Journal ArticleDOI
Improved Binary Grey Wolf Optimizer and Its application for feature selection
TLDR
Through verifying the benchmark functions, the advanced binary GWO is superior to the original BGWO in the optimality, time consumption and convergence speed.Abstract:
Grey Wolf Optimizer (GWO) is a new swarm intelligence algorithm mimicking the behaviours of grey wolves. Its abilities include fast convergence, simplicity and easy realization. It has been proved its superior performance and widely used to optimize the continuous applications, such as, cluster analysis, engineering problem, training neural network and etc. However, there are still some binary problems to optimize in the real world. Since binary can only be taken from values of 0 or 1, the standard GWO is not suitable for the problems of discretization. Binary Grey Wolf Optimizer (BGWO) extends the application of the GWO algorithm and is applied to binary optimization issues. In the position updating equations of BGWO, the a parameter controls the values of A and D , and influences algorithmic exploration and exploitation. This paper analyses the range of values of A D under binary condition and proposes a new updating equation for the a parameter to balance the abilities of global search and local search. Transfer function is an important part of BGWO, which is essential for mapping the continuous value to binary one. This paper includes five transfer functions and focuses on improving their solution quality. Through verifying the benchmark functions, the advanced binary GWO is superior to the original BGWO in the optimality, time consumption and convergence speed. It successfully implements feature selection in the UCI datasets and acquires low classification errors with few features.read more
Citations
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Journal ArticleDOI
Metaheuristic Algorithms on Feature Selection: A Survey of One Decade of Research (2009-2019)
TL;DR: In this article, an extensive literature review on solving feature selection problem using metaheuristic algorithms which are developed in the ten years (2009-2019) is presented, and a categorical list of more than a hundred metaheuristics algorithms is presented.
Journal ArticleDOI
Optimization of support vector machine through the use of metaheuristic algorithms in forecasting TBM advance rate
Jian Zhou,Yingui Qiu,Shuangli Zhu,Danial Jahed Armaghani,Chuanqi Li,Hoang Nguyen,Saffet Yagiz +6 more
TL;DR: Modeling results revealed that the MFO algorithm can capture better hyper-parameters of the SVM model in predicting TBM AR among all three hybrid models, confirming that this hybrid S VM model is a powerful and applicable technique addressing problems related to TBM performance with a high level of accuracy.
Journal ArticleDOI
MbGWO-SFS: Modified Binary Grey Wolf Optimizer Based on Stochastic Fractal Search for Feature Selection
TL;DR: A Modified Binary GWO based on Stochastic Fractal Search (SFS) to identify the main features by achieving the exploration and exploitation balance and shows the superiority of the proposed algorithm compared to binary versions of the-state-of-the-art optimization techniques.
Journal ArticleDOI
Adaptive crossover operator based multi-objective binary genetic algorithm for feature selection in classification
TL;DR: A multi-objective binary genetic algorithm integrating an adaptive operator selection mechanism (MOBGA-AOS) is proposed, which is capable of removing a large amount of features while ensuring a small classification error and is compared with five well-known evolutionary multi- objective algorithms on ten datasets.
Journal ArticleDOI
An efficient surrogate-assisted hybrid optimization algorithm for expensive optimization problems
TL;DR: An efficient surrogate-assisted hybrid optimization (SAHO) algorithm is proposed via combining two famous algorithms, namely, teaching-learning-based optimization (TLBO) and differential evolution (DE).
References
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Journal ArticleDOI
Grey Wolf Optimizer
TL;DR: The results of the classical engineering design problems and real application prove that the proposed GWO algorithm is applicable to challenging problems with unknown search spaces.
Journal ArticleDOI
A survey on opinion mining and sentiment analysis
Kumar Satish Ravi,Vadlamani Ravi +1 more
TL;DR: A rigorous survey on sentiment analysis is presented, which portrays views presented by over one hundred articles published in the last decade regarding necessary tasks, approaches, and applications of sentiment analysis.
Journal ArticleDOI
Binary grey wolf optimization approaches for feature selection
TL;DR: Results prove the capability of the proposed binary version of grey wolf optimization (bGWO) to search the feature space for optimal feature combinations regardless of the initialization and the used stochastic operators.