RETRACTED ARTICLE: Usability feature extraction using modified crow search algorithm: a novel approach

Gupta, Deepak; Rodrigues, Joel J. P. C.; Sundaram, Shirsh; Khanna, Ashish; Korotaev, Valery V.; Albuquerque, Victor Hugo C. de

doi:10.1007/s00521-018-3688-6

Cited by 81 publications

(27 citation statements)

References 23 publications

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“…In [97], Coelho et al [98] tried to propose a modified CSA by using Gaussian distribution and diversity information of the population. Also, Gupta et al tried to extract usability features by proposing a novel approach of CSA called modified CSA (MCSA) in which a particular selected feature number is generated and applied to the life cycle of software development by using usability factors hierarchical model.…”

Section: ) Modified Csamentioning

confidence: 99%

Crow Search Algorithm: Theory, Recent Advances, and Applications

et al. 2020

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In this paper, a comprehensive overview of the Crow Search Algorithm (CSA) is introduced with detailed discussions, which is intended to keep researchers interested in swarm intelligence algorithms and optimization problems. CSA is a new swarm intelligence algorithm recently developed, which simulates crow behavior in storing excess food and retrieving it when needed. In the optimization theory, the crow is the searcher, the surrounding environment is the search space, and randomly storing the location of food is a feasible solution. Among all food locations, the location where the most food is stored is considered to be the global optimal solution, and the objective function is the amount of food. By simulating the intelligent behavior of crows, CSA tries to find optimal solutions to various optimization problems. It has gained a considerable interest worldwide since its advantages like simple implementation, a few numbers of parameters, flexibility, etc. This survey introduces a comprehensive variant of CSA, including hybrid, modified, and multi-objective versions. Furthermore, based on the analyzed papers published in the literature by some publishers such as IEEE, Elsevier, and Springer, the comprehensive application scenarios of CSA such as power, computer science, machine learning, civil engineering have also been reviewed. Finally, the advantages and disadvantages of CSA have been discussed by conducting some comparative experiments with other similar published peers.

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Section: ) Modified Csamentioning

confidence: 99%

Crow Search Algorithm: Theory, Recent Advances, and Applications

et al. 2020

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“…iter max is the number of maximum iterations. There is a parameter in CSA named awareness probability which its role is to balance the intensification and diversification [55] to increase the intensification using small values for awareness probability by searching on a local space and by increasing the awareness probability value; CSA can tend to determine the searching space on the global scale. CSA implementation for optimization can be briefly explained as [54]:…”

Section: Crow Search Algorithm (Csa)mentioning

confidence: 99%

Forecasting Daily Solar Radiation Using CEEMDAN Decomposition-Based MARS Model Trained by Crow Search Algorithm

Rezaie-Balf

Maleki²,

Kim

et al. 2019

Energies

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The precise forecasting of daily solar radiation (DSR) is receiving prominent attention among thriving solar energy studies. In this study, three standalone models, including gene expression programing (GEP), multivariate adaptive regression splines (MARS), and self-adaptive MARS (SaMARS), were evaluated to forecast DSR. A SaMARS model was classified as MARS model when using the crow search algorithm (CSA). In addition, to overcome the limitations of the standalone models, the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) was employed to enhance the accuracy of DSR forecasting. Therefore, three hybrid models including CEEMDAN-GEP, CEEMDAN-MARS, and CEEMDAN-SaMARS were proposed to forecast DSR in Busan and Incheon stations in South Korea. The performance of proposed models were evaluated and affirmed that the accuracy of the CEEMDAN-SaMARS model (NSE = 0.878–0.883) outperformed CEEMDAN-MARS (NSE = 0.819–0.818), CEEMDAN-GEP (NSE = 0.873–0.789), SaMARS (NSE = 0.846–0.769), MARS (NSE = 0.819–0.758), and GEP (NSE = 0.814–0.755) models at both stations. Therefore, it can be concluded that the optimized CEEMDAN-SaMARS model significantly enhanced the accuracy of DSR forecasting compared to that of standalone models.

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“…Modified Crow Search Algorithm (MCSA) introduced by Deepak Get al [11]. This algorithm used for feature extraction usability and prediction.…”

Section: Literature Surveymentioning

confidence: 99%

Crow Search Algorithm (CSA)

2019

IJRTE

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Researchers’ are taking keen interest in Optimization algorithms due to their heuristic and meta-heuristic nature. Heuristic algorithms find the arrangement by utilizing the experimentation strategy. Then again, meta-heuristic algorithms discover the response at a more elevated tier. Several nature-based metaheuristic algorithms are easily accessible. Askarzadeh has introduced the Crow search algorithm and stated that it is meta-heuristic optimization algorithm. The astute conduct of the crow moves CSA. Crows are keen on putting away the abundance nourishment at concealing spots and recuperating it at whatever point it is needed. CSA's previous outcomes show that it can unravel different complex building related optimization issues. There are six compelled building plan issues, and CSA is utilized to upgrade these issues. This paper focuses on a far-reaching investigation of CSA in the diverse application is given with the examination just as the exhibitions of the CSA in the different structure is talked about.

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RETRACTED ARTICLE: Usability feature extraction using modified crow search algorithm: a novel approach

Cited by 81 publications

References 23 publications

Crow Search Algorithm: Theory, Recent Advances, and Applications

Crow Search Algorithm: Theory, Recent Advances, and Applications

Forecasting Daily Solar Radiation Using CEEMDAN Decomposition-Based MARS Model Trained by Crow Search Algorithm

Crow Search Algorithm (CSA)

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