Parameter identification of BIPT system using chaotic-enhanced fruit fly optimization algorithm

Yuan, Xiaofang; Liu, Yuanming; Xiang, Yongzhong; Yan, Xing‐Gang

doi:10.1016/j.amc.2015.07.030

Cited by 24 publications

(13 citation statements)

References 32 publications

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“…Parameter estimation plays an important role in bidirectional inductive power transfer (BIPT) systems. To obtain a proper parameter setting for this system, an improved algorithm using chaotic PSO to enhance the original FOA has been proposed [36], namely, CFOA, and the experimental results showed that the 11 parameters of this system were determined properly. In certain real-world problems, such as joint replenishment problems (JRPs), FOA has also been applied.…”

Section: Introductionmentioning

confidence: 99%

An improved chaotic fruit fly optimization based on a mutation strategy for simultaneous feature selection and parameter optimization for SVM and its applications

Lou

Sun

2017

PLoS ONE

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This paper proposes a new support vector machine (SVM) optimization scheme based on an improved chaotic fly optimization algorithm (FOA) with a mutation strategy to simultaneously perform parameter setting turning for the SVM and feature selection. In the improved FOA, the chaotic particle initializes the fruit fly swarm location and replaces the expression of distance for the fruit fly to find the food source. However, the proposed mutation strategy uses two distinct generative mechanisms for new food sources at the osphresis phase, allowing the algorithm procedure to search for the optimal solution in both the whole solution space and within the local solution space containing the fruit fly swarm location. In an evaluation based on a group of ten benchmark problems, the proposed algorithm’s performance is compared with that of other well-known algorithms, and the results support the superiority of the proposed algorithm. Moreover, this algorithm is successfully applied in a SVM to perform both parameter setting turning for the SVM and feature selection to solve real-world classification problems. This method is called chaotic fruit fly optimization algorithm (CIFOA)-SVM and has been shown to be a more robust and effective optimization method than other well-known methods, particularly in terms of solving the medical diagnosis problem and the credit card problem.

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Section: Introductionmentioning

confidence: 99%

An improved chaotic fruit fly optimization based on a mutation strategy for simultaneous feature selection and parameter optimization for SVM and its applications

Lou

Sun

2017

PLoS ONE

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“…Fruit fly optimization algorithm (FFOA) is a new swarm optimization algorithm which is inspired by the intrinsic behaviour of food search in fruit fly swarm [17][18][19][20]. The FFOA has been successfully adopted to deal with multi-objective optimization and scheduling problems.…”

Section: Introductionmentioning

confidence: 99%

Self-adaptive step fruit fly algorithm optimized support vector regression model for dynamic response prediction of magnetorheological elastomer base isolator

et al. 2016

Neurocomputing

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Parameter optimization of support vector regression (SVR) plays a challenging role in improving the generalization ability of machine learning. Fruit fly optimization algorithm (FFOA) is a recently developed swarm optimization algorithm for complicated multi-objective optimization problems and is also suitable for optimizing SVR parameters. In this work, parameter optimization in SVR using FFOA is investigated. In view of problems of premature and local optimum in FFOA, an improved FFOA algorithm based on self-adaptive step update strategy (SSFFOA) is presented to obtain the optimal SVR model. Moreover, the proposed method is utilized to characterize magnetorheological elastomer (MRE) base isolator, a typical hysteresis device. In this application, the obtained displacement, velocity and current level are used as SVR inputs while the output is the shear force response of the device. Experimental testing of the isolator with two types of excitations is applied for model performance evaluation. The results demonstrate that the proposed SSFFOA-optimized SVR (SSFFOA_SVR) has perfect generalization ability and more accurate prediction accuracy than other machine learning models, and it is a suitable and effective method to predict the dynamic behaviour of MRE isolator. Keywords Support vector regression, fruit fly optimization algorithm, self-adaptive step, magnetorheological elastomer base isolator, dynamic response prediction Recently, the swarm intelligence optimization algorithms were proposed to tackle optimization problems, which were also applied to optimize the SVR parameters, such as particle swarm optimization (PSO), ant colony optimization (ACO), bee colony algorithm (BCA), firefly algorithm (FA) and artificial fish swarm algorithm (AFSA). In [14], Li et al. put forward a hybrid self-adaptive learning approach based on SVR

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“…To improve the search efficiency and global search ability, researchers designed several improved FOAs [2].Yuan et.al [14] proposed a novel CFOA (chaoticenhanced fruit fly optimization algorithm), which employs chaotic sequence to enhance the global optimization capacity of original FOA. Wang et.al [15] introduced AM-FOA (adaptive mutation fruit fly optimization algorithm).…”

Section: Literature Reviewmentioning

confidence: 99%

An improved fruit fly optimization algorithm based on selecting evolutionary direction intelligently *

Wu¹,

Xiao²,

Liang³

et al. 2016

IJCIS

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As a novel global optimization algorithm, the fruit fly optimization algorithm FOA has been successfully applied in a variety of mathematic and engineering fields. For the purpose of accelerating the convergence speed and overcoming the shortcomings of FOA, an improved fruit fly optimization called SEDI-FOA was proposed in this paper. In the proposed SEDI-FOA, more fruit flies would fly in the search direction that was best for finding the optimal solution, or at least in a direction close to the optimal direction. Experiments were conducted on a set of 12 benchmark functions, and the results showed that SEDI-FOA performed better than other several improved FOA and frequently-used intelligence algorithms, especially in the areas of accelerating convergence and global search ability and efficiency.

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Parameter identification of BIPT system using chaotic-enhanced fruit fly optimization algorithm

Cited by 24 publications

References 32 publications

An improved chaotic fruit fly optimization based on a mutation strategy for simultaneous feature selection and parameter optimization for SVM and its applications

An improved chaotic fruit fly optimization based on a mutation strategy for simultaneous feature selection and parameter optimization for SVM and its applications

Self-adaptive step fruit fly algorithm optimized support vector regression model for dynamic response prediction of magnetorheological elastomer base isolator

An improved fruit fly optimization algorithm based on selecting evolutionary direction intelligently *

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