Review on Nature-Inspired Algorithms

Wael Korani; Malek Mouhoub

doi:10.1007/s43069-021-00068-x

Improving nature-inspired algorithms for feature selection

Journal of Ambient Intelligence and Humanized Computing ◽

10.1007/s12652-021-03136-6 ◽

2021 ◽

Author(s):

Niam Abdulmunim Al-Thanoon ◽

Omar Saber Qasim ◽

Zakariya Yahya Algamal

Keyword(s):

Feature Selection ◽

Nature Inspired Algorithms

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Simulation of foamed concrete compressive strength prediction using adaptive neuro-fuzzy inference system optimized by nature-inspired algorithms

Frontiers of Structural and Civil Engineering ◽

10.1007/s11709-020-0684-6 ◽

2021 ◽

Author(s):

Ahmad Sharafati ◽

H. Naderpour ◽

Sinan Q. Salih ◽

E. Onyari ◽

Zaher Mundher Yaseen

Keyword(s):

Compressive Strength ◽

Fuzzy Inference System ◽

Fuzzy Inference ◽

Strength Prediction ◽

Concrete Compressive Strength ◽

Inference System ◽

Neuro Fuzzy ◽

Foamed Concrete ◽

Nature Inspired Algorithms

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Comparative study on nature inspired algorithms for optimization problem

2017 International conference of Electronics, Communication and Aerospace Technology (ICECA) ◽

10.1109/iceca.2017.8212781 ◽

2017 ◽

Cited By ~ 5

Author(s):

Ishani Luthra ◽

Shubham Krishna Chaturvedi ◽

Divya Upadhyay ◽

Richa Gupta

Keyword(s):

Comparative Study ◽

Optimization Problem ◽

Nature Inspired Algorithms

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Fusing Nature with Computational Science for Optimal Signal Extraction

Stats ◽

10.3390/stats4010006 ◽

2021 ◽

Vol 4 (1) ◽

pp. 71-85

Author(s):

Hossein Hassani ◽

Mohammad Reza Yeganegi ◽

Xu Huang

Keyword(s):

Data Science ◽

Time Series Data ◽

Singular Spectrum Analysis ◽

Hybrid Approach ◽

Computational Science ◽

Series Data ◽

Computationally Efficient ◽

Forecasting Performance ◽

Nature Inspired Algorithms ◽

Optimal Signal

Fusing nature with computational science has been proved paramount importance and researchers have also shown growing enthusiasm on inventing and developing nature inspired algorithms for solving complex problems across subjects. Inevitably, these advancements have rapidly promoted the development of data science, where nature inspired algorithms are changing the traditional way of data processing. This paper proposes the hybrid approach, namely SSA-GA, which incorporates the optimization merits of genetic algorithm (GA) for the advancements of Singular Spectrum Analysis (SSA). This approach further boosts the performance of SSA forecasting via better and more efficient grouping. Given the performances of SSA-GA on 100 real time series data across various subjects, this newly proposed SSA-GA approach is proved to be computationally efficient and robust with improved forecasting performance.

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