计算机科学

计算机科学 ›› 2016, Vol. 43 ›› Issue (10): 282-286.doi: 10.11896/j.issn.1002-137X.2016.10.053

• 人工智能 • 上一篇    下一篇

基于迁出地动态选择与自适应迁入策略的BBO算法

唐继勇,仲元昌,曾广朴   

  1. 重庆电子工程职业学院计算机学院 重庆401331,重庆大学飞行器测控与通信教育部重点实验室 重庆400044,长江师范学院计算机工程学院 重庆408100
  • 出版日期:2018-12-01 发布日期:2018-12-01
  • 基金资助:
    本文受重庆市科技攻关项目(cstc2012gg-yyjs40010),重庆市自然科学基金项目(CSTC2008BB2340),重庆市教委科学技术项目(KJ131307)资助

Biogeography-based Optimization with Adaptive Immigration and Dynamic Selection Emigration Strategy

TANG Ji-yong, ZHONG Yuan-chang and ZENG Guang-pu   

  • Online:2018-12-01 Published:2018-12-01

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摘要： Dan Simon用生物地理学的方法和机制来解决工程优化问题,提出了生物地理学优化算法(Biogeography-Based Optimization,BBO)。该算法因其独特的搜索机制和较好的性能在智能优化算法领域得到了广泛的关注。为了进一步提高生物地理学优化算法的全局和局部收索能力,提出了一种基于动态选择迁出地与混合自适应迁入的优化策略,对生物地理学优化算法进行改进,形成一种新的改进型BBO算法。该算法根据进化阶段动态选择待迁出地,并综合当前迁出地和随机迁出地优化迁入策略；同时,设计与适应度相关的变异机制,以增加算法的全局搜索能力。仿真实验结果表明,该算法在全局搜索、收敛速度和收敛精度上均优于对比算法。

关键词: 生物地理学优化,动态选择,自适应迁入,自适应变异

Abstract: Dan Simon proposed a biogeography-based optimization to solve engineering optimization problems.The algorithm has captured the attention of many researchers in the field of intelligent optimization algorithm with its unique search mechanism and good performance.In order to improve the global and local search ability of biogeography-based optimization algorithm,an improved biogeography optimization strategy based on dynamic selection emigration and adaptive immigration was proposed.The improved algorithm mixes the stages of evolution,dynamic selection emigration,random emigration and self-variation to increase the global search ability of the algorithm.The results of simulation experiments show that the algorithm is superior to the contrast algorithm in global searching,convergence speed and convergence accuracy.

Key words: Biogeography-based optimization,Dynamic selection,Adaptive immigrate,Adaptive mutation

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