The traditional Ziegler-Nichols (Z-N) method usually fails to achieve the best control performance for tuning PID parameters. Thus, this paper proposed an immune flyfruitoptimizationalgorithm (IFOA) with the error ...
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ISBN:
(纸本)9781467397148
The traditional Ziegler-Nichols (Z-N) method usually fails to achieve the best control performance for tuning PID parameters. Thus, this paper proposed an immune flyfruitoptimizationalgorithm (IFOA) with the error performance criterion of ITAE as fitness function for the PID parameters optimized. Firstly, the proposed algorithm selected the best fruit flies for immune vaccines in the osphresis search mode. Then, it introduced the immune vaccination and immune selection mechanism in the visual search mode, so as to avoid flyfruitoptimizationalgorithm (FOA) falling into premature, and to overcome the artificial immune algorithm (AIA) shortcomings in the cumbersome and inefficient calculations. Finally, test the performance of the hybrid algorithm with four benchmarks, and apply it in PID parameters tuning. Simulation results show that the IFOA has fast convergence, good stability and higher precision, and also prove the feasibility and effectiveness in PID control parameter optimization.
The expression of the smell concentration judgment value is significantly important in the application of the fruit fly optimization algorithm (FOA). The original FOA can only solve problems that have optimal solution...
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The expression of the smell concentration judgment value is significantly important in the application of the fruit fly optimization algorithm (FOA). The original FOA can only solve problems that have optimal solutions in zero vicinity. To make FOA more universal for the continuous optimization problems, especially for those problems with optimal solutions that are not zero. This paper proposes an improved fruit fly optimization algorithm based on differential evolution (DFOA) by modifying the expression of the smell concentration judgment value and by introducing a differential vector to replace the stochastic search. Through numerical experiments based on 12 benchmark instances, experimental results show that the improved DFOA has a stronger global search ability, faster convergence, and convergence stability in high-dimensional functions than the original FOA and evolutionary algorithms from literature. The DFOA is also applied to optimize the operation of the Texaco gasification process by maximizing the syngas yield using two decision variables, i.e., oxygen-coal ratio and coal concentration. The results show that DFOA can quickly get the optimal output, demonstrating the effectiveness of DFOA. (C) 2015 Elsevier B.V. All rights reserved.
In this paper, a Pareto based fruit fly optimization algorithm (PFOA) is proposed to solve the task scheduling and resource allocating (TSRA) problem in cloud computing environment. First, a heuristic based on the pro...
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ISBN:
(纸本)9781509006229
In this paper, a Pareto based fruit fly optimization algorithm (PFOA) is proposed to solve the task scheduling and resource allocating (TSRA) problem in cloud computing environment. First, a heuristic based on the property of minimum cost is proposed for initializing the population. Second, a resource reassign operator is designed to generate non-dominated solutions. Third, a critical path based search operator is designed to improve the exploitation capability. In addition, the non-dominated sorting technique based on the concept of Pareto optimum is adopted and visual memory is also employed to deal with multiple objectives in solving the TSRA problem by the PFOA. Finally, the effectiveness of the PFOA is demonstrated by the comparative results and statistical analysis by using some test instances.
Range image registration is a popular problem in pattern recognition and computer vision, and it has a wide range of applications in real life. The objective of registration is to match two models as close as possible...
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ISBN:
(纸本)9781509019151
Range image registration is a popular problem in pattern recognition and computer vision, and it has a wide range of applications in real life. The objective of registration is to match two models as close as possible. In this area, the best known iterative closest point (ICP) method is sensitive to the initial position of two models and it is easy stuck in local minima. In recent years, heuristic algorithms have been used for registration with good ability for global searching. However, the features of models are ignored generally in the iterative evolution process, so the tailored methods are lack of versatility for different models registration. In this paper, normal angle histogram is added into the fruit fly optimization algorithm for registration. The searching step of each individual is relative to the initial position of two models. The versatility and effectiveness of proposed algorithm are illustrated by a series of experiments.
Based on the nonlinear and non-stationary characteristics of rotating machinery vibration, a FOA-SVM model is established by fruit fly optimization algorithm (FOA) and combining the Support Vector Machine (SVM) to rea...
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Based on the nonlinear and non-stationary characteristics of rotating machinery vibration, a FOA-SVM model is established by fruit fly optimization algorithm (FOA) and combining the Support Vector Machine (SVM) to realize the optimization of the SVM parameters. The mechanism of this model is imitating the foraging behavior of fruit flies. The smell concentration judgment value of the forage is used as the parameter to construct a proper fitness function in order to search the optimal SVM parameters. The FOA algorithm is proved to be convergence fast and accurately with global searching ability by optimizing the analog signal of rotating machinery fault. In order to improve the classification accuracy rate, built FOA-SVM model, and then to extract feature value for training and testing, so that it can recognize the fault rolling bearing and the degree of it. Analyze and diagnose actual signals, it prove the validity of the method, and the improved method had a good prospect for its application in rolling bearing diagnosis.
