A novel improved fruit fly optimization algorithm for aerodynamic shape design optimization

Tian, Xu; Li, Jie

doi:10.1016/j.knosys.2019.05.005

Cited by 37 publications

(9 citation statements)

References 29 publications

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“…Then, the smell value of all nine combinations of fruit fly individuals can be obtained on the basis of the fitness function (31). The fruit fly individual corresponding to the best value of smell among the nine individuals will be selected as the new fruit fly individual.…”

Section: Olfoa Stepsmentioning

confidence: 99%

Advanced Fruit Fly Optimization Algorithm and Its Application to Irregular Subarray Phased Array Antenna Synthesis

Zhang

Shi

2019

IEEE Access

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In this paper, an advanced fruit fly algorithm (FOA) is proposed and applied in subarray phased array antenna synthesis. The proposed algorithm introduces orthogonal crossover, quantum selection and simulated annealing operations on the individuals, and then combines them by using an adaptive expansion-contraction factor. Accordingly, a linear generation mechanism of candidate solution based fruit fly algorithm (LGMS-FOA) is generated, in which individuals are selected in a highly balanced way, and the poor solutions are still accepted with a varying probability during the iteration. These mechanisms help the proposed algorithm enhance the population diversity and global searching capability but avoid falling into local optimum. Numerical classical unimodel benchmark functions are provided to test the proposed algorithm (OLFOA) in comparison with other advanced algorithms. In addition, to further validate its superiority, the proposed algorithm is applied to handle the subarray array synthesis of several tough planar and circular apertures with different array sizes and subarray shapes. Simulation results show that the proposed OLFOA can achieve better performance than other improved evolutionary algorithms.

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Section: Olfoa Stepsmentioning

confidence: 99%

Advanced Fruit Fly Optimization Algorithm and Its Application to Irregular Subarray Phased Array Antenna Synthesis

Zhang

Shi

2019

IEEE Access

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“…First, we must choose the unit structuring element as well as the maximum analytical scale; calculate features based on IP S; and then, in order to reduce the dimensions of IP S curve, calculate five statistic parameters and constitute the feature vectors, which include the maximum value, the minimum value, the average value, the geometric mean, and the standard deviation. On this basis, F OA (fruit fly optimization algorithm) [17][18][19] is introduced in SV M parameters optimization using feature vectors of training samples. Finally, the fault types will be diagnosed by the optimized SV M model.…”

Section: Fault Diagnosis Flow Using Ips and Foa-svmmentioning

confidence: 99%

A Fault Diagnosis Method Based on Improved Pattern Spectrum and FOA-SVM

Sun¹,

Wang²,

Xiong³

et al. 2019

The International Journal of Acoustics and Vibration

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A fault diagnosis method using improved pattern spectrum (IP S) and F OA−SV M is proposed. Improved pattern spectrum is introduced for feature extraction by employing morphological erosion operator, and this feature is able to present fault information for roller bearing on different scales. Simulation analysis is processed and shows that, the value of IP S has a steady distinction among different fault types and the calculating amount is less than traditional method. After feature extraction, SV M with F OA, which can help with 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 on training is 87.5% ( 21 24 ) and reaches 91.7% ( 44 48 ) in a testing data set. The analysis shows that the method has a good diagnosis effect and an acceptable recognition result.

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“…As becoming more and more sophisticated, the various optimization issues are extensively solved using the promising stochastic bioinspired algorithms [5][6][7][8][9]. This kind of algorithm is unsensitive to the searching magnitude and has strong optimization capability and flexibility of finding high-quality solution efficiently through a brief process, even for highdimensional difficult problems [4,[10][11][12][13][14][15][16][17][18]. A great number of well-regarded bioinspired optimizers, such as Differential Evolution (DE) [19], Particle Swarm Optimization (PSO) [8], Sine Cosine Algorithm (SCA) [20], Grey Wolf Optimizer (GWO) [21], Moth-Flame Optimization (MFO) [22], Whale Optimization Algorithm (WOA) [23], Bat Algorithm (BA) [24], Gravitational Search Algorithm (GSA) [25], Harris Hawks Optimizer (HHO) [26], Slime Mould Algorithm (SMA) [27], Beluga Whale Optimization (BWO) [28], Dandelion Optimizer (DO) [29], Marine Predators Algorithm (MPA) [30], and their variants have been presented to handle a various optimization problems.…”

Section: Introductionmentioning

confidence: 99%

Crisscross Harris Hawks Optimizer for Global Tasks and Feature Selection

et al. 2022

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Harris Hawks Optimizer (HHO) is a recent well-established optimizer based on the hunting characteristics of Harris hawks, which shows excellent efficiency in solving a variety of optimization issues. However, it undergoes weak global search capability because of the levy distribution in its optimization process. In this paper, a variant of HHO is proposed using Crisscross Optimization Algorithm (CSO) to compensate for the shortcomings of original HHO. The novel developed optimizer called Crisscross Harris Hawks Optimizer (CCHHO), which can effectively achieve high-quality solutions with accelerated convergence on a variety of optimization tasks. In the proposed algorithm, the vertical crossover strategy of CSO is used for adjusting the exploitative ability adaptively to alleviate the local optimum; the horizontal crossover strategy of CSO is considered as an operator for boosting explorative trend; and the competitive operator is adopted to accelerate the convergence rate. The effectiveness of the proposed optimizer is evaluated using 4 kinds of benchmark functions, 3 constrained engineering optimization issues and feature selection problems on 13 datasets from the UCI repository. Comparing with nine conventional intelligence algorithms and 9 state-of-the-art algorithms, the statistical results reveal that the proposed CCHHO is significantly more effective than HHO, CSO, CCNMHHO and other competitors, and its advantage is not influenced by the increase of problems’ dimensions. Additionally, experimental results also illustrate that the proposed CCHHO outperforms some existing optimizers in working out engineering design optimization; for feature selection problems, it is superior to other feature selection methods including CCNMHHO in terms of fitness, error rate and length of selected features. Supplementary Information The online version contains supplementary material available at 10.1007/s42235-022-00298-7.

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A novel improved fruit fly optimization algorithm for aerodynamic shape design optimization

Cited by 37 publications

References 29 publications

Advanced Fruit Fly Optimization Algorithm and Its Application to Irregular Subarray Phased Array Antenna Synthesis

Advanced Fruit Fly Optimization Algorithm and Its Application to Irregular Subarray Phased Array Antenna Synthesis

A Fault Diagnosis Method Based on Improved Pattern Spectrum and FOA-SVM

Crisscross Harris Hawks Optimizer for Global Tasks and Feature Selection

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