计算机科学 ›› 2011, Vol. 38 ›› Issue (7): 220-224.

• 人工智能 • 上一篇    下一篇

自适应步长萤火虫群多模态函数优化算法

黄正新,周永权   

  1. (广西民族大学数学与计算机科学学院 南宁530006)
  • 出版日期:2018-11-16 发布日期:2018-11-16
  • 基金资助:
    本文受自然科学基金项目(0991086),国家民委科研项目基金(08GX01)资助。

Self-adaptive Step Glowworm Swarm Optimization Algorithm for Optimizing Multimodal Functions

HUANG Zhcng-xin,ZHOU Yong-quan   

  • Online:2018-11-16 Published:2018-11-16

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

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