Multi-objective Dynamic Optimization Using An Improved Particle Swarm Optimization Algorithm For Construction Projects

Multi-objective optimization of construction project has become a hot point in the area of project management.Multi-objective optimization problem penetrates the various stages of construction management,which can effectively solve the specific problems in the construction process and has important practical value.There are a lot of classic algorithms which have been used in multi-objective optimization field dealing with complicated problems. However, the application of the current optimization methods requires so many resources that the practical situation can not be satisfactorily met.The particle swarm optimization(PSO) is an evolutionary computational method, which may be conveniently employed to execute random and global search.Analyzing the basic principle and the topology structure of swarm optimization,the author has found the key point to improve the 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.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 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.