A Complex-Valued Encoding Moth-Flame Optimization Algorithm for Global Optimization

Wang, Pengchuan; Zhou, Yongquan; Luo, Qifang; Fan, Cheng-Qi; Xiang, Zhehong

doi:10.1007/978-3-030-26763-6_69

Cited by 4 publications

(3 citation statements)

References 11 publications

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“…2: random initialization of moth positions in the search space M. 3: While (l <= T) 4: Calculate the fitness of each moth OM. 5: Calculate the number of flames using Equation (29). 6: If the current iteration number l = 1, then F = sort(M) update the flame population according to.…”

Section: ) Population Initializationmentioning

confidence: 99%

“…Although some adaptive stochastic resonance methods have been proposed [ 22 , 23 , 24 ], these improvement algorithms (i.e., genetic algorithms, particle swarm improvement, and grid search methods) still suffer from the problem of limited global optimization capability [ 25 , 26 , 27 , 28 ]. Considering that the performance and efficiency of the adaptive stochastic resonance method will be directly affected by the global optimization capability of an optimization algorithm, the moth flame improvement algorithm [ 29 ]—which is simple, flexible, robust, and close to the global optimum—was selected and improved upon. Moreover, the model’s effectiveness (with the adaptive underdamped tri-stable stochastic resonant system proposed in this paper) in extracting weak magnetic signals was verified through simulated and experimental weak magnetic signals.…”

Section: Introductionmentioning

confidence: 99%

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Adaptive Stochastic Resonance-Based Processing of Weak Magnetic Slippage Signals of Bearings

Zhang

2022

Entropy

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Slip is one of the most common forms of failure in aviation bearings, and it can pose a great threat to the stable operation of aviation bearings. Bearing cage speed monitoring methods based on weak magnetic detection can achieve nondestructive measurements. However, the method suffers from solid signal background noise due to the high sensitivity of the sensor. Therefore, in this paper, an adaptive stochastic resonance algorithm was proposed in response to the characteristics of the weak magnetic detection signal and the problem of solid noise. In addition, by adaptively adjusting the coefficients of the stochastic resonance system—by an improved moth flame optimization algorithm—the drawback in which the stochastic resonance method required artificially set parameters for extracting the feature frequencies of the weak magnetic signals was solved. In this process, we used parameters, such as general refined composite multi-scale sample entropy, as the adaptation function of the optimization algorithm. In the end, simulation and experimental outcomes verified the efficacy of the approach put forward.

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Section: ) Population Initializationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Adaptive Stochastic Resonance-Based Processing of Weak Magnetic Slippage Signals of Bearings

Zhang

2022

Entropy

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“…Considering the optimization algorithm for feature detection of network intrusion detection, the moth-flame optimization (MFO) [12] algorithm is an intelligent optimization algorithm which originates from the behavior simulation of moth flying around the flame. Nowadays, the MFO algorithm is deeply studied to solve multi-objective problems, unconstrained optimization problems, global optimization problems, and so on [13][14][15][16][17][18]. The MFO algorithm has been proved to be effective in networks [19,20], manufacturing [21], power systems [22][23][24], control [25], energy [26][27][28], reliability analysis [29][30][31], autonomous robot navigation [32], testing [33], photovoltaic modules [34], biomedical science [35][36][37], and so on.…”

Section: Related Workmentioning

confidence: 99%

A Combination Strategy of Feature Selection Based on an Integrated Optimization Algorithm and Weighted K-Nearest Neighbor to Improve the Performance of Network Intrusion Detection

Przystupa

Fang

et al. 2020

Electronics

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With the widespread use of the Internet, network security issues have attracted more and more attention, and network intrusion detection has become one of the main security technologies. As for network intrusion detection, the original data source always has a high dimension and a large amount of data, which greatly influence the efficiency and the accuracy. Thus, both feature selection and the classifier then play a significant role in raising the performance of network intrusion detection. This paper takes the results of classification optimization of weighted K-nearest neighbor (KNN) with those of the feature selection algorithm into consideration, and proposes a combination strategy of feature selection based on an integrated optimization algorithm and weighted KNN, in order to improve the performance of network intrusion detection. Experimental results show that the weighted KNN can increase the efficiency at the expense of a small amount of the accuracy. Thus, the proposed combination strategy of feature selection based on an integrated optimization algorithm and weighted KNN can then improve both the efficiency and the accuracy of network intrusion detection.

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Moth Flame Optimization: Developments and Challenges up to 2020

Nayak

Vakula²,

Dinesh³

et al. 2020

Computational Intelligence in Pattern Recognition

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A Complex-Valued Encoding Moth-Flame Optimization Algorithm for Global Optimization

Cited by 4 publications

References 11 publications

Adaptive Stochastic Resonance-Based Processing of Weak Magnetic Slippage Signals of Bearings

Adaptive Stochastic Resonance-Based Processing of Weak Magnetic Slippage Signals of Bearings

A Combination Strategy of Feature Selection Based on an Integrated Optimization Algorithm and Weighted K-Nearest Neighbor to Improve the Performance of Network Intrusion Detection

Moth Flame Optimization: Developments and Challenges up to 2020

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