Review and empirical analysis of sparrow search algorithm

Yue, Yinggao; Cao, Li; Lu, Dongwan; Hu, Zhongyi; Xu, Minghai; Wang, Shuxin; Li, Bo; Ding, Haihua

doi:10.1007/s10462-023-10435-1

Cited by 65 publications

(45 citation statements)

References 171 publications

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“…Its ease of implementation and solution accuracy have rapidly drawn attention in engineering applications, demonstrating SSA's effectiveness in complex optimization challenges. SSA faces challenges like low convergence accuracy and local optima entrapment, especially in complex functions [11][12][13][14][15]. Zhang et al enhanced SSA's exploration and diversity using sine chaotic mapping and adaptive weight factors, improving convergence speed [16].…”

Section: Introductionmentioning

confidence: 99%

Enhancing sparrow search algorithm with OCSSA: Integrating osprey optimization and Cauchy mutation for improved convergence and precision

Yong,

Ting,

Peng

2024

Electronics Letters

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This study enhances the traditional sparrow search algorithm (SSA) to address its weaknesses, such as poor convergence rates and precision due to a tendency to fall into local optima. The improved version, OCSSA, integrates the Osprey optimization algorithm (OOA) and Cauchy mutation. Logistic chaotic mapping is used for initial population generation to increase genetic diversity. OOA boosts global exploration in the producer phase, and Cauchy mutation in the scroungers phase disrupts suboptimal solutions, enhancing the algorithm's ability to avoid local optima. OCSSA's performance, validated through ten benchmark functions and fault diagnosis optimization tasks, significantly improves convergence speed and accuracy, proving its effectiveness in complex optimization challenges.

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Section: Introductionmentioning

confidence: 99%

Enhancing sparrow search algorithm with OCSSA: Integrating osprey optimization and Cauchy mutation for improved convergence and precision

Yong,

Ting,

Peng

2024

Electronics Letters

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“…According to the characteristics of the topology of WSNs, routing protocols may be separated into flat routing protocols and cluster routing protocols. The plane routing protocol’s nodes all have the same structure and purpose, but because resource optimization management cannot be carried out without a central management node, it is only appropriate for small-scale networks [ 6 , 7 ]. The network is divided into numerous clusters using the clustering routing protocol.…”

Section: Introductionmentioning

confidence: 99%

A Novel Topology Optimization Protocol Based on an Improved Crow Search Algorithm for the Perception Layer of the Internet of Things

et al. 2023

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In wireless sensor networks, each sensor node has a finite amount of energy to expend. The clustering method is an efficient way to deal with the imbalance in node energy consumption. A topology optimization technique for wireless sensor networks based on the Cauchy variation optimization crow search algorithm (CM-CSA) is suggested to address the issues of rapid energy consumption, short life cycles, and unstable topology in wireless sensor networks. At the same time, a clustering approach for wireless sensor networks based on the enhanced Cauchy mutation crow search algorithm is developed to address the issue of the crow algorithm’s sluggish convergence speed and ease of falling into the local optimum. It utilizes the Cauchy mutation to improve the population’s variety and prevent settling for the local optimum, as well as to broaden the range of variation and the capacity to carry out global searches. When the leader realizes he is being followed, the discriminative probability is introduced to improve the current person’s location update approach. According to the simulation findings, the suggested CM-CSA algorithm decreases the network’s average energy consumption by 66.7%, 50%, and 33.3% and enhances its connectivity performance by 52.9%, 37.6%, and 23.5% when compared to the PSO algorithm, AFSA method, and basic CSA algorithm.

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“…Furthermore, the fuzzy controller has the advantage of lower computing needs, making it suitable for applications in lithium-ionization electrical energy converters, where embedded chips function as processors. We employ the sparrow search algorithm (SSA) [12]- [13] to identify the optimal values and address the issue of suboptimal performance caused by the inappropriate selection of proportional and quantization factors. Hence, the overall performance of the fuzzy controller is improved.…”

Section: Introductionmentioning

confidence: 99%

Design of Fuzzy Controller for Lithium Battery Formation LLC Converter Based on Sparrow Search Algorithm Optimization

Li,

Xie

2023

J. Phys.: Conf. Ser.

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This paper provides a novel fuzzy PI controller design for LLC resonant converters that make use of the sparrow search algorithm optimization. The LLC resonant converter is a complex nonlinear and time-varying system. As the trend in lithium battery energy conversion systems shifts toward high-power applications, they frequently encounter wide load variations. When employing traditional linear PI controllers of LLC resonant converters, the transient response is slow and fails to adapt effectively to the requirements of wide load changes. In this proposed approach, fuzzy control and PI control are combined to enhance the controller’s flexibility and transient response characteristics. The PI controller parameters are continuously adjusted online, incorporating fuzzy control outputs to improve the controller’s performance. Furthermore, the sparrow search algorithm is applied to optimize the proportional and quantization factors, minimizing the impact of improper parameter selection on the performance of the fuzzy PI controller. This method successfully addresses the challenges posed by the LLC resonant converter, improving adaptability and transient response characteristics in varying load conditions by combining fuzzy control and PI control, online parameter modification, and utilizing the sparrow search algorithm to optimize proportional and quantization factors.

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Review and empirical analysis of sparrow search algorithm

Cited by 65 publications

References 171 publications

Enhancing sparrow search algorithm with OCSSA: Integrating osprey optimization and Cauchy mutation for improved convergence and precision

Enhancing sparrow search algorithm with OCSSA: Integrating osprey optimization and Cauchy mutation for improved convergence and precision

A Novel Topology Optimization Protocol Based on an Improved Crow Search Algorithm for the Perception Layer of the Internet of Things

Design of Fuzzy Controller for Lithium Battery Formation LLC Converter Based on Sparrow Search Algorithm Optimization

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