摘要
工期-成本优化是施工项目计划的一个重要方面.它从实质上属于一类多目标优化问题.结合近年来提出的一种新的进化算法-蚁群算法(ACO),尝试对工期成本问题(TCTP)进行求解.通过与改进自适应权重方法(MAWA)的结合,ACO算法不仅可以找到最优解,还可以得到问题的帕雷托前沿.通过一个算例验证了算法的有效性,并和枚举法和遗传算法的计算结果进行了比较.结果表明蚁群算法对于工期成本优化问题的求解是十分适用的.
The time-cost trade-off is one of the most crucial aspects of construction project planning, which in fact is a multi-objective optimization problem. A new evolutionary algorithm-ant colony optimization （ACO） algorithm was employed to solve the time-cost trade-off problem （TCTP）. Combining with the modified adaptive weight approach （MAWA）, the ACO algorithm can find out the optimal solution, and define the Pareto front as well. To verify the efficiency and performance of the proposed method, a test example was conducted and the results were compared with that of the exhaustive enumeration and the genetic algorithm. The results indicate that the ACO algorithm is much suitable for solving time-cost trade-off problem.
出处
《系统工程理论与实践》
EI
CSCD
北大核心
2007年第3期105-111,共7页
Systems Engineering-Theory & Practice
关键词
工期成本优化
改进自适应权重
蚁群算法
多目标优化
time-cost trade-off
modified adaptive weight
ant colony algorithm
multi-objective optimization
