基于双指标分组学习粒子群算法的动态敏捷软件项目调度

Dynamic agile software project scheduling using dual-index group learning particle swarm optimization

针对敏捷软件开发中的用户故事选择和任务分配2个紧耦合子问题,考虑用户故事的新增和开发者工作时长的不确定性,构建敏捷软件项目的动态周期性调度模型,提出一种基于目标值和潜力值双指标进行分组学习的粒子群优化算法。该算法依据不同分组特征选用相异的学习对象,以提高搜索的多样性;基于投资回报率和时间利用率设计初始化和局部搜索策略,以应对环境变化并增强挖掘能力。与7种已有算法相比,所提算法能够规划出一套产出价值更大和时间利用率更高的调度方案。

To address the two tightly coupled sub-problems of user story selection and task allocation in agile software development,while considering the uncertainties of new user stories and developers'working hours,a dynamic periodic scheduling model for agile software projects is constructed.A particle swarm optimization algorithm based on grouped learning using both target values and potential values as indicators is proposed.By selecting different learning objects based on the characteristics of different groups,the diversity of search is enhanced.Initialization and local search strategies are designed based on return on investment and time utilization,allowing the algorithm to adapt to environmental changes and improve its exploration capabilities.Compared with seven existing algorithms,the proposed algorithm can devise a scheduling plan with greater output value and higher time utilization.