基于分组学习粒子群算法的众包软件项目调度
Crowdsourcing software project scheduling based on group learning particle swarm optimization algorithm
申晓宁 1徐继勇 2姚铖滨 2宋丽妍3
作者信息
- 1. 南京信息工程大学 自动化学院,江苏 南京 210044;江苏省大气环境与装备技术协同创新中心,江苏 南京 210044;江苏省大数据分析技术重点实验室,江苏 南京 210044
- 2. 南京信息工程大学 自动化学院,江苏 南京 210044
- 3. 南方科技大学广东省类脑智能计算重点实验室,广东 深圳 518055
- 折叠
摘要
为解决众包软件项目调度问题中的开发者选择、任务分配和投入度确定3个强耦合子问题,引入开发者信誉度,考虑技能、工作时长、开发团队规模等约束,以项目完成质量和工期为目标建立数学模型.提出一种采用三段式混合编码的分组学习粒子群算法求解所建模型.所提算法根据适应度排序将种群划分为3组,不同分组的粒子数量随进化代数自适应变化,且各组根据不同的适应度采用不同的更新策略.将所提算法与10种具有代表性的算法在12个不同规模的众包软件项目调度算例中进行对比,结果表明,所提算法能够获得精度更高的调度方案.
Abstract
To solve the three coupled subproblems of the crowdsourcing software project scheduling including devel-oper selection,task assignment and determination of the dedications,by introducing the reputation of the developers and considering the constraints such as task skills,working hours and team size,a mathematical model was con-structed aiming to maximize the completion quality and minimize the project duration simultaneously.A group learn-ing particle swarm optimization algorithm was proposed to solve the model,which adopted a three-segment hybrid encoding method and divided the population into three groups according to the fitness ranking.The number of parti-cles in different groups changed adaptively with the evolutionary generation,and each group employed distinct up-date strategies according to the differences of fitness values.The proposed algorithm was compared with 10 repre-sentative algorithms on 12 instances with different scales.Experimental results showed that the proposed algorithm could obtain a scheduling solution with higher precision.
关键词
众包软件项目调度/粒子群优化/分组学习/混合编码/信誉度Key words
crowdsourcing software project scheduling/particle swarm optimization/group learning/hybrid enco-ding/reputation引用本文复制引用
基金项目
广东省重点实验室资助项目(2020B121201001)
国家自然科学基金资助项目(61502239)
国家自然科学基金资助项目(62002148)
江苏省自然科学基金资助项目(BK20150924)
出版年
2024
NETL
NSTL
万方数据