Opposition-based antlion optimizer using Cauchy distribution and its application to data clustering problem

Dinkar, Shail Kumar; Deep, Kusum

doi:10.1007/s00521-019-04174-0

Cited by 18 publications

(4 citation statements)

References 54 publications

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“…Kilic et al [34] replaced the roulette selection method with the tournament strategy and reduced the scale of the random walk of the ant population, which contributes to improving the accuracy and speed of the algorithm. Guo et al proposed an improved antlion optimizer (OB-C-ALO) [35] and used it to solve the data clustering problem. The optimizer uses a random walk based on the Cauchy distribution rather than a uniform distribution to escape from local optimal solutions the local optimal value, and then combines the learning model based on the opposition with the acceleration coefficient, which overcomes the shortcomings of the slow convergence of the original ALO.…”

Section: Related Workmentioning

confidence: 99%

A Chaotic Antlion Optimization Algorithm for Text Feature Selection

Chen

Zhou

Shi

2022

Int J Comput Intell Syst

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Text classification is one of the important technologies in the field of text data mining. Feature selection, as a key step in processing text classification tasks, is used to process high-dimensional feature sets, which directly affects the final classification performance. At present, the most widely used text feature selection methods in academia are to calculate the importance of each feature for classification through an evaluation function, and then select the most important feature subsets that meet the quantitative requirements in turn. However, ignoring the correlation between the features and the effect of their mutual combination in this way may not guarantee the best classification effect. Therefore, this paper proposes a chaotic antlion feature selection algorithm (CAFSA) to solve this problem. The main contributions include: (1) Propose a chaotic antlion algorithm (CAA) based on quasi-opposition learning mechanism and chaos strategy, and compare it with the other four algorithms on 11 benchmark functions. The algorithm has achieved a higher convergence speed and the highest optimization accuracy. (2) Study the performance of CAFSA using CAA for feature selection when using different learning models, including decision tree, Naive Bayes, and SVM classifier. (3) The performance of CAFSA is compared with that of eight other feature selection methods on three Chinese datasets. The experimental results show that using CAFSA can reduce the number of features and improve the classification accuracy of the classifier, which has a better classification effect than other feature selection methods.

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Section: Related Workmentioning

confidence: 99%

A Chaotic Antlion Optimization Algorithm for Text Feature Selection

Chen

Zhou

Shi

2022

Int J Comput Intell Syst

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“…The ATD, which can adaptively adjust the speed factor and filter factor according to the change rate of the input signal, is designed in this paper, to manage the contradiction between filtering performance and tracking speed. Moreover, the LADRC introducing the proposed ATD will consume less energy than the LADRC [34], eliminating the peak value of the control signal and reducing the impact of the control effect on the final controlling element.…”

Section: Proposed Adaptive Tracking Differentiator Designmentioning

confidence: 99%

Linear Active Disturbance Rejection Control-Based Diagonal Recurrent Neural Network for Radar Position Servo Systems with Dead Zone and Friction

2022

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This paper proposes a control scheme for the radar position servo system facing dead zone and friction nonlinearities. The controller consists of the linear active disturbance rejection controller (LADRC) and diagonal recurrent neural network (DRNN). The LADRC is designed to estimate in real time and compensate for the disturbance with vast matched and mismatched uncertainties, including the internal dead zone and friction nonlinearities and external noise disturbance. The DRNN is introduced to optimize the parameters in the linear state error feedback (LSEF) of the LADRC in real time and estimate the model information, namely Jacobian information, of the plant on-line. In addition, considering the Cauchy distribution, an adaptive tracking differentiator (ATD) is designed in order to manage the contradiction between filtering performance and tracking speed, which is introduced to the LADRC. Another novel idea is that the back propagation neuron network (BPNN) is also introduced to tune the parameters of the LADRC, just as in the DRNN, and the comparison results show that the DRNN is more suitable for high precision control due to its feedback structure compared with the static BPNN. Moreover, the regular controller performances and robust performance of the proposed control approach are verified based on the radar position servo system by MATLAB simulations.

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“…Also in [133], [134], the same authors introduced 2 new variant of ALO called OB-ac-ALO, and OB-C-ALO. The first algorithm is based on OBL and acceleration coefficient(ac), while the second use OBL besides Cauchy distribution.…”

Section: ) Lévy Alo and Opposition-based Alomentioning

confidence: 99%

Ant Lion Optimization: Variants, Hybrids, and Applications

2020

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Ant Lion Optimizer (ALO) is a recent novel algorithm developed in the literature that simulates the foraging behavior of a Ant lions. Recently, it has been applied to a huge number of optimization problems. It has many advantages: easy, scalable, flexible, and have a great balance between exploration and exploitation. In this comprehensive study, many publications using ALO have been collected and summarized. Firstly, we introduce an introduction about ALO. Secondly, we categorized the recent versions of ALO into 3 Categories mainly Modified, Hybrid and Multi-Objective. we also introduce the applications in which ALO has been applied such as power, Machine Learning, Image processing problems, Civil Engineering, Medical, etc. The review paper is ended by giving a conclusion of the main ALO foundations and providing some suggestions & possible future directions that can be investigated.

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Opposition-based antlion optimizer using Cauchy distribution and its application to data clustering problem

Cited by 18 publications

References 54 publications

A Chaotic Antlion Optimization Algorithm for Text Feature Selection

A Chaotic Antlion Optimization Algorithm for Text Feature Selection

Linear Active Disturbance Rejection Control-Based Diagonal Recurrent Neural Network for Radar Position Servo Systems with Dead Zone and Friction

Ant Lion Optimization: Variants, Hybrids, and Applications

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