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To solve the problem that the emerging sparrow search algorithm (SSA) lacks systematic comparison and analysis with other classical algorithms, this paper first introduces the principle of the sparrow search algorithm and then describes the mathematical model and algorithm description of the sparrow search algorithm. By comparing several classical...
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Due to the outbreak of COVID-19, the last couple of years have been drastic in terms of human behavioral patterns. People were forced to stay at home for a very long duration because of the strict lockdown measures imposed by governments all over the globe. India was no exception, wherein the Indian government imposed several very strict lockdowns...
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... ant colony optimization (ACO) [9], etc. Some group intelligent optimization algorithms proposed in recent years includeSimulated Annealing (SA) [10], Simulated Annealing (SA) [11], Grey Wolf optimization (GWO) [12], Whale Optimization Algorithm(WOA) [13], Dung beetle optimizer (DBO) [14], Harris Hawks Optimization (HHO) [15], Harris Hawks Optimization (HHO) [16],and so on. Liu et al. introduced sinusoidal chaos theory instead of random population initialization, and then introduced adaptive inertia weights into the whale position update formula to improve the optimization performance of the algorithm. ...
In this paper, aiming at the problem that the balance between exploration and exploitation of traditional Tasmanian devil optimization algorithm is unflexible, and easy to fall into local optimum, an improved Tasmanian devil optimization algorithm (NTDO) based on the sine-cosine strategy of dynamic weighted factors is proposed. The designed method balances the global and local search capabilities of the algorithm, effectively improves the situation that the algorithm falls into local optimum, and integrally improves the optimization performance of the algorithm. Firstly, the good point set theory is used instead of the traditional random method to find the initial individuals, which can render the initial population is more evenly distributed in the search space and the population diversity is improved. Secondly, A sine-cosine strategy based on dynamic weighted factors is proposed to coordinate the global exploration and local optimization capabilities of the algorithm, and enhance the convergence accuracy of the algorithm. Thirdly, since Tasmanian devil is easy to fall into local optimum in the process of hunting prey, a nonlinear decline strategy based on oscillation factor is presented, which increases the search range of the algorithm and improves the ability of the algorithm to jump out of the local optimal value.Finally, 12 evaluation functions, cec2019 and cec2021 test functions commonly used in NTDO and TDO, WOA, DBO, PSO, GWO, DFPSO and PDGWO algorithms are compared and analyzed, and the experimental results show the effectiveness and feasibility of the scheme.
... Sparrow Search Algorithm (SSA) is a population optimization algorithm. Based on observing the local optimal solution of the target problem, the SSA algorithm iteratively searches for the global optimal solution, which has the characteristics of global exploration and local optimization (Dong et al., 2022;Yan et al., 2022;Yue et al., 2023). Through the global search strategy and the ability to regenerate the initial solution, the sparrow search algorithm can help ELM and BP algorithms to jump out of the local optimal solution and discover a better combination of weights. ...
Moisture content testing of agricultural products is critical for quality control, processing efficiency and storage management. Testing foxtail millet moisture content ensures stable foxtail millet quality and helps farmers determine the best time to harvest. A differential capacitance moisture content detection device was designed based on STM32 and PCAP01 capacitance digital converter chip. The capacitance method combined with the back-propagation(BP) algorithm and the extreme learning machine(ELM) algorithm was chosen to construct an analytical model for foxtail millet moisture content, temperature, and volume duty cycle. This work performs capacitance measurements on foxtail millet with different moisture contents, temperatures, and proportions of the measured substance occupying the detection area (that is, the volumetric duty cycle). On this foundation, the sparrow search algorithm (SSA) is used to optimize the BP and ELM models. However, SSA may encounter problems such as falling into local optimization solutions due to the reduction of population diversity in the late iterations. As a consequence, Logistic algorithm is introduced to optimize SSA, making it more appropriate for solving specific problems. Upon comparative analysis, the model predicted using the Logistic-SSA-ELM algorithm was more accurate. The results indicate that the predicted values of prediction set coefficient of determination (RP), prediction set root mean square error (RMSEP) and prediction set ratio performance deviation (RPDP) were 0.7016, 3.7150 and 1.4035, respectively. This algorithm has excellent prediction performance and can be used as a model for detection of foxtail millet moisture content. In view of the important role of foxtail millet moisture content detection in acquisition and storage, it is particularly important to study a nondestructive and fast online real-time detection method. The designed capacitive sensor with differential structure has well stabilization and high accuracy, which can be further studied in depth and gradually move towards the general trend of agricultural development of smart agriculture and precision agriculture.
