A dividing-based many-objective evolutionary algorithm for large-scale feature selection

Li, Haoran; He, Fazhi; Liang, Yunsheng; Quan, Quan

doi:10.1007/s00500-019-04324-5

Cited by 66 publications

(24 citation statements)

References 68 publications

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“…In order to obtain fast convergence, two recombination techniques were presented. A triangular decision making was also proposed using manhattan distance metric for providing assistance to the users knowledge [40]. A feature based data exchange facility is proposed in cloud based design and manufacturing domain [41].…”

Section: Related Workmentioning

confidence: 99%

Multiclass Model for Agriculture Development Using Multivariate Statistical Method

et al. 2020

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Mahalanobis taguchi system (MTS) is a multi-variate statistical method extensively used for feature selection and binary classification problems. The calculation of orthogonal array and signalto-noise ratio in MTS makes the algorithm complicated when more number of factors are involved in the classification problem. Also the decision is based on the accuracy of normal and abnormal observations of the dataset. In this paper, a multiclass model using Improved Mahalanobis Taguchi System (IMTS) is proposed based on normal observations and Mahalanobis distance for agriculture development. Twentysix input factors relevant to crop cultivation have been identified and clustered into six main factors for the development of the model. The multiclass model is developed with the consideration of the relative importance of the factors. An objective function is defined for the classification of three crops, namely paddy, sugarcane and groundnut. The classification results are verified against the results obtained from the agriculture experts working in the field. The proposed classifier provides 100% accuracy, recall, precision and 0% error rate when compared with other traditional classifier models.

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Section: Related Workmentioning

confidence: 99%

Multiclass Model for Agriculture Development Using Multivariate Statistical Method

et al. 2020

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“…First, the exploration and exploitation phases were selected according to the escape energy of the prey in each iteration. The adaptive cooperation strategy was introduced into the exploration process, and the solution search equation is the modified location update equation (formula (8)). Then, one-dimensional or all-dimensional update operations were performed according to the conversion factor CF, which improved the diversity of Harris hawks' population and helps to find a better solution.…”

Section: Algorithm Flowmentioning

confidence: 99%

“…SI algorithm is a typical nature-inspired optimization algorithm, which simulates the social behavior of groups of animals [7]. Because of its simplicity, flexibility, no gradient information, and the ability to bypass local optimum, SI has been widely used in different disciplines and engineering fields [8]- [11]. In the past two decades, a large number of SI algorithms have been proposed.…”

Section: Introductionmentioning

confidence: 99%

Improved Harris Hawks Optimization Based on Adaptive Cooperative Foraging and Dispersed Foraging Strategies

2020

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Harris hawks optimization (HHO) is a new swarm intelligence optimization technique. Because of its simple structure and easy to implement, HHO has attracted research interest from scholars in different fields. However, the low population diversity and the single search method in the exploration phase weakened the global search capability of the HHO algorithm. In response to these defects, this paper proposes an improved HHO algorithm based on adaptive cooperative foraging and dispersed foraging strategies. First, the adaptive cooperative foraging strategy uses three random individuals to guide the position update, which achieves cooperation between individuals. Then the cooperation behavior is embedded in the onedimensional update operation framework, and the one-dimensional or total-dimensional update operation is adaptively selected. This way allows the algorithm to perform position update operations for a specific dimension of individual vectors with a certain probability, which improves the population diversity. Second, the dispersed foraging strategy is introduced into the HHO, forcing a part of Harris hawks to leave their current position to find more prey to obtain a better candidate solution. This way effectively avoids the algorithm falling into local optimum. Finally, a randomly shrinking exponential function is used to simulate the energy change of the prey, so that the algorithm maintains the exploration ability in the later exploitation process, effectively balancing the exploration and exploitation ability of the algorithm. The performance of the proposed ADHHO algorithm is evaluated using Wilcoxon's test on unimodal, multimodal and CEC 2014 benchmark functions. Numerical results and statistical experiments show that ADHHO provides better solution quality, convergence accuracy and stability compared with other state-of-the-art algorithms. INDEX TERMS Harris hawks optimization, adaptive cooperative foraging, dispersed foraging, Wilcoxon's test, CEC 2014 benchmark functions.

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“…Swarm intelligence imitates the social behavior of animal groups. Because of its simplicity, flexibility, non-derivation mechanism, and avoidance of local optima, SI is widely applied in feature selection [5], hardware/software co-design [6], scheduling [7], agriculture [8], metallurgy [9], and military [10]. The most representative SI algorithms are particle swarm optimization (PSO) [11], ant colony optimization (ACO) [12], and artificial bee colony (ABC) [13].…”

Section: Introductionmentioning

confidence: 99%

An Improved Squirrel Search Algorithm With Reproductive Behavior

et al. 2020

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The squirrel search algorithm (SSA) is a recently proposed nature-inspired algorithm based on the dynamic foraging and gliding behavior of squirrels. Because of its simplicity and stability, the squirrel algorithm has attracted increasing research interest. However, the lack of exploration ability of the SSA may lead to premature convergence to the local optimum. To overcome this disadvantage, an improved SSA with reproductive behavior (RSSA) is proposed to solve the numerical optimization problem. First, the reproductive behavior of the invasive weed algorithm (IWO) is introduced to the conventional SSA to generate offspring individuals, and these offspring individuals are scattered into the search space by Gaussian distribution to complete the location update. This method makes it possible for individuals with poor fitness to enter the next generation search, improving the exploration ability of the SSA. Second, an adaptive step strategy is proposed to adaptively adjust the search step of squirrels according to the distance between each squirrel and other family members. This strategy effectively balances the exploration and exploitation of the algorithm. Finally, the performance of the proposed RSSA algorithm is evaluated using Wilcoxon's test on unimodal, multimodal, fixed-dimensional multimodal and CEC 2014 benchmark functions. Experimental results and statistical tests show that RSSA has better performance in terms of convergence, accuracy, and search capability compared with other state-of-the-art algorithms. INDEX TERMS Nature-inspired, squirrel search algorithm, exploration and exploitation, Wilcoxon's test, CEC 2014 benchmark functions.

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A dividing-based many-objective evolutionary algorithm for large-scale feature selection

Cited by 66 publications

References 68 publications

Multiclass Model for Agriculture Development Using Multivariate Statistical Method

Multiclass Model for Agriculture Development Using Multivariate Statistical Method

Improved Harris Hawks Optimization Based on Adaptive Cooperative Foraging and Dispersed Foraging Strategies

An Improved Squirrel Search Algorithm With Reproductive Behavior

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