摘要
为改进果蝇算法应用于云计算中的不足,提出一种改进的混沌元胞果蝇算法(CCF)。利用混沌的遍历性和有界性,通过Logistic映射对算法初值进行优化,改进果蝇算法对初值的敏感性和不稳定性;利用元胞自动机的演化规则,在算法每一轮迭代过程中对果蝇个体的适应度进行演化,选择出最优值并进行替换,改进果蝇算法易出现收敛速度慢和陷入局部最优的缺点。结合实例,对算法进行仿真实验,验证了该算法在多个方面优于果蝇算法,更适合于云计算。
To achieve the improvement to fruit fly optimization algorithm(FOA)in the cloud computing,an improved algorithm of chaos-cellular fruit fly optimization algorithm(CCF)was put forward.Random chaotic signal was generated by logistic map,and the initial value of the algorithm was optimized using the signal to improve the sensitivity and the instability of FOA.In the iterative process,the fitness of every fruit fly was evolved by cellular evolution rules and the optimum solution was selected as the replacement.The proposed method was used to improve the defects of slow convergence speed and falling into the local extremum in FOA.Four function tests and a cloud simulation environment were built by Cloudsim.It is verified that CCF algorithm is better than FOA algorithms,and is more suitable for cloud computing.
作者
战非
曹国震
王建军
张少茹
ZHAN Fei;CAO Guo-zhen;WANG Jian-jun;ZHANG Shao-ru(School of Computer Science,Xi’an Aeronautical Universities,Xi’an 710077,China;Health Science Center,Xi’an Jiaotong University,Xi’an 710049,China)
出处
《计算机工程与设计》
北大核心
2018年第2期452-457,510,共7页
Computer Engineering and Design
基金
国家自然科学基金项目(71373203)
陕西省工业科技攻关基金项目(2016GY-139)
关键词
云计算
果蝇算法
混沌理论
元胞自动机
云仿真
cloud computing
fruit fly optimization algorithm
chaos theory
cellular automata
cloud simulation
