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

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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Short-term load forecasting of urban gas using a hybrid model based on improved fruit fly optimization algorithm and support vector machine

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ENERGY REPORTS 2019年 5卷 666-677页

作者： Lu, Hongfang Azimi, Mohammadamin Iseley, Tom Louisiana Tech Univ Trenchless Technol Ctr 599 Dan Reneau Dr Ruston LA 71270 USA

The accurate forecasting of short-term load for urban gas is the premise of gas supply sales, pipe network planning, and energy optimization scheduling. This paper adopts a hybrid model that integrates the fruit fly optimization algorithm (FFOA), simulated annealing algorithm (SA), cross factor (CF) and support vector machine (SVM) to forecast the short-term gas load of urban gas. In the model, SA and CF are used to optimize the FFOA algorithm. This paper takes the urban gas system in Kunming, China as a case study and uses the CF-SA-FFOA-SVM algorithm to predict the gas consumption and compares the results with the other four forecasting methods such as back-propagation neural network (BPNN) and autoregressive integrated moving average model (ARIMA). Besides, this paper analyzes the influence of temperature types (daily maximum temperature and daily mean temperature) in models on forecasting results, and the applicability of the algorithm for forecasting weekly and monthly gas load is analyzed. Moreover, the impact of grouping raw data by the feature on forecasting result is discussed. The following conclusions are drawn: (1) compared with other forecasting models, CF-SA-FFOA-SVM model has higher gas load forecasting accuracy. (2) for Kunming city, if the daily maximum temperature is used as the input variable in the gas load forecasting model, the forecasting accuracy is higher. (3) grouping raw data according to holiday attributes or gas types can effectively improve the accuracy of load forecasting. (C) 2019 The Authors. Published by Elsevier Ltd.

关键词： Short-term Gas load Forecasting Urban gas fruit fly optimization algorithm Support vector machine

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Single-objective optimization for stack unit of standing wave thermoacoustic refrigerator through fruit fly optimization algorithm

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INTERNATIONAL JOURNAL OF REFRIGERATION-REVUE INTERNATIONALE DU FROID 2019年 98卷 35-41页

作者： Rahman, Anas A. Zhang, Xiaoqing Huazhong Univ Sci & Technol Sch Energy & Power Engn Dept Refrigerat & Cryogen Wuhan 430074 Hubei Peoples R China

Thermoacoustic refrigeration is considered as a potential alternative refrigeration technology with environmentally-friendly working substance, no or few moving parts, high reliability as well as utilization of low grade energy. optimization for thermoacoustic systems or their key components has become a crucial task to make their performances competitive over conventional vapor-compression ones. In this study, fruit fly optimization algorithm (FOA) as an intelligent method is introduced to separately maximize coefficient of performance of stack unit and cooling power produced for one standing wave thermoacoustic refrigerator, as single objective functions under optimized stack length, stack center position, blockage ratio and drive ratio. FOA had shown better optimal results, compared to published literature results. Compared to results of genetic algorithm, COP had been enhanced by 15.8%. Moreover, cooling power was first discussed as another important objective to optimize. Studied results demonstrated that FOA is an effective approach for design optimization of thermoacoustic refrigerators. (C) 2018 Elsevier Ltd and IIR. All rights reserved.

关键词： Standing wave thermoacoustic refrigerator fruit fly optimization algorithm Stack unit Coefficient of performance Cooling power

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New fruit fly optimization algorithm with joint search strategies for function optimization problems

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KNOWLEDGE-BASED SYSTEMS 2019年 176卷 77-96页

作者： Wang, Lin Xiong, Yingnan Li, Shuwen Zeng, Yu-Rong Huazhong Univ Sci & Technol Sch Management Wuhan 430074 Hubei Peoples R China China Univ Geosci Wuhan Sch Econ & Management Wuhan 430074 Hubei Peoples R China Hubei Univ Econ Wuhan 430205 Hubei Peoples R China

An enhanced fruit fly optimization algorithm (FOA) with joint search strategies named JS-FOA is proposed to optimize continuous function problems. First, a collaborative group search, which includes a new parameter, is conducted to obtain the critical value. Second, a new search strategy similar to biological memory, namely, memory move direction, is proposed to improve solution accuracy. Third, a gradient descent search is used in the collaborative group search to ensure that it does not fall into a local optimum. Finally, a new function, which is similar to the excitation function in a neural network, is proposed to combine the three search strategies. To test the robustness and convergence of the proposed JS-FOA, we used 29 complex continuous benchmark functions. Results show that the proposed JS-FOA outperforms other heuristic algorithms for most functions. The performance of JS-FOA is also evaluated for different parameter values and the results show that parameter values affect convergence speed within a certain range, but do not change the convergence accuracy for the continuous benchmark functions. The proposed JS-FOA may potentially solve high-dimensional optimization problems. (C) 2019 Elsevier B.V. All rights reserved.