... The sparrow search algorithm (SSA) is an optimization algorithm with global search capability and the capability to escape the local optimal value [19]. The algorithm mainly implements the search process by introducing early warning behavior and a division of labor strategy [20,21]. Therefore, this study used the SSA algorithm to optimize parameters of the neural network forecasting model. ...
To reach the target yield of crops, nutrient management is essential. Selecting the appropriate prediction model and adjusting the nutrient supply based on the actual situation can effectively improve the nutrient utilization efficiency, crop yield, and product quality. Therefore, a prediction model of the NPK fertilizer application rate for greenhouse tomatoes under the target yield was studied in this study. Under low, medium, and high soil fertility conditions, a neural network prediction model based on the sparrow search algorithm (SSA-NN), a neural network prediction model based on the improved sparrow search algorithm (ISSA-NN), and a neural network prediction model based on the hybrid algorithm (HA-NN) were used to predict the NPK fertilizer application rate for greenhouse tomatoes. The experimental results indicated that the evaluation indexes (i.e., the mean square error (MSE), explained variance score (EVS), and coefficient of determination (R2)) of the HA-NN prediction model proposed in this study were superior than the SSA-NN and ISSA-NN prediction models under three different soil fertility conditions. Under high soil fertility, compared with the SSA-NN prediction model, the MSE of the ISSA-NN and HA-NN prediction models decreased to 0.007 and 0.005, respectively; the EVS increased to 0.871 and 0.908, respectively; and the R2 increased to 0.862 and 0.899, respectively. This study showed that the HA–NN prediction model was superior in predicting the NPK fertilizer application rate for greenhouse tomatoes under three different soil fertility conditions. Due to the significance of NPK fertilizer application rate prediction for greenhouse tomatoes, this technique is expected to bring benefits to agricultural production management and decision support.
... To provide context for this study, it is essential to recognize the foundational efforts laid by prior research. Several notable works have undertaken the compilation of existing knowledge and insights regarding application of optimization methods and path planning methodologies [1], [2], [3], [4], [5], [6], [7] with the aim of further enriching the existing corpus of literature in technical perspective. Optimization methods play a pivotal role in path planning, providing the means to generate efficient paths. ...
Over recent decades, the field of mobile robot path planning has evolved significantly, driven by the pursuit of enhanced navigation solutions. The need to determine optimal trajectories within complex environments has led to the exploration of diverse path planning methodologies. This paper focuses on a specific subset: Bio-inspired Population-based Optimization (BPO) methodologies. BPO methods play a pivotal role in generating efficient paths for path planning. Amidst the abundance of optimization approaches over the past decade, only a fraction of studies has effectively integrated these methods into path planning strategies. This paper’s focus is on the years 2014-2023, reviewing BPO techniques applied to mobile robot path planning challenges. Contributions include a comprehensive review of recent BPO methods in mobile robot path planning, along with an experimental methodology to compare them under consistent conditions. This encompasses the same environment, initial conditions, and replicates. A multi-objective function is incorporated to evaluate optimization methods. The paper delves into key concepts, mathematical models, and algorithm implementations of examined optimization techniques. The experimental setup, methodology, and benchmarking performance results are discussed. Based on the proposed experimental methodology, Improved Sparrow Search Algorithm (ISpSA) shows the best cost improvement percentage (7.87%), but suffers in terms of optimization time. On the other hand, Whale Optimization Algorithm (WOA) has lesser improvement percentage of 6.05% but better optimization time. In conclusion, the standardized approach for benchmarking BPO algorithms provides useful insights into their strengths and challenges in mobile robot path planning.
... The SSA is a nascent optimization algorithm rooted in swarm intelligence principles [34] that learns from the behavioral strategies of sparrows, including foraging and anti-predation. The authors of [35] compared the performance of four emerging intelligent optimization algorithms: Grey Wolf optimization (GWO), particle swarm optimization (PSO), the differential evolution (DE) algorithm, and the SSA. Their experimental results indicated that the SSA exhibits strong local search ability under a variety of test function experiments, and has the advantages of high precision and fast convergence speed. ...
