Due to the inaccurate and unreliable moving distance measurement of the hydraulic support in mines, a method based on the random circle detection (RCD) algorithm and the fruit fly optimization algorithm (FOA) is propo...
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Due to the inaccurate and unreliable moving distance measurement of the hydraulic support in mines, a method based on the random circle detection (RCD) algorithm and the fruit fly optimization algorithm (FOA) is proposed. According to the changing center and radium of the circle on the support, the relative position of adjacent supports is acquired by the camera. The noise of the collected image is moved, and the edge feature is protected using a bilateral filter. A local adaptive threshold algorithm is used for binary processing of the image. Then, RCD is used to detect the contour, which is similar to the circle. A method to detect the circle based on FOA is used to accurately detect the circle. Subsequently, the relative distance is calculated according to the change of the circle. Finally, the accuracy and reliability of the proposed method are verified though the experiment. (C) 2016 Society of Photo-Optical Instrumentation Engineers (SPIE)
The fruit fly optimization algorithm (FOA) is one of the latest swarm intelligence-based methods inspired by the foraging behavior of fruitfly swarm. To improve the global search ability and solution accuracy of the ...
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The fruit fly optimization algorithm (FOA) is one of the latest swarm intelligence-based methods inspired by the foraging behavior of fruitfly swarm. To improve the global search ability and solution accuracy of the FOA, a bimodal adaptive fruit fly optimization algorithm using normal cloud learning (BCMFOA) is proposed in this paper. Based on the labor allocation characteristics of the swarm foraging behavior, the fruitfly population is divided into two groups in the optimization process according to their duties of searching or capturing. The search group is mainly based on the fruitfly's olfactory sensors to find possible global optima in a large range, while the capture group makes use of their keen visions to exploit neighborhood of the current best food source found by the search group. Moreover, the randomness and fuzziness of the foraging behavior of fruitfly swarm during the olfactory phase are described by a normal cloud model. Using a normal cloud generator and an adaptive parameter updation strategy, the search range of the fruitfly population is adaptively adjusted. Therefore, the ability of FOA to avoid local optima is enhanced greatly. Twenty-three benchmark functions are used to test the performance of the proposed BCMFOA algorithm. Numerical results show that the proposed method can significantly improve the global search ability and solution accuracy of FOA. Compared with existing methods such as PSO, DE, AFAS, the experimental results indicate that BCMFOA has better or comparative convergence performance and accuracy. The application to the multi-parameter estimation of a permanent magnet synchronous motor further confirms its good performance.
This paper presents an improved fruit fly optimization algorithm (IFFOA) for solving the multidimensional knapsack problem (MKP). In IFFOA, the parallel search is employed to balance exploitation and exploration. To m...
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This paper presents an improved fruit fly optimization algorithm (IFFOA) for solving the multidimensional knapsack problem (MKP). In IFFOA, the parallel search is employed to balance exploitation and exploration. To make full use of swarm intelligence, a modified harmony search algorithm (MHS) is proposed and applied to add cooperation among swarms in IFFOA. In MHS, novel pitch adjustment scheme and random selection rule are developed by considering specific characters of MKP and FOA. Moreover, a vertical crossover is designed to guide stagnant dimensions out of local optima and further improve the performance. Extensive numerical simulations are conducted and comparisons with other state-of-the-art algorithms verify that the proposed algorithm is an effective alternative for solving the MKP. (C) 2016 Elsevier B.V. All rights reserved.
Fuzzy clustering has emerged as an important tool for discovering the structure of data. Kernel based clustering has emerged as an interesting and quite visible alternative in fuzzy clustering. Aimed at the problems o...
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ISBN:
(纸本)9781509066681
Fuzzy clustering has emerged as an important tool for discovering the structure of data. Kernel based clustering has emerged as an interesting and quite visible alternative in fuzzy clustering. Aimed at the problems of both a local optimum and depending on initialization strongly in the fuzzy c-means clustering algorithm (FCM), a method of kernel-based fuzzy c-means clustering based on fruitflyalgorithms (FOAKFCM) is proposed in this paper. In this algorithm, the fruitflyalgorithm is used to optimize the initial clustering center firstly, kernel-based fuzzy c-means clustering algorithm (KFCM) is used to classify data. At the same time we reference classification evaluation index to choose the fuzziness parameter in adaptive way. The clustering performance of FCM algorithm, KFCM algorithm, and the proposed algorithm is testified by test datasets. FCM algorithm and FOAKFCM are used for power load characteristic data classification, respectively. Experiment results show that FOAKFCM algorithm proposed overcomes FCM's defects efficiently and improves the clustering performance greatly.
A fault diagnosis method using improved pattern spectrum and fruit fly optimization algorithm-support vector machine is proposed. Improved pattern spectrum is introduced for feature extraction by employing morphologic...