关键词： fruit fly optimization algorithm Group collaboration Memory move direction Gradient descent search

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Optimal Gating System Design of Steel Casting by fruit fly optimization algorithm Based on Casting Simulation Technology

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INTERNATIONAL JOURNAL OF METALCASTING 2019年第3期13卷 561-570页

作者： Wang, Tong Shen, Xu Zhou, Jianxin Yin, Yajun Ji, Xiaoyuan Zhou, Qin Huazhong Univ Sci & Technol State Key Lab Mat Proc & Die & Mould Technol Wuhan Hubei Peoples R China Huazhong Univ Sci & Technol Engn Training Ctr Wuhan Hubei Peoples R China

Casting simulation technology is an effective method to provide the predicted information on defects such as shrinkage, gas entrapment, cold shut, misrun and inclusions. To extend the analysis of casting simulation technology, a new optimal method for gating system design by fruit fly optimization algorithm (FOA) is proposed in this paper. First, according to the filling principles of steel casting, the gating system geometry mathematical model, which includes objective function and constraint conditions, is established. Second, in order to obtain optimal solution, fruit fly optimization algorithm is introduced to solve the above model. Finally, numerical simulation software is used to verify the validity of the proposed optimal method. Taking an upper center plate casting as an example, the results indicate that the proposed optimal method could provide practical and useful suggestions for the designer to obtain higher-quality and lower-resource-cost designs in the gating system design process.

关键词： gating system design steel casting fruit fly optimization algorithm casting simulation technology

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Object Pose Estimation in Accommodation Space using an Improved fruit fly optimization algorithm

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JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS 2019年第2期95卷 405-417页

作者： Guo, Qingda Quan, Yanming Jiang, Changcheng South China Univ Technol Sch Mech & Automot Engn Guangzhou 510640 Guangdong Peoples R China

The accommodation space changes as flexible products are packed into it. In order to improve the automatic loading of containers, it is necessary to solve the problem of object pose estimation in accommodation space. The goal of this study is to establish a method for pose estimation of a target object in the accommodation space. Firstly, the paper introduces basic algorithms and concepts, including the quick hull (Qhull) algorithm, oriented bounding box (OBB) algorithm, and fruit fly optimization algorithm (FOA). Secondly, the constraint conditions and the objective function of pose estimation are set up according to the pose variables in three-dimensional (3D) space, and a solution method for pose estimation is established using an improved FOA. Then, the algorithms with different population parameters are simulated, and the optimal parameters are obtained. The bounding box algorithm is used for system optimization, whereas a convex hull is used to simplify the target object significantly, reducing the corresponding running time. Finally, the hardware platform of the industrial robot is established, the initial and final poses of the end-effector are obtained using the proposed method, and tests are performed for different cases. The results show that the application of convex hull algorithm can significantly simplify a target object reducing the running time, and half individuals of the population guide the entire population to search for an optimal pose (6 degrees of freedom) in accommodation space.

关键词： Pose estimation fruit fly optimization algorithm Quick hull Oriented Bounding Box Accommodation space

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TWIN SUPPORT VECTOR REGRESSION BASED ON fruit fly optimization algorithm

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INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL 2019年第5期15卷 1851-1864页

作者： Fang, Jianwen Pan, Feng Gu, Binjie Jiangnan Univ Key Lab Adv Proc Control Light Ind Minist Educ 1800 Lihu Rd Wuxi 214122 Jiangsu Peoples R China

Recently, Twin Support Vector Regression (TSVR), which determines a pair of epsilon-insensitive lower and upper bound functions by solving two related SVR-type problems, has become a new hot topic in machine learning field. However, at least four parameters should be appropriately specified in TSVR. In this paper, in order to obtain the optimal parameters of TSVR, we proposed a twin support vector regression based on fruit fly optimization algorithm. First, we represented the parameters to be optimized in TSVR by the locations of the fruit flies. Then, we used fitting regression precision as fitness function, and let fruit flies fly randomly to avoid trapping into local minimum. Finally, we could find the highest regression accuracy corresponding to the final position of the fruit flies within finite iterations. The experimental results on benchmark datasets and glutamic acid fed-batch fermentation process show that the proposed algorithm can be used to find suitable parameters for TSVR. Furthermore, our algorithm costs less optimization time than other state-of-the-art algorithms.