Satellite clock error is a key factor affecting the positioning accuracy of a global navigation satellite system (GNSS). In this paper, we use a gated recurrent unit (GRU) neural network to construct a satellite clock bias forecasting model for the BDS-3 navigation system. In order to further improve the prediction accuracy and stability of the GRU, this paper proposes a satellite clock bias forecasting model, termed ITSSA-GRU, which combines the improved sparrow search algorithm (SSA) and the GRU, avoiding the problems of GRU’s sensitivity to hyperparameters and its tendency to fall into local optimal solutions. The model improves the initialization population phase of the SSA by introducing iterative chaotic mapping and adopts an iterative update strategy based on t-step optimization to enhance the optimization ability of the SSA. Five models, namely, ITSSA-GRU, SSA-GRU, GRU, LSTM, and GM(1,1), are used to forecast the satellite clock bias data in three different types of orbits of the BDS-3 system: MEO, IGSO, and GEO. The experimental results show that, as compared with the other four models, the ITSSA-GRU model has a stronger generalization ability and forecasting effect in the clock bias forecasting of all three types of satellites. Therefore, the ITSSA-GRU model can provide a new means of improving the accuracy of navigation satellite clock bias forecasting to meet the needs of high-precision positioning.
... This behavior might be related to SSA's large randomness issue, easily falling into the local optimum. Moreover, the poor communication mechanism between the participants (that communicate only with the best discoverers) can result in missing the best solutions, affecting fitting quality [67]. ...
... Despite SSA's known fast convergence capacity, the very slow convergence of the BPNN impacted the total computational time of the SSA-BP algorithm [67,69]. ...
Modeling and forecasting the river flow is essential for the management of water resources. In this study, we conduct a comprehensive comparative analysis of different models built for the monthly water discharge of the Buzău River (Romania), measured in the upper part of the river’s basin from January 1955 to December 2010. They employ convolutional neural networks (CNNs) coupled with long short-term memory (LSTM) networks, named CNN-LSTM, sparrow search algorithm with backpropagation neural networks (SSA-BP), and particle swarm optimization with extreme learning machines (PSO-ELM). These models are evaluated based on various criteria, including computational efficiency, predictive accuracy, and adaptability to different training sets. The models obtained applying CNN-LSTM stand out as top performers, demonstrating a superior computational efficiency and a high predictive accuracy, especially when built with the training set containing the data series from January 1984 (putting the Siriu Dam in operation) to September 2006 (Model type S2). This research provides valuable guidance for selecting and assessing river flow prediction models, offering practical insights for the scientific community and real-world applications. The findings suggest that Model type S2 is the preferred choice for the discharge forecast predictions due to its high computational speed and accuracy. Model type S (considering the training set recorded from January 1955 to September 2006) is recommended as a secondary option. Model type S1 (with the training period January 1955–December 1983) is suitable when the other models are unavailable. This study advances the field of water discharge prediction by presenting a precise comparative analysis of these models and their respective strengths
... If the alarm value is greater than the safety threshold, the discoverer will take all the participants out of the dangerous area. The literature [31] is the traditional sparrow algorithm process. This paper improves the traditional sparrow algorithm based on the traditional sparrow algorithm. ...
In this article, the trajectory planning and high-precision motion control of an excavator based on the independent hydraulic system of the load port are studied. A trajectory planning algorithm based on the combination of a quintic non-uniform B-spline curve and improved sparrow algorithm and an oil inlet flow controller based on the time-varying secant barrier Lyapunov function (TSBLF) are designed. First, the traditional sparrow algorithm is innovatively improved in trajectory planning to make the generated trajectory time shorter, more stable and energy better. Then, the secant function and fixed time controller are first introduced in the design of the oil flow controller to ensure that the system error converges to the predefined boundary in finite time. At the same time, the RBF neural network is used to approximate the unmodeled error and disturbance of the system. Finally, the simulation verification is carried out with the common trenching conditions in the intelligent operation of the excavator. The results show that the generated trajectory has obvious advantages over the traditional sparrow algorithm. The trajectory tracking error can converge to the neighborhood near the equilibrium point in a fixed time while satisfying the constraints, and has high control accuracy.
... Yet, achieving effective Hyperparameter Optimization (HPO) remains a critical issue affecting the accuracy, scalability, and applicability of models [17]. Current state-of-the-art optimization algorithms, including the Sparrow Search Algorithm (SSA), still exhibit room for improvement in exploration and exploitation capabilities when addressing HPO challenges [23], [24]. Additionally, current research focuses on modeling individual motor or pump components. ...
... While SSA shows notable advantages in single-optimum problems, its capacity to escape from local optima in multi-optimal scenarios remains moderate [24]. Addressing SSA's limitations in machine learning HPO tasks involving multiple optima presents challenges. ...
