A vector-encirclement-model-based sparrow search algorithm for engineering optimization and numerical optimization problems

Hong, Jiale; Shen, Bo; Xue, Jiankai; Pan, Anqi

doi:10.1016/j.asoc.2022.109777

Cited by 12 publications

(4 citation statements)

References 38 publications

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“…In addition to the original versions of metaheuristic algorithms, many researchers have tried to improve the performance of existing algorithms by developing their improved versions, such as the Enhanced Snake Optimizer (ESO) [ 71 ], Improved Sparrow Search Algorithm (ISSA) [ 72 ], and multi-strategy-based Adaptive Sine–Cosine Algorithm (ASCA) [ 73 ].…”

Section: Literature Reviewmentioning

confidence: 99%

Botox Optimization Algorithm: A New Human-Based Metaheuristic Algorithm for Solving Optimization Problems

Hubálovská,

Hubálovský,

Trojovský

2024

Biomimetics

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This paper introduces the Botox Optimization Algorithm (BOA), a novel metaheuristic inspired by the Botox operation mechanism. The algorithm is designed to address optimization problems, utilizing a human-based approach. Taking cues from Botox procedures, where defects are targeted and treated to enhance beauty, the BOA is formulated and mathematically modeled. Evaluation on the CEC 2017 test suite showcases the BOA’s ability to balance exploration and exploitation, delivering competitive solutions. Comparative analysis against twelve well-known metaheuristic algorithms demonstrates the BOA’s superior performance across various benchmark functions, with statistically significant advantages. Moreover, application to constrained optimization problems from the CEC 2011 test suite highlights the BOA’s effectiveness in real-world optimization tasks.

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Section: Literature Reviewmentioning

confidence: 99%

Botox Optimization Algorithm: A New Human-Based Metaheuristic Algorithm for Solving Optimization Problems

Hubálovská,

Hubálovský,

Trojovský

2024

Biomimetics

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“…These strategies improved solution quality and accelerated convergence. Hong et al [10] proposed an improved Sparrow Search Algorithm (ISSA), which introduced a strategy focusing on forager concentration to update forager positions, enhancing global search capabilities. They also put forward vector enclosure models and direction selection strategies to update joiner positions, thus enhancing local search capabilities.…”

Section: Multiple Mechanism Improvementsmentioning

confidence: 99%

Overview and Application of Sparrow Search Algorithm in Deep Learning

Wang,

Fei,

Liu

et al. 2024

Frontiers in Artificial Intelligence and Applications

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A brand-new swarm intelligence optimization algorithm called the Sparrow Search Algorithm (SSA) was put forth in 2020. By simulating the foraging process of sparrows, SSA efficiently solves optimization problems, exhibiting advantages such as faster convergence and excellent optimization abilities. This paper introduces the basic principles of the Sparrow Search Algorithm, analyzes its existing issues, summarizes improvements made to existing Sparrow algorithms, and then discusses the application of SSA in deep learning.

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“…According to the above SSA characteristics, SSA is easy to understand and implement [30]. Furthermore, because the producers, scroungers, and perceivers work together, SSA has better optimization efficiency compared with other popular evolutionary algorithms [31,32].…”

Section: Ssamentioning

confidence: 99%

A multi-strategy improved sparrow search algorithm for mobile robots path planning

Fan,

2024

Meas. Sci. Technol.

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Path planning for mobile robots plays a vital role in task execution, given the constraints imposed by environments and energy resources. It poses a significant challenge for mobile robots, requiring them to find a feasible path between the start point and target point that is obstacle-free and as short as possible. To address the challenge of path planning, a multi-strategy improved sparrow search algorithm with crazy operator (CMISSA) is proposed. Firstly, Tent chaos mapping and reverse learning are introduced into the population initialization of sparrow search algorithm (SSA) to enhance the uniformity and effectiveness of the initial population distribution. Secondly, adaptive parameters are applied in SSA to maintain a balance between exploitation and exploration. Thirdly, to prevent SSA from getting trapped in local optima, the crazy operator is used to perturb the population position. Finally, a novel adaptive boundary control strategy is introduced to handle the location of individuals that have crossed the boundary. In addition, the experimental results on 15 classical benchmark functions show that CMISSA has better optimization performance than other 10 comparison algorithms. Furthermore, in the path planning experimental results, the results of comparing CMISSA with 5 comparison algorithms on 5 different environments reveal CMISSA's average path shortening rates were 34.90%, 20.11%, 29.01%, 51.97%, 37.42%, respectively. It is further demonstrated that CMISSA has superior availability for solving mobile robots path planning.

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A vector-encirclement-model-based sparrow search algorithm for engineering optimization and numerical optimization problems

Cited by 12 publications

References 38 publications

Botox Optimization Algorithm: A New Human-Based Metaheuristic Algorithm for Solving Optimization Problems

Botox Optimization Algorithm: A New Human-Based Metaheuristic Algorithm for Solving Optimization Problems

Overview and Application of Sparrow Search Algorithm in Deep Learning

A multi-strategy improved sparrow search algorithm for mobile robots path planning

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