关键词： Twin support vector regression fruit fly optimization algorithm Fitness function Parameter selection

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APTEEN routing protocol optimization in wireless sensor networks based on combination of genetic algorithms and fruit fly optimization algorithm

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AD HOC NETWORKS 2020年 102卷 102138-102138页

作者： Wang, Minghao Wang, Shubin Zhang, Bowen Inner Mongolia Univ Coll Elect Informat Engn Hohhot 010021 Peoples R China

APTEEN routing protocol exists the problems of uneven network energy consumption, premature death of some nodes, consume too much unnecessary energy and low effective coverage of the whole network. To solve these problems, this paper optimizes the APTEEN routing protocol by combining genetic algorithm with fruit fly optimization algorithm. By adding residual energy, distance from node to base station, distance from node to geometric center of the whole network, node degree and other selection factors to cluster heads selection, the genetic algorithm and fruit fly optimization algorithm is used to select cluster heads for the first time, and the second time of cluster heads selection based on density adaptive algorithm. Some nodes are selected to sleep according to the position and degree of nodes. The residual energy of cluster head, the distance between node and cluster head, and the number of cluster members are taken into account when nodes join clusters. When energy is transmitted from cluster heads to base station, the Dijkstra algorithm is used to find the optimal path. Add the rule of rotating cluster heads when the energy consumption of data transmission is too high, and the GA-APTEEN routing protocol is obtained through the above optimization. The simulation results show that the GA-APTEEN improves the 50% lifetime, 10% coverage and robustness of the network, reduces the energy consumption of the overall network system and avoids the phenomenon of the hot zone of energy. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Genetic algorithm fruit fly optimization algorithm Density adaptive algorithm Lifetime Coverage

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A Clinical Prediction Model in Health Time Series Data Based on Long Short-Term Memory Network Optimized by fruit fly optimization algorithm

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IEEE ACCESS 2020年 8卷 136014-136023页

作者： Lu, Weijia Ma, Liang Chen, Hao Jiang, Xiaojuan Gong, Ming Nantong Univ Sci & Technol Dept Affiliated Hosp Nantong 226001 Peoples R China Nantong Univ Informat Dept Affiliated Hosp Nantong 226001 Peoples R China

Aiming the problems that the clinical data of different patients is difficult for reasonable representation and the time interval between medical events is different, which lead to the difficulty of clinical prediction, a clinical prediction model based on the long short-term memory (LSTM) network optimized by fruit fly optimization algorithm in health time series data is proposed. First, FastText method is used to represent the interpretable vector of medical events, which can extract the concept relationship rich in medical information more effectively. Then, considering the strong dependence of clinical data on time stamp, LSTM network is used to model clinical events for better extraction of long-term and short-term information, so as to improve the prediction performance of the model. Finally, the fruit fly optimization algorithm is used to find the optimal super parameters of LSTM network, which can improve the training efficiency and prediction precision of the network. Experimental results on MIMIC datasets show that the prediction precision, Recall@k and MAP@k of the proposed model are better than those of other models. The validity of the model is proved.

关键词： Medical diagnostic imaging Predictive models Diseases Hospitals Indexes Electronic medical records fruit fly optimization algorithm LSTM network FastText method clinical prediction health time series data MIMIC dataset

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Research on A fruit fly optimization algorithm for OFDM Modulation System

Research on A Fruit Fly Optimization Algorithm for OFDM Modu...

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Chinese Automation Congress (CAC)

作者： Liu, Tao Bai, Zongmei Li, Changlin Univ Sci & Technol Beijing Automat & Elect Engn Beijing Peoples R China

ISBN: (纸本)9781728140940

the allocation of bit and power in OFDM system is realized by a improved discrete fruit fly optimization algorithm proposed in this paper. Firstly, the swap operator and swap sequence are introduced into the algorithm, which solves that the traditional fruit fly algorithm is only suitable for the optimization of continuous solutions;the fruit fly algorithm is then improved from the search step length and the update strategy;finally, the OFDM system meets the requirements of power minimization and throughput maximization by the algorithm. The simulation results by MATLAB prove that the improved algorithm is superior to traditional algorithms such as Chow.

关键词： Orthogonal frequency division multiplexing bit and power allocation fruit fly optimization algorithm

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