计算机科学 ›› 2011, Vol. 38 ›› Issue (7): 220-224.
• 人工智能 • 上一篇 下一篇
黄正新,周永权
出版日期:
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基金资助:
HUANG Zhcng-xin,ZHOU Yong-quan
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摘要: 针对萤火虫群优化(GSO)算法优化多模态函数存在收敛速度慢和求解精度低等缺陷,提出一种自适应步长萤火虫群多模态函数优化算法((SASGSO)。该算法解决了萤火虫群优化(GSO)算法优化多模态函数所存在的不足;同时SASGSO算法也可找到多模态函数的所有极值点。数值实验仿真表明,该算法具有操作简单、易理解、收敛速度快和求解精度高等优点。
关键词: 人工萤火虫,多模态函数,GSO,SASGSO
Abstract: Because the GSO algorithm has slow convergence and low precision defects when optimizing the multi modal function, a self-adaptive step glowworm swarm optimization(SASGSO ) algorithms was proposed in this paper. This algorithm can overcome slow convergence and low precision defects of the GSO algorithm simultaneously it can find all peaks of the multi-modal function. Experiments show that,the SASGSO algorithm has the advantages of simple operation,easy to understand,fast convergence rates and high precision.
Key words: Glowworm, Multimodal function, GSO, SASGSO
黄正新,周永权. 自适应步长萤火虫群多模态函数优化算法[J]. 计算机科学, 2011, 38(7): 220-224. https://doi.org/
HUANG Zhcng-xin,ZHOU Yong-quan. Self-adaptive Step Glowworm Swarm Optimization Algorithm for Optimizing Multimodal Functions[J]. Computer Science, 2011, 38(7): 220-224. https://doi.org/
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