Primary sequences of proteinlike copolymers: Levy-flight–type long-range correlations

Background: An efficient feature selection method for Histopathological image classification plays an important role to eliminate irrelevant and redundant features. Therefore, this paper proposes a new levy flight salp swarm optimizer based feature selection method. Methods: The proposed levy flight salp swarm optimizer based feature selection method uses the levy flight steps for each follower salp to deviate them from local optima. The best solution returns the relevant and non-redundant features, which are fed to different classifiers for efficient and robust image classification. Results: The efficiency of the proposed levy flight salp swarm optimizer has been verified on 20 benchmark functions. The anticipated scheme beats the other considered meta-heuristic approaches. Furthermore, the anticipated feature selection method has shown better reduction in SURF features than other considered methods and performed well for histopathological image classification. Conclusion: This paper proposes an efficient levy flight salp Swarm Optimizer by modifying the step size of follower salp. The proposed modification reduces the chances of sticking into local optima. Furthermore, levy flight salp Swarm Optimizer has been utilized in the selection of optimum features from SURF features for the histopathological image classification. The simulation results validate that proposed method provides optimal values and high classification performance in comparison to other methods.

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Design of agricultural ontology based on levy flight distributed optimization and Naïve Bayes classifier

Sadhana ◽

10.1007/s12046-021-01652-x ◽

2021 ◽

Vol 46 (3) ◽

Author(s):

Deepa Rajendran ◽

S Vigneshwari

Keyword(s):

Naive Bayes ◽

Distributed Optimization ◽

Naïve Bayes ◽

Naive Bayes Classifier ◽

Lévy Flight ◽

Bayes Classifier ◽

Naïve Bayes Classifier ◽

Levy Flight

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Short Term Solar Power Forecasting using Hybrid Minimum Variance Expanded RVFLN and Sine-Cosine Levy Flight PSO Algorithm

Renewable Energy ◽

10.1016/j.renene.2021.04.088 ◽

2021 ◽

Author(s):

Deepak Ranjan Das ◽

P.K. Dash ◽

Ranjeeta Bisoi

Keyword(s):

Solar Power ◽

Pso Algorithm ◽

Minimum Variance ◽

Lévy Flight ◽

Short Term ◽

Levy Flight ◽

Solar Power Forecasting ◽

Power Forecasting

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Studies of charm and beauty hadron long-range correlations in pp and pPb collisions at LHC energies

Physics Letters B ◽

10.1016/j.physletb.2020.136036 ◽

2021 ◽

Vol 813 ◽

pp. 136036

Author(s):

A.M. Sirunyan ◽

A. Tumasyan ◽

W. Adam ◽

F. Ambrogi ◽

T. Bergauer ◽

...

Keyword(s):

Long Range ◽

Long Range Correlations

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Salp swarm algorithm with crossover scheme and Lévy flight for global optimization

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-201737 ◽

2021 ◽

pp. 1-12

Author(s):

Heming Jia ◽

Chunbo Lang

Keyword(s):

Global Optimization ◽

Heuristic Algorithm ◽

Experimental Results ◽

High Dimensional ◽

Lévy Flight ◽

Test Functions ◽

Levy Flight ◽

Salp Swarm Algorithm ◽

Swarm Algorithm ◽

Accuracy And Stability

Salp swarm algorithm (SSA) is a meta-heuristic algorithm proposed in recent years, which shows certain advantages in solving some optimization tasks. However, with the increasing difficulty of solving the problem (e.g. multi-modal, high-dimensional), the convergence accuracy and stability of SSA algorithm decrease. In order to overcome the drawbacks, salp swarm algorithm with crossover scheme and Lévy flight (SSACL) is proposed. The crossover scheme and Lévy flight strategy are used to improve the movement patterns of salp leader and followers, respectively. Experiments have been conducted on various test functions, including unimodal, multimodal, and composite functions. The experimental results indicate that the proposed SSACL algorithm outperforms other advanced algorithms in terms of precision, stability, and efficiency. Furthermore, the Wilcoxon’s rank sum test illustrates the advantages of proposed method in a statistical and meaningful way.

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