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Optimum deployment of sensor nodes in wireless sensor network using hybrid fruit fly optimization algorithm and bat optimization algorithm for 3D Environment

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PEER-TO-PEER NETWORKING AND APPLICATIONS 2022年第6期15卷 2694-2718页

作者： Mohar, Satinder Singh Goyal, Sonia Kaur, Ranjit Punjabi Univ Dept Elect & Commun Engn Patiala Punjab India

Deployment of sensor nodes in three dimensional areas with sufficient coverage of sensor nodes is one of the major challenges in wireless sensor network. Coverage is main concern in node deployment because it influences the performance of wireless sensor network. For better performance of wireless sensor network it is essential to increase the coverage of nodes by locating the nodes at optimum positions with help of efficient optimization algorithm. In this paper the sensor nodes are located by using Hybrid fruit fly optimization algorithm and bat optimization algorithm in three dimensional environment. The exploration feature of fruit fly optimization algorithm is combined with exploitation characteristics of bat optimization algorithm in proposed algorithm. The grid points covered once by sensor node are removed from entire grid points for remaining nodes. With removal of grid points the overload on sensor nodes is reduced in proposed algorithm. The simulation results of Hybrid fruit fly optimization algorithm and bat optimization algorithm are compared in terms of variance, standard deviation, coverage rate and coefficient of dispersion with fruit fly optimization algorithm and bat optimization algorithm. Moreover, to verify the efficiency of proposed algorithm the results are also compared with other optimization algorithms such as artificial bee colony algorithm with dynamic search strategy, grey wolf optimization algorithm, enhanced grey wolf optimization algorithm, whale optimization algorithm, hybrid shuffled frog leaping algorithm and whale optimization algorithm, differential evolution algorithm, shuffled frog leaping algorithm, hybrid shuffled frog leaping algorithm and whale optimization algorithm based on differential evolution respectively. The simulation result signifies that proposed Hybrid fruit fly optimization algorithm and bat optimization algorithm is efficient than above stated existing optimization algorithm in terms of average coverage rate.

关键词： Sensor nodes Bat optimization algorithm Wireless sensor network Node deployment fruit fly optimization algorithm

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Improved exponential model for thermal error modeling of machine-tool spindle based on fruit fly optimization algorithm

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE 2022年第12期236卷 6912-6922页

作者： Liao, Qihao Yin, Qin Xie, Luofeng Yin, Guofu Sichuan Univ Sch Mech Engn 24 South Sect 1Yihuan Rd Chengdu 610065 Peoples R China

Establishing a robust thermal error model is crucial for thermal error compensation of machine tools. In this paper, an improved exponential model for thermal error modeling was proposed. First, the improved exponential model is established by combining the exponential model and the temperature-dependent model. Second, the improved fruit fly optimization algorithm is used to determine the parameters. Finally, two strategies are used to select the optimal sensor location from six potential ambient temperature sensors. The ambient temperature data is proved to be effective in improving the robustness of the model. The average root mean square error of the proposed model, the exponential model without ambient temperature, and the linear model are 1.18 mu m, 1.79 mu m, and 2.70 mu m, respectively. The modeling results show that the proposed model has high accuracy and strong robustness, which is suitable for workshop environment and variable conditions in thermal error modeling of machine tools.

关键词： Thermal error modeling spindle exponential model fruit fly optimization algorithm grinding machine

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Structural Damage Identification Based on Improved fruit fly optimization algorithm

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KSCE JOURNAL OF CIVIL ENGINEERING 2021年第3期25卷 985-1007页

作者： Xiong, Chunbao Lian, Sida Tianjin Univ Sch Civil Engn Tianjin 300072 Peoples R China

To locate the damage of the structure efficiently and judge the damage degree, this paper proposes an improved fruit fly optimization algorithm (IFOA). Aiming at the problem of poor convergence of the standard fruit fly optimization algorithm in the face of complex structure damage identification, the IFOA introduces the concept of collaborative search of two subpopulations. The IFOA divides the entire population into positive subgroups and negative subgroups based on the individual taste concentration results. Among them, the positive subgroup uses the improved dynamic adaptive search step size to perform a fine search locally to improve its local search ability. Negative subgroups continue to use the standard fruit fly optimization algorithm for optimization, taking advantage of the powerful global search capabilities of the standard fruit fly optimization algorithm. It enables the algorithm to balance global and local search capabilities, prevents the algorithm from falling into local optimum, and speeds up the convergence speed and accuracy of the algorithm. Simulation results show that IFOA can effectively identify the damage location and damage degree of the structure, and it still performs well when facing the complex steel truss damage identification.

