基于人工萤火虫局部决策域的改进生物地理学优化算法

发布时间：2018-06-27 02:37

  本文选题：生物地理学优化 + 迁移策略　； 参考：《计算机应用》2017年05期

【摘要】：针对生物地理学优化(BBO)算法搜索能力不足的缺点,提出基于萤火虫算法局部决策域策略的改进迁移操作来提算法的全局寻优能力。改进的迁移操作能够在考虑不同栖息地各自的迁入率与迁出率的基础上,进一步利用栖息地之间的相互影响关系。将改进算法应用于12个典型的函数优化问题来测试改进生物地理学优化算法的性能,验证了改进算法的有效性。与BBO、改进BBO(IBBO)、基于差分进化的BBO(DE/BBO)算法的实验结果表明,改进算法提高了算法的全局搜索能力、收敛速度和解的精度。
[Abstract]:Aiming at the deficiency of the search ability of the Biogeography Optimization (BBO) algorithm, an improved migration operation based on the local decision domain strategy of the firefly algorithm is proposed to improve the global optimization ability of the algorithm. The improved migration operation can further utilize the interaction between habitats on the basis of considering the migration rate and migration rate of different habitats. The improved algorithm is applied to 12 typical function optimization problems to test the performance of the improved biogeographic optimization algorithm, and the effectiveness of the improved algorithm is verified. The experimental results of improved BBO (IBBO) algorithm and BBO (DEP / BBO) algorithm based on differential evolution show that the improved algorithm improves the global searching ability of the algorithm and the precision of convergence speed and concordance.
【作者单位】： 山东师范大学信息科学与工程学院;山东省分布式计算机软件新技术重点实验室;Department
【基金】：国家自然科学基金资助项目(61373148,61502151) 山东省自然科学基金资助项目(ZR2014FL010) 山东省社会科学规划项目(2012BXWJ01,15CXWJ13,16CFXJ05)~~
【分类号】：TP18
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本文编号：2072343