摘要
基金项目管理中,专家分配问题的研究具有很现实的意义。在解决专家分配问题上做过一些基础性的工作,提出了使用遗传算法及一种信息素指导变异的新算法求解该问题。实验证明,遗传算法是一种可行的途径,并且信息素指导下的启发式变异操作,可以加速算法向最优解搜索。但是,这两种方法都存在局部搜索能力差的问题,在算法运行的中后期会出现大量的冗余迭代。鉴于此,提出一种信息素指导下的自适应变异方法求解专家分配问题。实验证明,新算法具有更强的收敛能力和局部搜索能力。
Expert assignment is chief and basic work of project review in project management. So it is significant to research how to solve expert assignment problem （EAP）. In previous papers, we established the mathematical model of expert assignment problem, and proposed genetic algorithm and GA using heuristic mutation guide by pheromone to solve EAP. Though it has been proven they are effective ways for EAP, they have disadvantages of massive redundancy iteration in later period and inferior local search ability. In this paper a modification of GA which introduces adaptive mutation is proposed to solve EAP. The simulation results show that the new algorithm improves the ability of local search and generates solutions of better quality.
出处
《计算机应用》
CSCD
北大核心
2007年第9期2276-2278,2293,共4页
journal of Computer Applications
关键词
专家分配
遗传算法
蚂蚁算法
自适应变异
信息素
expert assignment
genetic algorithm
ant algorithm
adaptive mutation
pheromone
