A Modified Sparrow Search Algorithm with Application in 3d Route Planning for UAV

Liu, Guiyun; Shu, Cong; Liang, Zhongwei; Peng, Baihao; Cheng, Lefeng

doi:10.3390/s21041224

Cited by 147 publications

(54 citation statements)

References 37 publications

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“…e parameter Q is a random number that obeys the standard normal distribution. e parameter L indicates that the elements are all the matrix of 1. e parameters R 2 and ST represent the warning value and the safety value [29]. When R 2 < ST, the population does not find the presence of predators or other dangers, the search environment is safe, and the discoverer can search extensively to guide the population to obtain higher fitness.…”

Section: Sparrow Search Algorithmmentioning

confidence: 99%

A Data Collection Strategy for Heterogeneous Wireless Sensor Networks Based on Energy Efficiency and Collaborative Optimization

Cao

Yue

Zhang

2021

Computational Intelligence and Neuroscience

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In the clustering routing protocol, prolonging the lifetime of the sensor network depends to a large extent on the rationality of the cluster head node selection. The selection of cluster heads for heterogeneous wireless sensor networks (HWSNs) does not consider the remaining energy of the current nodes and the distribution of nodes, which leads to an imbalance of network energy consumption. A strategy for selecting cluster heads of HWSNs based on the improved sparrow search algorithm- (ISSA-) optimized self-organizing maps (SOM) is proposed. In the stage of cluster head selection, the proposed algorithm establishes a competitive neural network model at the base station and takes the nodes of the competing cluster heads as the input vector. Each input vector includes three elements: the remaining energy of the node, the distance from the node to the base station, and the number of neighbor nodes of the node. The best cluster head is selected through the adaptive learning of the improved competitive neural network. When selecting the cluster head node, comprehensively consider the remaining energy, the distance, and the number of times the node becomes a cluster head and optimize the cluster head node selection strategy to extend the network life cycle. Simulation experiments show that the new algorithm can reduce the energy consumption of the network more effectively than the basic competitive neural network and other algorithms, balance the energy consumption of the network, and further prolong the lifetime of the sensor network.

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Section: Sparrow Search Algorithmmentioning

confidence: 99%

A Data Collection Strategy for Heterogeneous Wireless Sensor Networks Based on Energy Efficiency and Collaborative Optimization

Cao

Yue

Zhang

2021

Computational Intelligence and Neuroscience

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show abstract

“…At present, there are some other efforts trying to improve the SSA. For example, Liu et al [29] used the chaos to strengthen the diversity of the population and use Cauchy-Gaussian mutation to avoid obvious local optimization. Zhou et al [30] modified the location update of scrounger by introducing GA strategy to achieve a higher convergence rate than the traditional SSA.…”

Section: Authors Algorithms Strategymentioning

confidence: 99%

A Fractional-Order Chaotic Sparrow Search Algorithm for Enhancement of Long Distance Iris Image

Xiong

Zhang

et al. 2021

Mathematics

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At present, iris recognition has been widely used as a biometrics-based security enhancement technology. However, in some application scenarios where a long-distance camera is used, due to the limitations of equipment and environment, the collected iris images cannot achieve the ideal image quality for recognition. To solve this problem, we proposed a modified sparrow search algorithm (SSA) called chaotic pareto sparrow search algorithm (CPSSA) in this paper. First, fractional-order chaos is introduced to enhance the diversity of the population of sparrows. Second, we introduce the Pareto distribution to modify the positions of finders and scroungers in the SSA. These can not only ensure global convergence, but also effectively avoid the local optimum issue. Third, based on the traditional contrast limited adaptive histogram equalization (CLAHE) method, CPSSA is used to find the best clipping limit value to limit the contrast. The standard deviation, edge content, and entropy are introduced into the fitness function to evaluate the enhancement effect of the iris image. The clipping values vary with the pictures, which can produce a better enhancement effect. The simulation results based on the 12 benchmark functions show that the proposed CPSSA is superior to the traditional SSA, particle swarm optimization algorithm (PSO), and artificial bee colony algorithm (ABC). Finally, CPSSA is applied to enhance the long-distance iris images to demonstrate its robustness. Experiment results show that CPSSA is more efficient for practical engineering applications. It can significantly improve the image contrast, enrich the image details, and improve the accuracy of iris recognition.

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“…The SSA has high search accuracy, fast convergence, high stability, and robustness compared to other population intelligence optimization algorithms. Additionally, the SSA has been successfully applied in the field of path planning [42] and structural optimization of micro-grids [43].…”

Section: Sparrow Search Algorithmmentioning

confidence: 99%

A Combined Model Based on EOBL-CSSA-LSSVM for Power Load Forecasting

et al. 2021

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Inaccurate electricity load forecasting can lead to the power sector gaining asymmetric information in the supply and demand relationship. This asymmetric information can lead to incorrect production or generation plans for the power sector. In order to improve the accuracy of load forecasting, a combined power load forecasting model based on machine learning algorithms, swarm intelligence optimization algorithms, and data pre-processing is proposed. Firstly, the original signal is pre-processed by the VMD–singular spectrum analysis data pre-processing method. Secondly, the noise-reduced signals are predicted using the Elman prediction model optimized by the sparrow search algorithm, the ELM prediction model optimized by the chaotic adaptive whale algorithm (CAWOA-ELM), and the LSSVM prediction model optimized by the chaotic sparrow search algorithm based on elite opposition-based learning (EOBL-CSSA-LSSVM) for electricity load data, respectively. Finally, the weighting coefficients of the three prediction models are calculated using the simulated annealing algorithm and weighted to obtain the prediction results. Comparative simulation experiments show that the VMD–singular spectrum analysis method and two improved intelligent optimization algorithms proposed in this paper can effectively improve the prediction accuracy. Additionally, the combined forecasting model proposed in this paper has extremely high forecasting accuracy, which can help the power sector to develop a reasonable production plan and power generation plans.

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A Modified Sparrow Search Algorithm with Application in 3d Route Planning for UAV

Cited by 147 publications

References 37 publications

A Data Collection Strategy for Heterogeneous Wireless Sensor Networks Based on Energy Efficiency and Collaborative Optimization

A Data Collection Strategy for Heterogeneous Wireless Sensor Networks Based on Energy Efficiency and Collaborative Optimization

A Fractional-Order Chaotic Sparrow Search Algorithm for Enhancement of Long Distance Iris Image

A Combined Model Based on EOBL-CSSA-LSSVM for Power Load Forecasting

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