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A fault diagnosis method using improved pattern spectrum and fruit fly optimization algorithm-support vector machine is proposed. Improved pattern spectrum is introduced for feature extraction by employing morphological erosion operator. Simulation analysis is processed, and the improved pattern spectrum curves present a steady distinction feature and smaller calculating amount than pattern spectrum method. Support vector machine with fruit fly optimization algorithm which can help seeking optimal parameters is employed for pattern recognition. Experiments were conducted, and the proposed method is verified by roller bearing vibration data including different fault types. The classification accuracy of the proposed approach reaches 87.5% (21/24) in training and 91.7% (44/48) in testing, showing an acceptable diagnosis effect.
With the unveiling of the "Made in China 2025" plan, the future of manufacturing has been transformed from "made in China" to "intelligent manufacturing in China" and it means that the ne...
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With the unveiling of the "Made in China 2025" plan, the future of manufacturing has been transformed from "made in China" to "intelligent manufacturing in China" and it means that the new era of intelligent technology has come. However, there is still not a specific and effective model for predicting enterprises' operating performance because the development of intelligent technology industry in China started late. Therefore, this study applies fruit fly optimization algorithm to optimize multiple regression and construct the most appropriate model which can effectively predict the enterprises' operating performance of intelligent technology industry in China. The result shows it has good ability to optimize multiple regression by fruit fly optimization algorithm and obviously enhance the prediction performance.
As a new optimizationalgorithm,fruit fly optimization algorithm(FOA) attracts a lot of *** analyzing the probability of FOA jumping out of the local optimal range,we verified that FOA is ineffective in solving comple...
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As a new optimizationalgorithm,fruit fly optimization algorithm(FOA) attracts a lot of *** analyzing the probability of FOA jumping out of the local optimal range,we verified that FOA is ineffective in solving complex optimization problems whose optimal solution is *** order to improve the performance of FOA,a Modified Global fruit fly optimization algorithm(MGFOA) is introduced in this *** MGFOA,a uniform mechanism to produce the candidate solution is used to improve the global searching ability,a self-adaptive way to control the flight range is adapted to increase the optimize accuracy,and a ladder growth way of population is introduced to imitate the detection behavior of fruit *** experiment on 12 benchmark functions shows that MGFOA is more effective and robust than basic FOA,Global Particle Swarm optimizationalgorithm(GPSO) and another improved FOA(LGMS-FOA).
Accurate short-term traffic forecasting can relieve traffic congestion and improve the mobility of transportation, which is very important for management modernization of transportation systems. However, it is quite d...
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Accurate short-term traffic forecasting can relieve traffic congestion and improve the mobility of transportation, which is very important for management modernization of transportation systems. However, it is quite difficult to predict effectively and accurately short-term traffic information since traffic information shows the strongly nonlinear, complex and uncertain characteristics. In this paper, a new hybrid model based on grey neural network and fruit fly optimization algorithm (FOA) is proposed to solve this problem. The FOA is used to select the appropriate parameter values of the grey neural network model, thereby improving the accuracy of forecasting model. The proposed hybrid model can exploit sufficiently the characteristics of grey system model requiring less data, the non-linear map of neural networks and the quick-speed convergence of FOA, and has simpler structure. The effectiveness of this proposed hybrid model is proved by experiment simulation. The experiment results show that the proposed model has better performance than the single grey model GM(1,1), the single back-propagation neural network (BPNN) model, the combined model of them, i.e., the grey neural network (GNN) model, and the GNN model with particle swarm optimization (GNN-PSO), on short-term traffic forecasting.
In this paper, a novel improved fruit fly optimization algorithm（IFOA） is proposed for solving the multidimensional knapsack problem（MKP）, which is characterized as high dimension and strong constraint. Initial sw...
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In this paper, a novel improved fruit fly optimization algorithm（IFOA） is proposed for solving the multidimensional knapsack problem（MKP）, which is characterized as high dimension and strong constraint. Initial swarms are generated according to the probability vector respectively. After the smell-based searching accomplishing, a repair operator granded on the pseudo-utility ratio, which is calculated by solving the dual problem of linear programming relaxion of MKP, is applied to guarantee the feasibility and enhance the quality of solutions. A swarm reduction strategy is used to balance the searching ability and convergence speed. Numerous tests and comparison with other algorithms based on two sets of benchmark problems demonstrate that IFOA is an efficient algorithm to solve MKP.
This paper designs a novel path planner for unmanned aerial vehicle (UAV) in the three-dimensional terrain environment using the fruit fly optimization algorithm (FOA). The UAV path planning problem is formulated as a...
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ISBN:
(纸本)9781509067596
This paper designs a novel path planner for unmanned aerial vehicle (UAV) in the three-dimensional terrain environment using the fruit fly optimization algorithm (FOA). The UAV path planning problem is formulated as an optimization problem, using the B-Spline curve to represent flight paths. The cost function adopting herein to evaluate the flight path contains multiple optimization indexes and performance constraints. Detailed process of the FOA-based path planner is proposed to find the optimal path with the minimum cost function value. Numerical simulations are carried out and the results show that FOA is the powerful optimization technique in solving UAV path planning problem.
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