关键词： Structural damage identification fruit fly optimization algorithm Collaborative search Adaptive step size Steel truss structure

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A multi-strategy fruit fly optimization algorithm for the distributed permutation flowshop scheduling problem with sequence-dependent setup times

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APPLIED SOFT COMPUTING 2024年 167卷

作者： Zhao, Cai Wu, Lianghong 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

Distributed manufacturing has become one of the mainstream manufacturing modes today and is widely present in industries such as aviation and electronics. However, in actual production processes, unexpected situations such as machine failures and tool changes may occur, which require time. Based on practical needs, this paper studies a distributed permutation flow shop scheduling problem with sequence-dependent setup times (DPFSP/SDST) aimed at minimizing the makespan and proposes a hybrid multi-strategy fruit fly optimization algorithm (HMFOA) to solve it. In HMFOA, three strategies are constructed to initialize the positions of some individual flies in the solution space to improve population diversity. In the smell search phase, four problem-oriented neighborhood perturbation operators are designed, and sinusoidal optimization algorithm is introduced to control the search range, which improves the global search ability of the algorithm. In the visual search phase, a position reconstruction strategy is proposed to divide individual flies into different populations based on their mass. Through the interaction of individuals from different populations, the convergence is accelerated and the algorithm efficiency is improved. In addition, a local search strategy is designed to guide the flies to more promising areas. Based on well-known examples of DPFSP in the literature, a comprehensive test set was generated for DPFSP/SDST, taking into account various combinations of jobs, machines, factories, and SDST, resulting in 270 benchmark instances used to validate the performance of HMFOA, and compared to eight other advanced algorithms. The relative percentage deviation of HMFOA is 1.00%, which is significant improvement.

关键词： fruit fly optimization algorithm Makespan Scheduling Neighborhood structures

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Application of Improved fruit fly optimization algorithm in Three Bar Truss 18th

Application of Improved Fruit Fly Optimization Algorithm in ...

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18th International Conference on Intelligent Computing (ICIC)

作者： Tao, Dao Wei, Xiuxi Huang, Huajuan Guangxi Minzu Univ Coll Elect Informat Nanning 530006 Peoples R China Guangxi Minzu Univ Coll Artificial Intelligence Nanning 530006 Peoples R China

ISBN: (纸本)9783031138324;9783031138317

fruit fly algorithm (FOA) is a new swarm intelligence algorithm based on fruit fly foraging behavior. It has the advantages of less adjustment parameters, fast running speed and easy to use. However, the original algorithm also has some problems, such as poor stability, low convergence accuracy and easy to fall into local optimization. Because the exponential function, logarithmic function and inverse proportional function have different variation characteristics, they have good parameter regulation performance. In order to improve the optimization performance of FOA, the above three mechanisms are introduced into the original algorithm. Firstly, the variable step size mechanism (exponential function) is applied in the initialization of FOA, which can improve the stability of FOA;Secondly, the logarithmic mechanism is used for iterative optimization to improve the optimization accuracy of the original algorithm;Thirdly, the disturbance coefficient (inverse proportional function) is used to replace the fixed step value, so as to improve the global optimization ability of the original algorithm. Therefore, this paper proposes a multi mechanism improved fruit fly algorithm (IFOA), which is to reduce the blindness and disorder in the optimization process and promote the optimization ability of the algorithm. Finally, 15 test functions and an engineering example are selected to simulate experiment. The results show that IFOA has better performance.

关键词： fruit fly optimization algorithm Three bar truss Disturbance coefficient Test functions Variable step size

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Capacity Optimal Allocation Strategy of Energy Storage System Based on fruit fly optimization algorithm 4

Capacity Optimal Allocation Strategy of Energy Storage Syste...

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4th International Conference on Smart Power and Internet Energy Systems (SPIES)

作者： Wu, Shaojiang Wang, Yi Lin, Kai Liao, Wanying Huang, Weiqin Fujian Shuikou Power Generat Grp Co Ltd Fuzhou Peoples R China

ISBN: (纸本)9781665489577

The multi-energy system composed of new energy has the advantages of green and environmental protection. The hybrid energy storage system composed of pumped storage power stations and batteries is used in the wind-solar based multi energy system to realize the complementary regulation of the power generation and load side. In the working strategy of the system, in order to reduce the electric energy lost in the process of energy storage and discharge, and minimize the working times of pumps and turbines, a capacity optimal allocation strategy of energy storage system based on fruit fly optimization algorithm is proposed, taking the total investment cost of the system as the objective function. An example shows the effectiveness and applicability of the strategy. While the hybrid energy storage system meets the system performance index, the loss of electric energy in the energy storage process is the least, and it has the best economy.