This study introduces an Improved Sparrow Search Algorithm (ISSA) to address the challenges of Machine Learning Hyperparameter Optimization (HPO) and efficient modeling of Electric Coolant Pumps (ECP) in electric vehicles. By integrating Lévy flight and Bernoulli mapping, ISSA enhances global search capabilities and ability to escapes from local optima. Experimental validations across 12 benchmark functions demonstrate ISSA outperforming the standard Sparrow Search Algorithm (SSA) and other advanced optimization algorithms in terms of exploratory and exploitative effectiveness. Specifically, ISSA proves exceptionally effective in autonomously handling the HPO for three typical machine learning (ML) algorithms, demonstrating superior performance over SSA and random search methods. A novel ISSA-ML surrogate model for ECP, incorporating structural and operational parameters, showcases significant improvements in predictive accuracy and robustness over traditional polynomial regression and ML models under conventional HPO methods. Furthermore, the application of this surrogate model in multi-objective optimization design for ECPs significantly reduces development time and computational costs, offering a streamlined and cost-effective solution for optimizing ECP performance. This study highlights potential future research directions, including the integration of other ML enhancements and the inclusion of more comprehensive feature parameters, to further improve the model's universality and applicability.
... The sparrow search algorithm (SSA) is a new swarm intelligence optimization algorithm proposed in 2020 [41], based on the behavior of sparrows foraging and evading predators, which has fewer control parameters, higher convergence performance, and local search capability. The SSA algorithm outperforms the gray wolf optimization (GWO), gravitational search algorithm (GSA), and particle swarm optimization algorithm (PSO) in terms of accuracy, convergence speed, and stability [42,43]. ...
... Secondly, we constructed a nonlinear black box model by combining machine learning with new swarm intelligence optimization algorithms [40,41] with good convergence performance and local search capability. The application of this integrated algorithm effectively improved the prediction and generalization capabilities of the model [42]. Finally, according to the results of the case study, unlike previous studies, this integrated framework combined multi-source data with multiple methods and models, which avoids the limited scope of trophic state's identification due to the limitation of the S-2/MSI spectral bands and other factors [23,49]. ...
The trophic state is an important factor reflecting the health state of lake ecosystems. To accurately assess the trophic state of large lakes, an integrated framework was developed by combining remote sensing data, field monitoring data, machine learning algorithms, and optimization algorithms. First, key meteorological and environmental factors from in situ monitoring were combined with remotely sensed reflectance data and statistical analysis was used to determine the main factors influencing the trophic state. Second, a trophic state index (TSI) inversion model was constructed using a machine learning algorithm, and this was then optimized using the sparrow search algorithm (SSA) based on a backpropagation neural network (BP-NN) to establish an SSA-BP-NN model. Third, a typical lake in China (Hongze Lake) was chosen as the case study. The application results show that, when the key environmental factors (pH, temperature, average wind speed, and sediment content) and the band combination data from Sentinel-2/MSI were used as input variables, the performance of the model was improved (R2 = 0.936, RMSE = 1.133, MAPE = 1.660%, MAD = 0.604). Compared with the performance prior to optimization (R2 = 0.834, RMSE = 1.790, MAPE = 2.679%, MAD = 1.030), the accuracy of the model was improved by 12.2%. It is worth noting that this framework could accurately identify water bodies in different trophic states. Finally, based on this framework, we mapped the spatial distribution of TSI in Hongze Lake in different seasons from 2019 to 2020 and analyzed its variation characteristics. The framework can combine regional special feature factors influenced by a complex environment with S-2/MSI data to achieve an assessment accuracy of over 90% for TSI in sensitive waters and has strong applicability and robustness.
... The Sparrow Algorithm refers to an intelligent optimization algorithm proposed through the foraging and anti-predation behavior of sparrow populations [34,35]. The advantages of the sparrow algorithm are elucidated as follows. ...
... Better fitness was selected as the leader, and the position was updated according to formula (33) Update follower and alerter locations according to formulas (34) and ( ...
A hydraulic drive-based self-propelled photovoltaic panel cleaning robot was developed to tackle the challenges of harsh environmental conditions, difficult roads, and incomplete cleaning of dust particles on the photovoltaic panel surface in photovoltaic power plants. The robot has the characteristics of the crawler wheel drive, rear-wheel-independent turning and three-point-independent suspension design, which makes it adhere to the walking requirements of complex environmental terrains, more flexible in turning and automatically levelling so that the stability of the boom mechanism during walking can be ensured. The kinematics model of the upper arm structure equipped with the end cleaning device was built, and the optimized Circle chaotic map and nonlinear weight factor were introduced to enhance the search ability and convergence speed of the sparrow algorithm. Furthermore, the boom running track was optimized in combination with the seven-order non-uniform B-spline curve. Through optimization, the running time of the boom was reduced by 18.7%, and the cleaning efficiency of photovoltaic panel surface was increased. The effectiveness of self-propelled photovoltaic panel cleaning robot cleaning and the reliability of time-optimal trajectory planning were confirmed through simulation and experiment.
