An Improved Artificial Bee Colony Algorithm Based on Gaussian Mutation and Chaos Disturbance

Cheng, Xiaoya; Jiang, Mingxin

doi:10.1007/978-3-642-30976-2_39

Cited by 12 publications

(3 citation statements)

References 7 publications

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“…The Gaussian mutation operation has been derived from the Gaussian normal distribution and has demonstrated its effectiveness with application to evolutionary search [ 68 ]. This theory was referred to as classical evolutionary programming (CEP).The Gaussian mutations have been used to exploit the searching capabilities of ABC [ 69 ], PSO [ 70 ], and DE [ 71 ]. Also, Gaussian mutation is more likely to create a new offspring near the original parent because of its narrow tail.…”

Section: Background Informationmentioning

confidence: 99%

An Intelligent Parkinson’s Disease Diagnostic System Based on a Chaotic Bacterial Foraging Optimization Enhanced Fuzzy KNN Approach

Cai

Wen

et al. 2018

Computational and Mathematical Methods in Medicine

110

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Parkinson's disease (PD) is a common neurodegenerative disease, which has attracted more and more attention. Many artificial intelligence methods have been used for the diagnosis of PD. In this study, an enhanced fuzzy k-nearest neighbor (FKNN) method for the early detection of PD based upon vocal measurements was developed. The proposed method, an evolutionary instance-based learning approach termed CBFO-FKNN, was developed by coupling the chaotic bacterial foraging optimization with Gauss mutation (CBFO) approach with FKNN. The integration of the CBFO technique efficiently resolved the parameter tuning issues of the FKNN. The effectiveness of the proposed CBFO-FKNN was rigorously compared to those of the PD datasets in terms of classification accuracy, sensitivity, specificity, and AUC (area under the receiver operating characteristic curve). The simulation results indicated the proposed approach outperformed the other five FKNN models based on BFO, particle swarm optimization, Genetic algorithms, fruit fly optimization, and firefly algorithm, as well as three advanced machine learning methods including support vector machine (SVM), SVM with local learning-based feature selection, and kernel extreme learning machine in a 10-fold cross-validation scheme. The method presented in this paper has a very good prospect, which will bring great convenience to the clinicians to make a better decision in the clinical diagnosis.

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Section: Background Informationmentioning

confidence: 99%

An Intelligent Parkinson’s Disease Diagnostic System Based on a Chaotic Bacterial Foraging Optimization Enhanced Fuzzy KNN Approach

Cai

Wen

et al. 2018

Computational and Mathematical Methods in Medicine

110

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show abstract

“…In this paper, artificial bee colony algorithm is selected because its fast convergence, more accurate solution and stability [23][24].…”

Section: Parameter Optimization Of Modelsmentioning

confidence: 99%

Using Additive Expression Programming for Gene Regulatory Network Inference

Yang¹

2015

IJHIT

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“…GA approach is especially suitable for solving the complex and nonlinear problems which are hard for traditional search methods. It is good at global search and has stronger robustness than conventional search algorithms [9]. However, when GA approach searches nearby the global optimal solution, its search ability intends to decrease, even may get stuck in local minimum [2].…”

Section: Outline Of the Algorithmmentioning

confidence: 99%

Chaotic Genetic Algorithm for Mixed Integer Programming Problem

Wang

Zhang

Zheng

et al. 2014

AMM

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This paper proposed a chaotic genetic algorithm (CGA) to solve the mixed integer programming problem (MIPP). The basic idea of this algorithm is to overcome the deficiency of genetic algorithm (GA) by introducing chaotic disturbances into the genetic search process. Two typical MIPP problems are used to evaluate the performances of the proposed CGA. Experimental results show that performances of the algorithm have been improved by the chaotic disturbances, such as, search ability, precision, stability and convergence speed or calculation efficiency. The proposed CGA algorithm is suitable for solving complicated practical MIPP problem.

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An Improved Artificial Bee Colony Algorithm Based on Gaussian Mutation and Chaos Disturbance

Cited by 12 publications

References 7 publications

An Intelligent Parkinson’s Disease Diagnostic System Based on a Chaotic Bacterial Foraging Optimization Enhanced Fuzzy KNN Approach

An Intelligent Parkinson’s Disease Diagnostic System Based on a Chaotic Bacterial Foraging Optimization Enhanced Fuzzy KNN Approach

Using Additive Expression Programming for Gene Regulatory Network Inference

Chaotic Genetic Algorithm for Mixed Integer Programming Problem

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