Mathematical programming and meta-heuristics are two types of optimization methods. Meta-heuristic algorithms can identify optimal/near-optimal solutions by mimicking natural behaviours or occurrences and provide benefits such as simplicity of execution, a few parameters, avoidance of local optimization, and flexibility. Many meta-heuristic algorithms have been introduced to solve optimization issues, each of which has advantages and disadvantages. Studies and research on presented meta-heuristic algorithms in prestigious journals showed they had good performance in solving hybrid, improved and mutated problems. This paper reviews the sparrow search algorithm (SSA), one of the new and robust algorithms for solving optimization problems. This paper covers all the SSA literature on variants, improvement, hybridization, and optimization. According to studies, the use of SSA in the mentioned areas has been equal to 32%, 36%, 4%, and 28%, respectively. The highest percentage belongs to Improved, which has been analyzed by three subsections: Meat-Heuristics, artificial neural networks, and Deep Learning. 

https://link.springer.com/content/pdf/10.1007/s11831-022-09804-w.pdf

Advances in Sparrow Search Algorithm: A Comprehensive Survey

Review and empirical analysis of sparrow search algorithm

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. 

/pdf/recent-versions-and-applications-of-sparrow-search-algorithm-16tqd739.pdf

Recent Versions and Applications of Sparrow Search Algorithm

Short-term load forecasting (STLF) is necessary for system operators; however, its difficulty has been increasing since distributed resources, particularly behind-the-meter (BTM) PV resources, have been introduced to power systems. This study proposes a framework for STLF for holidays considering the four major factors that affect the net load profiles —calendar, trend, weather, and BTM PV. The target holiday is first paired with historical holidays following its calendar factor, which are defined as “similar days.” Subsequently, in terms of the remaining three factors, the differences between the historical holidays and target holidays are calculated, and their effects on load differences (factor-induced load differences) are quantified and reflected. Finally, for each pair, the modified load profiles are generated and combined to obtain a daily load profile of the target holiday. The proposed framework was implemented on a case study of Korean national holidays, and its forecasting accuracy was compared with conventional forecasting methods. The accuracy metrics show that the proposed framework outperforms conventional methods. The results suggest that the proposed framework can be applied to STLF for holidays to improve forecasting accuracy. 

/pdf/day-ahead-short-term-load-forecasting-for-holidays-based-on-5lfn7f1v.pdf

Day-Ahead Short-Term Load Forecasting for Holidays Based on Modification of Similar Days’ Load Profiles

To mine the battery’s health factors more comprehensively and accurately identify the lithium battery’s State of Health (SOH), an Improved Douglas–Peucker feature extraction algorithm is proposed, and the LAOS-XGboost model is proposed to be used to predict the SOH of the battery. Firstly, to solve the problem that the traditional Douglas–Peucker algorithm has difficulties extracting curve features in a fixed dimension, the Douglas–Peucker algorithm is improved by de-thresholding. Then, the Wrapper method combined with the Improved Douglas–Peucker algorithm is used to construct the feature engineering of battery life prediction, and the optimal feature subset is obtained. Then, LAOS-XGboost is used to establish a battery SOH prediction model; finally, this model is used to predict the SOH of different batteries and the same battery, and the robustness of the model is analyzed. The experimental results show that the R2 of all XGboost models is higher than 0.97 in the prediction experiments of different batteries. The AE of the LAOS-XGboost model is 0, and the TIC index is less than 3% under 10 dB SNR. In the same battery prediction experiment, the TIC index of the model is less than 0.3%.

https://www.mdpi.com/1996-1073/15/16/5981/pdf?version=1660821112

Lithium Battery Health Factor Extraction Based on Improved Douglas–Peucker Algorithm and SOH Prediction Based on XGboost

To address the problem that the difficulty of selecting parameters in the XGBoost model makes it difficult to optimize the regression effect, a short-term load forecasting model based on the sparrow search algorithm to optimize XGBoost is proposed. Similar days are selected as the training set by the GRA algorithm, the mean absolute error obtained by cross-validation is used as the fitness function, the sparrow search algorithm (SSA) is used to optimize the XGBoost covariate selection process, and the SSA-XGBoost load forecasting model is constructed, and finally the load is corrected by the compensation model to obtain the final load forecasting data. Taking the load data of a region in Zhejiang Province from January 2019 to December 2020 as an example, the prediction ability of the SSA-XGBoost load forecasting model is examined through five experiments. The experimental results show that (i) the parameters of SVM, RF, and XGBoost models can be optimized using the SSA algorithm, and SSA-SVM, SSA-RF, and SSA-XGBoost can quickly calculate the load forecasting data, among which the SSA-XGBoost model has the highest accuracy. Compared with kmeans and other clustering methods, this paper uses the GRA algorithm to select similar days more reasonably, with smaller prediction errors and a controllable number of training sets. The compensation model improves the prediction accuracy of the model by correcting the SSA-XGBoost load prediction data.

Optimized XGBoost based sparrow search algorithm for short-term load forecasting

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Optimized XGBoost based sparrow search algorithm for short-term load forecasting