水准点(测量)
算法
计算机科学
群体智能
基于群体的增量学习
搜索算法
理论(学习稳定性)
数学优化
麻雀
函数优化
算法设计
人口
遗传算法
粒子群优化
数学
机器学习
人口学
地理
生态学
大地测量学
生物
社会学
作者
Chunying Jiang,Xiran Zhang,Qingyun Zhou
标识
DOI:10.1109/icmtim58873.2023.10246587
摘要
The Integrated Strategy Sparrow Search Algorithm (ISSA) has been developed as an advanced optimization algorithm to enhance solution accuracy, stability, and to address local optima issues associated with the conventional Sparrow Search Algorithm (SSA). The ISSA algorithm incorporates various strategies, including a chaotic lens strategy, the Golden Sinusoidal Algorithm (GSA), and an adaptive control parameter $\eta$ based on gamma distribution, to augment population diversity, search coverage, exploration, and development capabilities. The performance of the ISSA algorithm has been evaluated using 12 benchmark functions, which demonstrate that the ISSA algorithm outperforms the basic SSA algorithm, other improved algorithms, as well as other swarm intelligence optimization algorithms.
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