Feature Selection by Hybrid Binary Ant Lion Optimizer with COVID-19 dataset

2021 ◽  
Author(s):  
Ivana Strumberger ◽  
Andjela Rakic ◽  
Stefan Stanojlovic ◽  
Jelena Arandjelovic ◽  
Timea Bezdan ◽  
...  
Keyword(s):  
Feature Selection ◽  
Ant Lion Optimizer ◽  
Ant Lion

Hybrid binary Sine Cosine Algorithm and Ant Lion Optimization (SCALO) approaches for feature selection problem

2020 ◽  
Vol 09 (01) ◽  
pp. 1950021
Author(s):  
Rahul Hans ◽  
Harjot Kaur
Keyword(s):  
Feature Selection ◽  
Optimization Problem ◽  
Transfer Functions ◽  
Selection Problem ◽  
Feature Selection Problem ◽  
Ant Lion Optimization ◽  
Ant Lion Optimizer ◽  
Sine Cosine Algorithm ◽  
Ant Lion ◽  
Better Than

These days, a massive quantity of data is produced online and is incorporated into a variety of datasets in the form of features, however there are lot of features in these datasets that may not be relevant to the problem. In this perspective, feature selection aids to improve the classification accuracy with lesser number of features, which can be well thought-out as an optimization problem. In this paper, Sine Cosine Algorithm (SCA) hybridized with Ant Lion Optimizer (ALO) to form a hybrid Sine Cosine Ant Lion Optimizer (SCALO) is proposed. The proposed algorithm is mapped to its binary versions by using the concept of transfer functions, with the objective to eliminate the inappropriate features and to enhance the accuracy of the classification algorithm (or in any case remains the same). For the purpose of experimentation, this research considers 18 diverse datasets and moreover, the performance of the binary versions of SCALO is compared with some of the latest metaheuristic algorithms, on the basis of various criterions. It can be observed that the binary versions of SCALO perform better than the other algorithms on various evaluation criterions for solving feature selection problem.

A feature selection approach for hyperspectral image based on modified ant lion optimizer

2019 ◽  
Vol 168 ◽  
pp. 39-48 ◽  
Author(s):  
Mingwei Wang ◽  
Chunming Wu ◽  
Lizhe Wang ◽  
Daxiang Xiang ◽  
Xiaohui Huang
Keyword(s):  
Feature Selection ◽  
Hyperspectral Image ◽  
Ant Lion Optimizer ◽  
Selection Approach ◽  
Ant Lion ◽  
Feature Selection Approach

Preventive maintenance scheduling using analysis of variance-based ant lion optimizer

2018 ◽  
Vol 15 (2) ◽  
pp. 254-272 ◽  
Author(s):  
Umamaheswari Elango ◽  
Ganesan Sivarajan ◽  
Abirami Manoharan ◽  
Subramanian Srikrishna
Keyword(s):  
Analysis Of Variance ◽  
Heuristic Algorithm ◽  
Heuristic Algorithms ◽  
Statistical Tests ◽  
Population Based ◽  
Maintenance Scheduling ◽  
Content Type ◽  
Ant Lion Optimizer ◽  
Ant Lion ◽  
Selection Of

Purpose Generator maintenance scheduling (GMS) is an essential task for electric power utilities as the periodical maintenance activity enhances the lifetime and also ensures the reliable and continuous operation of generating units. Though numerous meta-heuristic algorithms have been reported for the GMS solution, enhancing the existing techniques or developing new optimization procedure is still an interesting research task. The meta-heuristic algorithms are population based and the selection of their algorithmic parameters influences the quality of the solution. This paper aims to propose statistical tests guided meta-heuristic algorithm for solving the GMS problems. Design/methodology/approach The intricacy characteristics of the GMS problem in power systems necessitate an efficient and robust optimization tool. Though several meta-heuristic algorithms have been applied to solve the chosen power system operational problem, tuning of their control parameters is a protracting process. To prevail over the previously mentioned drawback, the modern meta-heuristic algorithm, namely, ant lion optimizer (ALO), is chosen as the optimization tool for solving the GMS problem. Findings The meta-heuristic algorithms are population based and require proper selection of algorithmic parameters. In this work, the ANOVA (analysis of variance) tool is proposed for selecting the most feasible decisive parameters in algorithm domain, and the statistical tests-based validation of solution quality is described. The parametric and non-parametric statistical tests are also performed to validate the selection of ALO against the various competing algorithms. The numerical and statistical results confirm that ALO is a promising tool for solving the GMS problems. Originality/value As a first attempt, ALO is applied to solve the GMS problem. Moreover, the ANOVA-based parameter selection is proposed and the statistical tests such as Wilcoxon signed rank and one-way ANOVA are conducted to validate the applicability of the intended optimization tool. The contribution of the paper can be summarized in two folds: the ANOVA-based ALO for GMS applications and statistical tests-based performance evaluation of intended algorithm.

scholarly journals An Ant-Lion Optimizer-Trained Artificial Neural Network System for Chaotic Electroencephalogram (EEG) Prediction

2018 ◽  
Vol 8 (9) ◽  
pp. 1613 ◽  
Author(s):  
Utku Kose
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Artificial Neural Network ◽  
Time Series Prediction ◽  
Optimization Technique ◽  
Mental States ◽  
Target Time ◽  
Ant Lion Optimizer ◽  
Ant Lion ◽  
Artificial Neural

The prediction of future events based on available time series measurements is a relevant research area specifically for healthcare, such as prognostics and assessments of intervention applications. A measure of brain dynamics, electroencephalogram time series, are routinely analyzed to obtain information about current, as well as future, mental states, and to detect and diagnose diseases or environmental factors. Due to their chaotic nature, electroencephalogram time series require specialized techniques for effective prediction. The objective of this study was to introduce a hybrid system developed by artificial intelligence techniques to deal with electroencephalogram time series. Both artificial neural networks and the ant-lion optimizer, which is a recent intelligent optimization technique, were employed to comprehend the related system and perform some prediction applications over electroencephalogram time series. According to the obtained findings, the system can successfully predict the future states of target time series and it even outperforms some other hybrid artificial neural network-based systems and alternative time series prediction approaches from the literature.

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