关键词： Multi energy system fruit fly optimization algorithm Hybrid energy storage system Capacity optimization configuration

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A Novel Hybrid Filter/Wrapper Feature Selection Approach Based on Improved fruit fly optimization algorithm and Chi-square Test for High Dimensional Microarray Data

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CURRENT BIOINFORMATICS 2021年第1期16卷 63-79页

作者： Yan, Chaokun Wu, Bin Ma, Jingjing Zhang, Ge Luo, Junwei Wang, Jianlin Luo, Huimin Henan Univ Sch Comp & Informat Engn Kaifeng Peoples R China China Mobile Online Serv Co Ltd Hong Kong Peoples R China Henan Polytech Univ Coll Comp Sci & Technol Jiaozuo Henan Peoples R China

Microarray data is widely utilized for disease analysis and diagnosis. However, it is hard to process them directly and achieve high classification accuracy due to the intrinsic characteristics of high dimensionality and small size samples. As an important data preprocessing technique, feature selection is usually used to reduce the dimensionality of some datasets. Given the limitations of employing filter or wrapper approaches individually for feature selection, in the study, a novel hybrid filter-wrapper approach is proposed for high dimensional datasets. First, the Chi-square Test is utilized to filter out some irrelevant or redundant features. Next, an improved binary fruit fly optimization algorithm is conducted to further search the optimal feature subset without degrading the classification accuracy. Here, the KNN classifier with the 10-fold-CV is utilized to evaluate the classification accuracy. Extensive experimental results on six benchmark biomedical datasets show that our proposed approach can achieve superior performance compared with other state-of-the-art methods.

关键词： Feature selection fruit fly optimization algorithm Chi-square test levy flight gaussian mutation microarray data

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Energy Saving optimization of Chilled Water System Based on Improved fruit fly optimization algorithm

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JOURNAL OF THERMAL SCIENCE AND ENGINEERING APPLICATIONS 2023年第8期15卷 081010-081010页

作者： Feng, Zengxi Wang, Wenjing He, Xin Li, Gangting Zhang, Lutong Xiang, Weipeng Xian Univ Architecture & Technol Sch Bldg Serv Sci & Engn Xian 710055 Peoples R China

As the main energy consumption part of the central air-conditioning system, the energy saving of the chilled water system is particularly important. In this paper, an improved fruit fly optimization algorithm (IFOA) is used to optimize the operating parameters of the chilled water system to reduce the energy consumption of the chilled water system. In IFOA, the 3-D position coordinate is introduced to expand the search space of the algorithm, the variable-step strategy balances the global search ability and local search ability of the algorithm and helps a single fruit fly jump out of the local optimization through chaos mapping. In order to verify the optimization effect of IFOA on the chilled water system, the energy consumption model of the chilled water system is established. With the lowest total energy consumption of the system as the goal, the operating parameters such as the chilled water supply temperature and the speed ratio of the chilled water pump are optimized. The simulation results show that the energy-saving optimization method of a central air-conditioning chilled water system based on IFOA can make the average energy-saving rate of the system reach 7.9%. Compared with other optimization algorithms, the method has a better energy-saving effect and is more stable.

关键词： chilled water system fruit fly optimization algorithm operating parameters energy consumption model

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An effective fruit fly optimization algorithm for the distributed permutation flowshop scheduling problem with total flowtime

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2023年第PartB期123卷

作者： Guo, Heng-Wei Sang, Hong-Yan Zhang, Xu-Jin Duan, Peng Li, Jun-Qing Han, Yu-Yan Liaocheng Univ Sch Comp Sci Liaocheng Peoples R China

Distributed permutation flowshop scheduling problem (DPFSP) has always been a hot issue. The optimization goal of minimizing the total flowtime is of great significance to the environment of multi-factory. In this paper, a discrete fruit fly optimization algorithm (DFFO) is proposed to solve the DPFSP with the total flowtime criterion. In the proposed DFFO, an initialization method considering the population quality and diversity is adopted. In the smell-based search stage, three perturbation operators, the Shift-based operator, the Exchange-based operator and the Hybrid operator are designed respectively, and each fruit fly improves its state through a specific neighborhood strategy. In addition, we propose an improved reference local search (MRLS) method to enhance the exploitation ability of fruit flies. In the vision-based search stage, fruit flies use a well-designed combination update mechanism to lead fruit flies to more potential areas. In order to enhance the exploration ability, we use random reinforcement method for the population. The parameters are evaluated using an orthogonal experimental design to determine the appropriate values of the key parameters. In addition, we test the DFFO and the state-of-art algorithms from the literature on 720 large-scale instances. The experimental results show that DFFO is a very effective metaheuristic.

关键词： fruit fly optimization algorithm Distributed scheduling Flowshop scheduling Evolution algorithm Total flowtime

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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

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