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Sine Cosine Embedded Squirrel Search Algorithm for Global Optimization and Engineering Design
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DOI：10.1007/s10586-023-04172-x.png)
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The Squirrel Search Algorithm (SSA) is an innovative optimization method that takes inspiration from the foraging and gliding behavior of squirrels. Despite its simple structure and stable performance, it is prone to the same issues as other algorithms, such as falling into local optima and experiencing premature convergence. To address this problem, this paper proposes an improved squirrel search algorithm embedded with the Sine Cosine Algorithm (SCSSA). Firstly, the Sine Cosine Algorithm is introduced into the SSA to enhance its local exploitation ability. Secondly, the Sobol sequence is utilized to generate the initial population, resulting in higher quality initial solutions. Thirdly, dimensional learning is applied to squirrels on both hickory and oak trees, promoting population diversity and preventing local optima. Finally, the glide constant Gc in SSA is adjusted to decay nonlinearly with iteration count, starting with a large value that gradually decreases in the early stage to facilitate global exploration, and then rapidly decreasing in the later stage to promote local exploitation. Extensive experiments are conducted on 23 classic benchmark functions, the CEC2017 test set, and three engineering problems. The experimental results show that SCSSA can effectively maintain population diversity and can achieve a balance between exploration and exploitation. It consistently outperforms the comparison algorithms in terms of numerical optimization and convergence rate.
Keyword:
Squirrel search algorithm
Sine cosine algorithm
Dimensional learning
Sobol
Optimization
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4.1
论文数:
5.1K
被引数:
7.5K
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