摘要
针对建设项目的复杂性和动态性,建立基于改进微粒群算法的多目标动态优化模型.首先,为提高算法性能,引入外部归档集和阈值并构建基于理想点法的适应度函数;其次,分别建立工期模型、加入系统可靠度的质量模型以及加入费用现值的成本模型,由其得到综合优化模型;最后结合工程实例对算法进行验证并与非劣分类遗传算法(NSGA-Ⅱ算法)对比.结果表明:方法比NSGA-Ⅱ算法的优化结果更科学、收敛速度更快.
According to complexity and dynamics of construction projects, the multi-objective dynamic optimization of construction projects is carried out using an improved PSO algorithm. In order to enhance the algorithm performance and avoid subjectivity to set the weight of subjectivity, an external archive set and threshold of operators are introduced, a new concept of fitness function based on the ideal point method is adopted. In addition, this paper sets up a multi-objective optimization model based on the reliability of complex system and present value cost. An Case study is illustrated by an improved PSO algorithm in comparison with NSGA-II algorithm. The results show that the algorithm is more effective and efficient than NSGA-II algorithm.
出处
《数学的实践与认识》
北大核心
2016年第1期93-101,共9页
Mathematics in Practice and Theory
关键词
多目标优化
改进微粒群算法
动态优化
理想点法
系统可靠度
multi-objective optimization
improved PSO algorithm
dynamic optimization
ideal point method
reliability of complex system
