基于改进微粒群算法的建设项目多目标动态优化  

Multi-objective Dynamic Optimization Using an Improved Particle Swarm Optimization Algorithm for Construction Projects

作　　者：李万庆[1] 刘玉丁 孟文清[2] 岳丽飞 

机构地区：[1]河北工程大学经济管理学院,河北邯郸056038 [2]河北工程大学土木工程学院,河北邯郸056038

出　　处：《数学的实践与认识》2016年第1期93-101,共9页Mathematics in Practice and Theory

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.

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 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]