發(fā)布時(shí)間：2018-06-27 02:37

  本文選題：生物地理學(xué)優(yōu)化 + 遷移策略　； 參考：《計(jì)算機(jī)應(yīng)用》2017年05期

【摘要】：針對生物地理學(xué)優(yōu)化(BBO)算法搜索能力不足的缺點(diǎn),提出基于螢火蟲算法局部決策域策略的改進(jìn)遷移操作來提算法的全局尋優(yōu)能力。改進(jìn)的遷移操作能夠在考慮不同棲息地各自的遷入率與遷出率的基礎(chǔ)上,進(jìn)一步利用棲息地之間的相互影響關(guān)系。將改進(jìn)算法應(yīng)用于12個(gè)典型的函數(shù)優(yōu)化問題來測試改進(jìn)生物地理學(xué)優(yōu)化算法的性能,驗(yàn)證了改進(jìn)算法的有效性。與BBO、改進(jìn)BBO(IBBO)、基于差分進(jìn)化的BBO(DE/BBO)算法的實(shí)驗(yàn)結(jié)果表明,改進(jìn)算法提高了算法的全局搜索能力、收斂速度和解的精度。
[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.
【作者單位】： 山東師范大學(xué)信息科學(xué)與工程學(xué)院;山東省分布式計(jì)算機(jī)軟件新技術(shù)重點(diǎn)實(shí)驗(yàn)室;Department
【基金】：國家自然科學(xué)基金資助項(xiàng)目(61373148,61502151) 山東省自然科學(xué)基金資助項(xiàng)目(ZR2014FL010) 山東省社會科學(xué)規(guī)劃項(xiàng)目(2012BXWJ01,15CXWJ13,16CFXJ05)~~
【分類號】：TP18
，

本文編號：2072343