A Hybrid Sparrow Search Algorithm Based on Constructing Similarity

Liu, Jianhua; Wang, Zhiheng

doi:10.1109/access.2021.3106269

Cited by 47 publications

(17 citation statements)

References 30 publications

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“…Jianhua and Zhiheng [ 19 ] worked on the local convergence and convergence stagnation of the original SSA. The Circle chaos map was used with the SSA, where the results showed that the model had a more vital global optimization ability and higher precision in convergence.…”

Section: Recent Variants Of Ssamentioning

confidence: 99%

Recent Versions and Applications of Sparrow Search Algorithm

Awadallah

Al-Betar

Doush

et al. 2023

Arch Computat Methods Eng

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This paper reviews the latest versions and applications of sparrow search algorithm (SSA). It is a recent swarm-based algorithm proposed in 2020 rapidly grew due to its simple and optimistic features. SSA is inspired by the sparrow living style of foraging and the anti-predation behavior of sparrows. Since its establishment, it has been utilized for a plethora of optimization problems in different research topics, such as mechanical engineering, electrical engineering, civil engineering, power systems, industrial engineering, image processing, networking, environment, robotics, planing and scheduling, and healthcare. Initially, the growth of SSA and its theoretical features are highlighted in terms of the number of published articles, citations, topics covered, etc. After that, the different extended versions of SSA are reviewed, where the main variations of SSA are produced to avoid premature convergence and to boost the diversity aspects. These extended versions are modifications and hybridization summarized with more focus on the motivations behind establishing these versions. Multi-objective SSA is also presented as another version to deal with Multi-objective optimization problems. The critical analysis of the main research gaps in the convergence behaviour of SSA is discussed. Finally, the conclusion and the possible future expansions are recommended based on the research works accomplished in the literature.

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Section: Recent Variants Of Ssamentioning

confidence: 99%

Recent Versions and Applications of Sparrow Search Algorithm

Awadallah

Al-Betar

Doush

et al. 2023

Arch Computat Methods Eng

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“…Mechanical optimization problem is closely related to mathematical model. [51,52] The key of constructing optimal design mathematical model is to find design variables, objective functions, and constraints. This section uses HMSWHO to optimize three mechanical design problems, which are tensile/ compression spring design problem (case 1), welded beam design problem (case 2), and pressure vessel design problem (case 3).…”

Section: Optimization Of Mechanical Designmentioning

confidence: 99%

Hybrid Multi‐Strategy Improved Wild Horse Optimizer

Yuan

Han

et al. 2022

Advanced Intelligent Systems

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Wild Horse Optimizer (WHO), a new metaheuristic algorithm proposed in recent years, has some weaknesses in solving practical problems, such as low searching accuracy and slow convergence speed. Herein, a Hybrid Multi‐Strategy improved Wild Horse Optimizer (HMSWHO) is proposed, which includes four strategies to improve the optimization capability. The Halton sequence is used to initialize the foal population to make the population more diverse. The adaptive parameter TDR is improved to balance the global exploration and local exploitation. The simplex method is used to improve the worst position of the population. Wild horse escaping behavior is added to improve search efficiency and optimization accuracy. The main innovation strategies are the improvement of TDR and the addition of escaping behavior. To verify the effectiveness of the improved strategies, 12 benchmark test functions, CEC2017, and CEC2021 test functions are selected for simulation experiments. Mechanical design examples are added for optimization, and the optimization results are 16.61%, 1.65%, and 0.21% less than that of WHO. The results show that the improved algorithm has obvious advantages in convergence speed, accuracy, and stability. HMSWHO can be applied to more practical engineering optimization problems and provide new ideas for structural optimization methods.

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“…Set the population size to N , dimension of the solution space to D , and number of iterations to T (in general,

D&gt;\text{log}_{2}N)

. Based on current literature [33], the time complexity of the basic SSA is given by.

\begin{equation} {O_{\text{SSA}}}(T(N*D)) \end{equation}

…”

Section: Prediction Modelmentioning

confidence: 99%

Improved hybrid sparrow search algorithm for an extreme learning machine neural network for short‐term photovoltaic power prediction in 5G energy‐routing base stations

Yan

Guo

Hu³

et al. 2022

IET Renewable Power Gen

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Given the advancements in solar power generation and fifth-generation (5G) technologies, it is crucial to reduce energy consumption based on accurate predictions of the photovoltaic power requirements of 5G base stations (BSs) connected to renewable energy sources. To ensure sufficient prediction performance even under complex weather conditions and promote two-way flow of energy and information in the power supply system of the 5G BS, this work first proposes a reference scenario for a 5G BS with an energy router. Then, the comprehensive grey relational analysis is used to determine the optimal photovoltaic input variables. Finally, the improved logistic distribution, Laplacian distribution with inverse incomplete gamma-function weight factor, and nonlinear mutation perturbation strategy are introduced to improve the ability of the sparrow search algorithm to avoid locally optimal solutions for an extreme learning machine of the short-term photovoltaic power prediction model. The proposed model is applied to actual measured data for validation, and the results show that this model has the lowest mean percentage error as well as highest prediction accuracy under different weather conditions. The coefficient of determination for the proposed prediction model is greater than 99%; therefore, it is expected to improve the photovoltaic prediction performances of 5G energy-routing BSs as well as promote sustainable development and sharing of energy and information.

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A Hybrid Sparrow Search Algorithm Based on Constructing Similarity

Cited by 47 publications

References 30 publications

Recent Versions and Applications of Sparrow Search Algorithm

Recent Versions and Applications of Sparrow Search Algorithm

Hybrid Multi‐Strategy Improved Wild Horse Optimizer

Improved hybrid sparrow search algorithm for an extreme learning machine neural network for short‐term photovoltaic power prediction in 5G energy‐routing base stations

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