A Bio-inspired Method Inspired by Arachnida Salticidade

版本 1.0.1 (5.6 KB) 作者: Hernan Peraza
A novel meta-heuristic called Jumping Spider Optimization Algorithm (JSOA) is proposed.
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This paper proposes a new meta-heuristic called Jumping Spider Optimization Algorithm (JSOA), inspired by Arachnida Salticidae hunting habits. The proposed algorithm mimics the behavior of spiders in nature and mathematically models its hunting strategies: search, persecution, and jumping skills to get the prey. These strategies provide a fine balance between exploitation and exploration over the solution search space and solve global optimization problems. JSOA is tested with 20 well-known testbench mathematical problems taken from the literature. Further studies include the tuning of a Proportional-Integral-Derivative (PID) controller, the Selective harmonic elimination problem, and a few real-world single objective bound-constrained numerical optimization problems taken from CEC 2020. Additionally, the JSOA’s performance is tested against several well-known bio-inspired algorithms taken from the literature. The statistical results show that the proposed algorithm outperforms recent literature algorithms and is capable to solve challenging real-world problems with unknown search space.
Research Paper: https://doi.org/10.3390/math10010102

引用格式

Peraza-Vázquez, Hernán, et al. “A Bio-Inspired Method for Mathematical Optimization Inspired by Arachnida Salticidade.” Mathematics, vol. 10, no. 1, MDPI AG, Dec. 2021, p. 102, doi:10.3390/math10010102.

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