An Improved Bat Algorithm Based on Hybrid with Ant Lion Optimizer

2020 ◽  
pp. 50-60
Author(s):  
Thi-Kien Dao ◽  
Shu-Chuan Chu ◽  
Jeng-Shyang Pan ◽  
Trong-The Nguyen ◽  
Truong-Giang Ngo ◽  
...  
Keyword(s):  
Bat Algorithm ◽  
Ant Lion Optimizer ◽  
Ant Lion

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.

scholarly journals Improved Backstepping Control of a DFIG based Wind Energy Conversion System using Ant Lion Optimizer Algorithm

2021 ◽  
Author(s):  
Zoubir Zeghdi ◽  
Linda Barazane ◽  
Youcef Bekakra ◽  
Abdelkader Larabi
Keyword(s):  
Fitness Function ◽  
Harmonic Distortion ◽  
Absolute Error ◽  
Optimization Method ◽  
Backstepping Control ◽  
Ant Lion Optimizer ◽  
Integral Time ◽  
Distortion Minimization ◽  
Ant Lion ◽  
Doubly Fed

In this paper, an improved Backstepping control based on a recent optimization method called Ant Lion Optimizer (ALO) algorithm for a Doubly Fed Induction Generator (DFIG) driven by a wind turbine is designed and presented. ALO algorithm is applied for obtaining optimum Backstepping control (BCS) parameters that are able to make the drive more robust with a faster dynamic response, higher accuracy and steady performance. The fitness function of the ALO algorithm to be minimized is designed using some indexes criterion like Integral Time Absolute Error (ITAE) and Integral Time Square Error (ITSE). Simulation tests are carried out in MATLAB/Simulink environment to validate the effectiveness of the proposed BCS-ALO and compared to the conventional BCS control. The results prove that the objectives of this paper were accomplished in terms of robustness, better dynamic efficiency, reduced harmonic distortion, minimization of stator powers ripples and performing well in solving the problem of uncertainty of the model parameter.

scholarly journals Approach of economic‐emission load dispatch using Ant Lion Optimizer

2018 ◽  
Vol 5 (7) ◽  
pp. 184-190 ◽  
Author(s):  
Jorge de Almeida Brito Júnior ◽  
Manoel Henrique Reis Nascimento ◽  
Carlos Alberto Oliveira de Freitas ◽  
Jandecy Cabral Leite ◽  
Tirso Lorenzo Reyes Carvajal
Keyword(s):  
Ant Lion Optimizer ◽  
Ant Lion

Ant Lion Optimizer (ALO) Algorithm

2017 ◽  
pp. 105-116 ◽  
Author(s):  
Melika Mani ◽  
Omid Bozorg-Haddad ◽  
Xuefeng Chu
Keyword(s):  
Ant Lion Optimizer ◽  
Ant Lion
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