An Improved Squirrel Search Algorithm With Reproductive Behavior

Zhang, Xuncai; Zhao, Kai; Wang, Lingfei; Wang, Yanfeng; Niu, Ying

doi:10.1109/access.2020.2998324

Cited by 20 publications

(32 citation statements)

References 48 publications

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“…From the below table and charts, it can be seen that the proposed PGS-ISSA has improved the feature selection process better than the SSA and other improved SSA based approaches. Comparing the SSA, ISSA [19], ISSA [22], RSSA [25] and the proposed PGS-ISSA applied to SVM classifier, it can be seen that, in most cases, PGS-ISSA based feature selection approach has increased accuracy, precision, recall, f-measure, specificity and reduce execution time. In particular, for the bigger Higgs dataset, PGS-ISSA has high accuracy of 64.72% which is 5.39%, 3.49%, 3.2%, and 1.78%, higher than the SSA, ISSA [19], ISSA [22], and RSSA [25], respectively.…”

Section: Performance Evaluation Of Pgs-issa Based Feature Selectionmentioning

confidence: 99%

“…However, the complexity of this ISSA negatively impacts the performance for high resolution applications. Similar to this algorithm, Zhang et al [25] proposed an improved SSA called Reproductive SSA (RSSA) in which the reproductive behavior of the invasive weed algorithm (IWO) and an adaptive step strategy are used to generate new population and enhance global search process to balance the exploration and exploitation. This RSSA achieved high convergence with 33.3% high optimum solutions, high accuracy and less error of 0.08.…”

Section: Related Workmentioning

confidence: 99%

“…Similarly, the improved and modified SSA models from the literature are also implemented to compare their efficiency with the proposed PGS-ISSA. ISSA [19], ISSA [22], and RSSA [25] are the improved SSA algorithms chosen for implementation. MOEA/D-EWA-ISSA [20] and CSSA [21] are omitted since it is highly similar to ISSA [19] but has high complexity.…”

Section: Performance Evaluation Of Pgs-issa Based Feature Selectionmentioning

confidence: 99%

“…MOEA/D-EWA-ISSA [20] and CSSA [21] are omitted since it is highly similar to ISSA [19] but has high complexity. MSSA [23] is provides low performance than PSO while ISSA [24] is much similar to the RSSA [25] but has high error rate and hence they are not considered for implementation.…”

Section: Performance Evaluation Of Pgs-issa Based Feature Selectionmentioning

confidence: 99%

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Population and Global Search Improved Squirrel Search Algorithm for Feature Selection in Big Data Classification

Ali¹,

Balakrishnan²

2021

IJIES

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The analytics techniques in Big Data are extensively employed as an alternative to generalized for data mining due to the huge volumes of large-scale high dimensional data. Feature selection techniques eradicate the redundant and inappropriate features to decrease the data dimensionality and increase the classifiers' efficiency. However, the traditional feature selection strategies are dearth of scalability to handle the unique characteristics of large-scale data and extract the valuable features within the restricted time. This article proposed a feature selection algorithm centered on the Population and Global Search Improved Squirrel Search Algorithm (PGS-ISSA) that tackles the problem of local optimum and reduce convergence rate in standard Squirrel Search Algorithm (SSA). The novelty of this proposed PGS-ISSA is the introduction of chaos theory to improve population initialization so that the search space is increased. Then the acceleration coefficients are used in the position update equations to improve the convergence rate in local search process while inertia weight is also applied to optimally balance the exploration and exploitation in SSA. PGS-ISSA employs the fitness function based on the minimum error rate for ensuring the selection of best features that improve the classification accuracy. The proposed PGS-ISSA based feature section algorithm is evaluated by using Support Vector Machine (SVM) classifier implemented in MATLAB tool to address the big data classification problem. The experiments performed on both small and large-scale datasets illustrated that the suggested PGS-ISSA enhances the classification accuracy by 1.7% to 5.4% better than other compared models through effective handling of the big data problems. The results obtained for the bigger Higgs dataset shows that the proposed PGS-ISSA achieved high performance than the standard SSA, existing ISSA models and other prominent optimizationbased feature selection algorithms with 64.72% accuracy, 67.3194% precision, 62.1528% recall, 62.3026% f-measure, 82.7226% specificity and consumed less time of 140.1366 seconds. PGS-ISSA also achieved comparatively better results for the other benchmark datasets with 0.3% to 6% improvement on statistical metrics and 10% to 25% reduction in execution time.

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Section: Performance Evaluation Of Pgs-issa Based Feature Selectionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Performance Evaluation Of Pgs-issa Based Feature Selectionmentioning

confidence: 99%

Section: Performance Evaluation Of Pgs-issa Based Feature Selectionmentioning

confidence: 99%

See 2 more Smart Citations

Population and Global Search Improved Squirrel Search Algorithm for Feature Selection in Big Data Classification

Ali¹,

Balakrishnan²

2021

IJIES

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“…Fruit fly optimization algorithm (FOA) [19] was proposed in 2011, inspired by the cooperative foraging behavior of fruit flies. Besides, many new SI algorithms appear every year, such as grey wolf optimization algorithm (GWO) [20], firefly algorithm (FF) [21], dragonfly algorithm (DA) [22], squirrel search algorithm (SSA) [23], [24]. It is worth mentioning that some recently proposed meta-heuristic algorithms, such as Henry gas solubility (HGS) [25], slime mould algorithm (SMA) [26], equilibrium optimizer (EO) [27] and quasi-affine transformation evolutionary (QUATRE) [28] show excellent performance in solving optimization problems.…”

Section: Parameters Of Proton Exchange Membrane Fuel Cell Stacksmentioning

confidence: 99%

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

2020

Self Cite

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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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Fused deep learning based Facial Expression Recognition of students in online learning mode

Sumalakshmi

Vasuki²

2022

Concurrency and Computation

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In this research work, Facial Expression Recognition (FER) is used in the analysis of facial expressions during the online learning sessions in the prevailing pandemic situation. An integrated geometric and appearance feature extraction is presented for the FER of the students participating in the online classes. The integrated features provided a low-dimensional significant feature area for better facial data representation. Feasible Weighted Squirrel Search Optimization (FW-SSO) algorithm is applied for selecting the optimal features due to its efficient exploration of the search space and enhancement of the dynamic search. The output of the FW-SSO algorithm is used for tuning the autoencoder. Autoencoder is used for combining the G&A features, for feature optimization process. Classification is done by using Long Short-Term Memory (LSTM) network with Attention Mechanism (ALSTM), as it is highly efficient in capturing the long-term dependency of the facial landmarks in the image/video sequences.The proposed fused deep learning method focuses on the fusion of the G&A features for high discrimination. Experimental analysis using FER-2013 and LIRIS datasets demonstrated that the proposed method achieved maximum accuracy of 85.96% than the existing architectures and maximum accuracy of 88.24% than the VGGNet-CNN architecture.

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An Improved Squirrel Search Algorithm With Reproductive Behavior

Cited by 20 publications

References 48 publications

Population and Global Search Improved Squirrel Search Algorithm for Feature Selection in Big Data Classification

Population and Global Search Improved Squirrel Search Algorithm for Feature Selection in Big Data Classification

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

Fused deep learning based Facial Expression Recognition of students in online learning mode

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