An enhanced Bat algorithm with mutation operator for numerical optimization problems

Ghanem, Waheed Ali H. M.; Jantan, Aman

doi:10.1007/s00521-017-3021-9

Cited by 27 publications

(12 citation statements)

References 34 publications

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“…A binary BAT algorithm was leveraged for feature selection for steganalysing of images [82]. An enhanced BAT (EBat) algorithm was presented by [83] to address the challenge of local optima trapping based on a special mutation operator that enhances the diversity of the standard BAT method.…”

Section: It Takes Into Cognizant the Interaction Of Featuresmentioning

confidence: 99%

A systematic review of emerging feature selection optimization methods for optimal text classification: the present state and prospective opportunities

Omolara

Abiodun

Alawida

et al. 2021

Neural Comput & Applic

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Specialized data preparation techniques, ranging from data cleaning, outlier detection, missing value imputation, feature selection (FS), amongst others, are procedures required to get the most out of data and, consequently, get the optimal performance of predictive models for classification tasks. FS is a vital and indispensable technique that enables the model to perform faster, eliminate noisy data, remove redundancy, reduce overfitting, improve precision and increase generalization on testing data. While conventional FS techniques have been leveraged for classification tasks in the past few decades, they fail to optimally reduce the high dimensionality of the feature space of texts, thus breeding inefficient predictive models. Emerging technologies such as the metaheuristics and hyper-heuristics optimization methods provide a new paradigm for FS due to their efficiency in improving the accuracy of classification, computational demands, storage, as well as functioning seamlessly in solving complex optimization problems with less time. However, little details are known on best practices for case-to-case usage of emerging FS methods. The literature continues to be engulfed with clear and unclear findings in leveraging effective methods, which, if not performed accurately, alters precision, real-world-use feasibility, and the predictive model's overall performance. This paper reviews the present state of FS with respect to metaheuristics and hyper-heuristic methods. Through a systematic literature review of over 200 articles, we set out the most recent findings and trends to enlighten analysts, practitioners and researchers in the field of data analytics seeking clarity in understanding and implementing effective FS optimization methods for improved text classification tasks.

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Section: It Takes Into Cognizant the Interaction Of Featuresmentioning

confidence: 99%

A systematic review of emerging feature selection optimization methods for optimal text classification: the present state and prospective opportunities

Omolara

Abiodun

Alawida

et al. 2021

Neural Comput & Applic

View full text Add to dashboard Cite

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“…To improve the global searching ability, Al-Betar and Awadallah [32] divided the whole bat population into two subgroups and specified the movement of bat individuals from one group to another by mobility. Jaddi et al [33] proposed to modify the velocity equation of the standard BA to better balance exploration and exploitation in the population, and Ghanem and Jantan [34] proposed an enhanced BA to enhance the diversity of the standard BA using a special mutation operator.…”

Section: Algorithm Structurementioning

confidence: 99%

Bat Algorithm Based on an Integration Strategy and Gaussian Distribution

Huang

2020

Mathematical Problems in Engineering

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The bat algorithm (BA) is a recent heuristic optimization algorithm based on the echolocation behavior of bats. However, the bat algorithm tends to fall into local optima and its optimization results are unstable because of its low global exploration ability. To solve these problems, a novel bat algorithm based on an integration strategy (IBA) is proposed in this paper. Through the integration strategy, an appropriate operator is adaptively selected to perform global search, so that the global search ability of the IBA is improved. Furthermore, the IBA disturbs the local optimum through a linear combination of Gaussian functions with different variances to avoid becoming trapped in local optima. The IBA also updates the velocity equation with an adaptive weight to further balance the exploration and exploitation. Moreover, the global convergence of the IBA is proved based on the convergence criterion of a stochastic algorithm. The performance of the IBA is evaluated on CEC2013 benchmark functions and compared with that of the standard BA as well as several of its variants. The results show that the IBA is superior to other algorithms.

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“…The proposed IBA is evaluated on 6 typical test functions which listed in Table 1. It compared with two classical optimization algorithm (i.e., BA and Particle swarm optimization algorithm (PSO) [27]), and two other recently improved BAs (i.e., EBA [28] and dBA [29]). Table 1.…”

Section: Cauchy Mutation Strategymentioning

confidence: 99%

A Novel Isomap-SVR Soft Sensor Model and Its Application in Rotary Kiln Calcination Zone Temperature Prediction

2020

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Soft sensing technology has been proved to be an effective tool for the online estimation of unmeasured or variables that are difficult to directly measure. The performance of a soft sensor depends heavily on its convergence speed and generalization ability to a great extent. Based on this idea, we propose a new soft sensor model, Isomap-SVR. First, the sample data set is divided into training set and testing set by using self-organizing map (SOM) neural network to ensure the fairness and symmetry of data segmentation. Isometric feature mapping (Isomap) method is used for dimensionality reduction of the model input data, which could not only reduce the structure complexity of the proposed model but speed up learning speed, and then the Support Vector Machine Regression (SVR) is applied to the regression model. A novel bat algorithm based on Cauchy mutation and Lévy flight strategy is used to optimize parameters of Isomap and SVR to improve the accuracy of the proposed model. Finally, the model is applied to the prediction of the temperature of rotary kiln calcination zone, which is difficult to measure directly. The simulation results show that the proposed soft sensor modeling method has higher learning speed and better generalization ability. Compared with other algorithms, this algorithm has obvious advantages and is an effective modeling method.

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An enhanced Bat algorithm with mutation operator for numerical optimization problems

Cited by 27 publications

References 34 publications

A systematic review of emerging feature selection optimization methods for optimal text classification: the present state and prospective opportunities

A systematic review of emerging feature selection optimization methods for optimal text classification: the present state and prospective opportunities

Bat Algorithm Based on an Integration Strategy and Gaussian Distribution

A Novel Isomap-SVR Soft Sensor Model and Its Application in Rotary Kiln Calcination Zone Temperature Prediction

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