摘要
针对果蝇优化算法的早熟收敛问题,提出了一种新的基于细菌迁徙的自适应果蝇优化算法。该算法在运行过程中根据进化停滞步数的大小自适应地引入细菌迁徙操作,提高算法跳出局部极值的能力;并且对每个个体根据适应值大小赋予不同的自适应迁徙概率,避免了迁徙可能带来的解退化的问题。对几种经典函数的测试结果表明,新算法具有更好的全局搜索能力,在收敛速度、收敛可靠性及收敛精度上比果蝇优化算法有较大的提高。
Considering the premature convergence problem of Fruit Fly Optimization Algorithm （FOA）, a new adaptive fruit fly optimization algorithm based on bacterial migration （AFOABM） is proposed. During the running time, according to the evolutionary stagnation step size, bacterial migration is adaptively introduced into FOA to improve its ability of jumping out of the local extreme; and accord- ing to the fitness values, each individual is assigned different adaptive migration probability in order to avoid the problem of possible solutions degradation resulting from migration. Experimental results show that the new algorithm has the advantages of better global searching ability, speeder convergence and more precise convergence.
出处
《计算机工程与科学》
CSCD
北大核心
2014年第4期690-696,共7页
Computer Engineering & Science
基金
甘肃省自然科学基金资助项目(1208RJZA133)
甘肃省科技支撑计划资助项目(1011NKCA058)
甘肃省教育厅科研基金资助项目(1202-04)
甘肃农业大学青年研究生指导教师扶持基金资助项目(GAV-QNDS-201309)
关键词
细菌觅食
迁徙算子
果蝇优化
自适应
bacterial foraging
bacterial migration
fruit fly optimization algorithm
adaptive
