.

关键词： fruit fly optimization algorithm metaheuristic optimization swarm intelligence Levy flight challengeprobability

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