An Improved Squirrel Search Algorithm for Global Function Optimization

Wang, Yanjiao; Du, Tianlin

doi:10.3390/a12040080

Cited by 36 publications

(26 citation statements)

References 36 publications

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“…3. like evolutionary algorithms (e.g., genetic algorithms (GA)) [21] and swarm intelligence (SI) techniques (e.g., particle swarm optimization (PSO)), that have been used to solve the FS problem [22], [23] physics based techniques(e.g Gravitational Search Algorithm (GSA)) SI techniques include unlimited algorithms such as Grey Wolf Optimizer (GWO) [24] , Water Cycle Algorithm (WCA) [25] , Whale Optimization Algorithm (WOA) [26], [27], Firefly Algorithm (FA) [28], Salp Swarm Algorithm (SSA) [29] , [30], [31], Emperor Penguin Colony [32] squirrel search algorithm [33], [34], slime mould algorithm (SMA ) [35] , Butterfly Optimization Algorithm [36], [37], Moth Flame Optimization [38], [39] and Marine Predators Algorithm (MPA), which is the most recent and newest SI algorithm [35].…”

Section: Figure 1: Feature Selection Frameworkmentioning

confidence: 99%

An Efficient Marine Predators Algorithm for Feature Selection

et al. 2021

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Feature Selection (F.S.) reduces the number of features by removing unnecessary, redundant, and noisy information while keeping a relatively decent classification accuracy. F.S. can be considered an optimization problem. As the problem is challenging and there are many local solutions, stochastic optimization algorithms may be beneficial .This paper proposes a novel approach to dimension reduction in feature selection. As a seminal attempt, this work uses binary variants of the recent Marine Predators Algorithm (MPA) to select the optimal feature subset to improve classification accuracy. MPA is a new and novel nature-inspired metaheuristic. This research proposes an algorithm that is a hybridization between MPA and k-Nearest Neighbors (k-NN) called MPA-KNN. k-Nearest Neighbors (k-NN) is used to evaluate the selected features on medical datasets with feature sizes ranging from tiny to massive. The proposed methods are evaluated on 18 well-known UCI medical dataset benchmarks and compared with eight well-regarded metaheuristic wrapper-based approaches. The core exploratory and exploitative processes are adapted in MPA to select the optimal and meaningful features for achieving the most accurate classification. The results show that the proposed MPA-KNN approach had a remarkable capability to select the optimal and significant features. It performed better than the well-established metaheuristic algorithms we tested. The algorithms we used for comparison are Grey Wolf Optimizer (GWO), MothFlame Optimization Algorithm (MFO), Sine Cosine Algorithm (SCA), Whale Optimization Algorithm (WOA), Slap Swarm Algorithm (SSA), Butterfly Optimization Algorithm (BFO), and Harris Hawks Optimization (HHO). This paper is the first work that implements MPA for Feature Selection problems. The results ensure that the proposed MPA-KNN approach has a remarkable capability to select the optimal and significant features and performed better than several metaheuristic algorithms. MPA-KNN achieves the best averages accuracy, Sensitivity, and Specificity rates of all datasets.

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Section: Figure 1: Feature Selection Frameworkmentioning

confidence: 99%

An Efficient Marine Predators Algorithm for Feature Selection

et al. 2021

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“…Zheng and Luo [22] developed an ISSA using four modifications to the SSA. An adaptive method of predator presence probability, normal cloud model for randomness and fuzziness, a selection strategy between successive positions, and a dimensional search enhancement strategy are applied to SSA to form the suggested ISSA.…”

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%

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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“…The literature is currently saturated by novel nature-inspired optimisation metaphors claiming to mimic the collaborative or individual behaviour animals [49][50][51][52][53][54][55][56], human activities [57][58][59] or other natural phenomena [60][61][62]. The contribution made by most of these new optimisation paradigms is arguable as they are very similar to established frameworks form the field of EA, such as the GA, the DE and the ES ones, and from the field of SI as e.g.…”

Section: Adding New Algorithms and Problemsmentioning

confidence: 99%

The SOS Platform: Designing, Tuning and Statistically Benchmarking Optimisation Algorithms

Caraffini¹

2020

Preprint

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The Stochastic Optimisation Software (SOS) is a Java platform facilitating the algorithmic design process and the evaluation of metaheuristic optimisation algorithms. It reduces the burden of coding miscellaneous methods for dealing with several bothersome and time-demanding tasks such as parameter tuning, implementation of comparison algorithms and testbed problems, collecting and processing data to display results, measuring algorithmic overhead, etc. SOS provides numerous off-the-shelf methods including 1) customised implementations of statistical tests, such as the Wilcoxon Rank-Sum test and the Holm-Bonferroni procedure, for comparing performances of optimisation algorithms and automatically generate result tables in PDF and LaTeX formats; 2) the implementation of an original advanced statistical routine for accurately comparing couples of stochastic optimisation algorithms; 3) the implementation of a novel testbed suite for continuous optimisation, derived from the IEEE CEC 2014 benchmark, allowing for controlled activation of the rotation operator. each testbed function. Moreover, this article comments on the current state of the literature in stochastic optimisation and highlights similarities shared by modern metaheuristics inspired by nature. It is argued that the vast majority of these algorithms are simply a reformulation of the same methods and that metaheuristics for optimisation should be simply treated as stochastic processes with less emphasis on the inspiring metaphor behind them.

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An Improved Squirrel Search Algorithm for Global Function Optimization

Cited by 36 publications

References 36 publications

An Efficient Marine Predators Algorithm for Feature Selection

An Efficient Marine Predators Algorithm for Feature Selection

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

The SOS Platform: Designing, Tuning and Statistically Benchmarking Optimisation Algorithms

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