Shearers play an important role in fully mechanized coal mining face and accurately identifying their cutting pattern is very helpful for improving the automation level of shearers and ensuring the safety of coal mini...
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Shearers play an important role in fully mechanized coal mining face and accurately identifying their cutting pattern is very helpful for improving the automation level of shearers and ensuring the safety of coal mining. The least squares support vector machine (LSSVM) has been proven to offer strong potential in prediction and classification issues, particularly by employing an appropriate meta-heuristic algorithm to determine the values of its two parameters. However, these meta-heuristic algorithms have the drawbacks of being hard to understand and reaching the global optimal solution slowly. In this paper, an improved flyoptimizationalgorithm (IFOA) to optimize the parameters of LSSVM was presented and the LSSVM coupled with IFOA (IFOA-LSSVM) was used to identify the shearer cutting pattern. The vibration acceleration signals of five cutting patterns were collected and the special state features were extracted based on the ensemble empirical mode decomposition (EEMD) and the kernel function. Some examples on the IFOA-LSSVM model were further presented and the results were compared with LSSVM, PSO-LSSVM, GA-LSSVM and FOA-LSSVM models in detail. The comparison results indicate that the proposed approach was feasible, efficient and outperformed the others. Finally, an industrial application example at the coal mining face was demonstrated to specify the effect of the proposed system.
Range image registration is a popular problem in pattern recognition and computer vision, and it has a wide range of applications in real life. The objective of registration is to match two models as close as possible...
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ISBN:
(纸本)9781509019168
Range image registration is a popular problem in pattern recognition and computer vision, and it has a wide range of applications in real life. The objective of registration is to match two models as close as possible. In this area, the best known iterative closest point (ICP) method is sensitive to the initial position of two models and it is easy stuck in local minima. In recent years, heuristic algorithms have been used for registration with good ability for global searching. However, the features of models are ignored generally in the iterative evolution process, so the tailored methods are lack of versatility for different models registration. In this paper, normal angle histogram is added into the fruit fly optimization algorithm for registration. The searching step of each individual is relative to the initial position of two models. The versatility and effectiveness of proposed algorithm are illustrated by a series of experiments.
Existing researches on lean UX are mostly theoretical elaboration and case studies. First, this paper demonstrated the feasibility and value of studying lean UX in complex adaptive systems(CAS) perspective. Second, to...
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Existing researches on lean UX are mostly theoretical elaboration and case studies. First, this paper demonstrated the feasibility and value of studying lean UX in complex adaptive systems(CAS) perspective. Second, to overcome the uncertainty and volatility of CAS, fruit fly optimization algorithm was applied to orient the emergent properties of CAS. Finally, the simulation framework for lean UX researches in CAS perspective was built based on previous discussion.
Accurate attitude information is needed in the deck of large ship, but it is influenced by the deformation of the deck, the deformation will degrade the performance of the ship-board weapons and equipments. Aim at thi...
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Accurate attitude information is needed in the deck of large ship, but it is influenced by the deformation of the deck, the deformation will degrade the performance of the ship-board weapons and equipments. Aim at this problem, IMUs which are contain laser gyros and accelerators, are installed in the key battle paint of the deck, applying for the estimation of the deformation, but how many IMUs are needed, and where they should be arranged, is a difficult problem for mankind to solve by hand. In this paper, a new proposed optimizationalgorithm named fruit fly optimization algorithm (FOA) is utilized to determine the layout of the IMUs, fitness function for the FOA algorithm is organized by the ship's Modal Assurance Criterion (MAC) matrix. Experimental results show that the FOA algorithm can give an optimal layout of the IMUs for detecting the deformation of the deck. (C) 2014 Elsevier GmbH. All rights reserved.
fruit fly optimization algorithm (FOA) is recently presented metaheuristic technique that is inspired by the behavior of fruit flies. This paper improves the standard FOA by introducing the novel parameter integrated ...
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fruit fly optimization algorithm (FOA) is recently presented metaheuristic technique that is inspired by the behavior of fruit flies. This paper improves the standard FOA by introducing the novel parameter integrated with chaos. The performance of developed chaotic fruitflyalgorithm (CFOA) is investigated in details on ten well known benchmark problems using fourteen different chaotic maps. Moreover, we performed comparison studies with basic FOA, FOA with Levy flight distribution, and other recently published chaotic algorithms. Statistical results on every optimization task indicate that the chaotic fruitflyalgorithm (CFOA) has a very fast convergence rate. In addition, CFOA is compared with recently developed chaos enhanced algorithms such as chaotic bat algorithm, chaotic accelerated particle swarm optimization, chaotic fireflyalgorithm, chaotic artificial bee colony algorithm, and chaotic cuckoo search. Overall research findings show that FOA with Chebyshev map show superiority in terms of reliability of global optimality and algorithm success rate. (C) 2015 Elsevier B.V. All rights reserved.
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