摘要
针对带全局约束条件的工作流可靠性计算问题,提出一种基于均匀多样性适应度函数的多子群协同进化算法,将工作流可靠性约束优化转化为双目标优化问题;为提高粒子在进化过程中的搜索能力,进化群体被分解为若干子群;综合考虑双目标优化问题的特点,设计了一种新颖实用的均匀多样性适应度函数,让各子群体在不同方向上协同搜索目标解;最后根据其适应度排序构造了基于非支配集合的全局最优解.仿真实验表明所提算法具有良好的效率,求得的最优解集全部满足约束条件,且分布和质量均优于基于非支配档案的混合离散粒子群算法.
As dynamic collaboration prevails, the reliability of collaborative workflow becomes more important. To deal with this prob- lem,a novel population co-evolutionary reliability model is proposed for cloud service-oriented workflow systems. Then, the constrained reliability optimization problem is converted into the bi-objective optimization, and a novel discrete particle swarm optimization model is presented. In the proposed model, a new uniform diversity fitness measure is defined to search the distributed Pareto solu- tions. The global non-dominated set of optimal solutions is constructed based on the fitness measures. Experimental results illustrate the effectiveness of the proposed model.
出处
《小型微型计算机系统》
CSCD
北大核心
2017年第2期273-276,共4页
Journal of Chinese Computer Systems
基金
国家自然科学基金项目(61272036)资助
关键词
可靠性计算
工作流调度
约束优化
离散粒子群优化
reliability evaluation
service-oriented workflow scheduling
constrained optimization
discrete particle swarm optimization
