A multi-objective feature selection method using Newton’s law based PSO with GWO

Dhal, Pradip; Azad, Chandrashekhar

doi:10.1016/j.asoc.2021.107394

Cited by 41 publications

(14 citation statements)

References 52 publications

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“…Dhal et al [30] proposed a binary version of the hybrid two-phase multi-objective FS approach, based on PSO and GWO. In the first stage, the PSO performs a global search.…”

Section: Algorithmmentioning

confidence: 99%

Threshold Binary Grey Wolf Optimizer Based on Multi-Elite Interaction for Feature Selection

Zhang

et al. 2023

IEEE Access

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The traditional grey wolf algorithm is widely used for feature selection. However, within complex feature multi-dimensional problems, the grey wolf algorithm is prone to reach locally optimal solutions and premature convergence. In this paper, a threshold binary grey wolf optimizer based on multi-elite interaction for feature selection (MTBGWO) is proposed. Firstly, the multi-population topology is adopted to enhance the population's diversity for improving search space utilization. Secondly, an information interaction learning strategy is adopted for the update of sub-population elite wolf position (optimal position) via learning better position from other elite wolves; in order to improve the local exploitation ability of the sub-population. At the same time, the command of β and δ wolves (second and third best positions) for population position updates is removed. Finally, a threshold approach is employed to convert the continuous position of grey wolf individuals into binary one to apply in the feature selection problem. Further, The MTBGWO algorithm proposed in this paper is compared with the traditional binary grey wolf algorithm (BGWO), binary whale algorithm (BWOA), as well as some recently developed novel algorithms to exhibit its superiority and robustness. Totally 16 classification datasets, from the UCI Machine Learning Repository, are chosen for comparison. The Wilcoxon's rank-sum non-parametric statistical test is carried out at 5% significance level to evaluate whether the results of the proposed algorithms significantly differs from those of the other algorithms. In the experimental results for all datasets, the overall average accuracy of the MTBGWO algorithm is 94.7%, while the highest of the other algorithms is 92.8% and the selected feature subset is 25% of the total dataset, while the GWO and WOA algorithms are twice as high as MTBGWO. In terms of computational efficiency, the overall processing time of MTBGWO is 24.2 seconds, whereas HSGW is 44.1 seconds. The results reveal that the MTBGWO has shown its superiority in solving the feature selection problem.INDEX TERMS Grey wolf optimizer, feature selection, binary optimizer, elite interaction strategy

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“…Dhal et al [30] proposed a binary version of the hybrid two-phase multi-objective FS approach, based on PSO and GWO. In the first stage, the PSO performs a global search.…”

Section: Algorithmmentioning

confidence: 99%

Threshold Binary Grey Wolf Optimizer Based on Multi-Elite Interaction for Feature Selection

Zhang

et al. 2023

IEEE Access

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“…The PSO algorithm has good local search capabilities and strong convergence characteristics, 62–65 while the CS algorithm is highly random and has a strong ability to avoid the local optimal solution and global searchability. 66–68 In recent years, the PSO-CS algorithm has been applied in many fields, and many scholars have also verified that the PSO-CS coupling algorithm has many advantages.…”

Section: Methodsmentioning

confidence: 99%

Motion comfort analysis of tight-fitting sportswear from multi-dimensions using intelligence systems

Cheng

Wang

Zeng

et al. 2022

Textile Research Journal

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Focusing on the “human–sport-clothing” system, this paper analyzed the influence of combinations of different tights and sport states on human body parts and overall body comfort from multiple dimensions. However, the motion state and some fabric parameters are non-numerical parameters, which could not be used for model analysis directly. In addition, there are too many numerical fabric parameters whose relationships are complicated, and it is difficult for general models to deal with these relationships, resulting in low accuracy of the comfort prediction model. Moreover, when using the artificial neural network to study comfort, it has some difficulties in expressing comfort and low prediction accuracy. To solve these problems, One-Hot was used to encode non-numerical parameters, and then intelligent algorithms were adopted to deal with these complex fabric parameters. Finally, a comfort prediction model was established in combination with an adaptive fuzzy reasoning system. The results showed that different fabric combinations and motion states had significant effects on local comfort (comfort of specific human body parts) and global comfort (whole body comfort). Moreover, the prediction model with non-numerical parameters has higher accuracy than the model without non-numerical parameters, which indicated that the prediction accuracy of the model had been improved after the introduction of One-Hot coding, so the non-numerical parameters cannot be ignored. The particle swarm optimization algorithm-cuckoo search algorithm-adaptive network-based fuzzy inference system hybrid model was superior to the particle swarm optimization algorithm-adaptive network-based fuzzy inference system and cuckoo search algorithm-adaptive network-based fuzzy inference system model in predicting local comfort and global comfort.

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“…Therefore, these three basic algorithms are explained in this section, briefly. 43 Wrapper Grey Wolf Optimization K-nearest neighbor Harris Hawks Optimization Dhal and Azad 44 Wrapper Grey Wolf Optimization -Particle Swarm Optimization…”

Section: Preliminariesmentioning

confidence: 99%

“…In addition, the rate of accuracy is the main considered objective in this study, which is measured by KNN classifier. Combining different meta‐heuristics to improve the quality of feature selection algorithms is the backbone idea of Dhal and Azad, 44 as well. This study combines particle swarm optimization and grey wolf optimization algorithms to reach the goal.…”

Section: Related Workmentioning

confidence: 99%

Multiobjective whale optimization algorithm‐based feature selection for intelligent systems

Riyahi

Rafsanjani

Gupta

et al. 2022

Int J of Intelligent Sys

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With regard to large dimensions of contemporary data sets and restricted computational time of intelligent systems, reducing the dimensions of data sets is necessary. Feature selection is a practical way to remove a set of redundant, irrelevant, and noisy features. In this way, the speed of decision-making procedure will be increased while the accuracy of decisions will be retained. To this end, numerous attentions have been attracted to the topic and consequently, extensive range of methods has been proposed. Regarding the goals of the feature selection concept, the proposed algorithms in this field must be fast and accurate. Therefore, this paper proposes a light meanwhile accurate algorithm to fulfill the mentioned goals. The presented algorithm takes the speed advantage of Whale Optimization Algorithm (WOA) to propose a novel feature selection method for intelligent systems. Moreover, to reach the goal of accuracy, the proposed strategy considers three important fitness objectives, namely, the number of selected features, the accuracy of classification, and information gain. The proposed scheme considers an accurate multiobjective fitness function instead of manipulating the basic algorithm. The reason is that improving the basic algorithms, WOA in our case, may lead to loading

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A multi-objective feature selection method using Newton’s law based PSO with GWO

Cited by 41 publications

References 52 publications

Threshold Binary Grey Wolf Optimizer Based on Multi-Elite Interaction for Feature Selection

Threshold Binary Grey Wolf Optimizer Based on Multi-Elite Interaction for Feature Selection

Motion comfort analysis of tight-fitting sportswear from multi-dimensions using intelligence systems

Multiobjective whale optimization algorithm‐based feature selection for intelligent systems

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