Fuzzy Multi-objective Trade-off Problem By Using Immune Genetic Particle Swarm Optimization Algorithm For Construction Projects

Time-Cost-Quality Trade-off Problem in construction projects has always been an important study in the field of construction management. Many scholars have an in-depth study of mutual relationships on the project duration, cost and quality, putting forward two dimensional or three dimensional optimization model under a certain condition. However, up to date, there are few literatures about the analysis and discussion of Fuzzy Time-Cost-Quality Trade-off Problem in construction projects. In addition, traditional multi-objective solutions cannot meet the need of multi-objective optimization problem in higher dimensions or higher complexity, and the solutions are vulnerable to local optimization, thus it is difficult to find global optimal solution or there is some distance between the true Pareto front and the known Pareto front.This article establishes a Time-Cost-Quality Trade-off model through the analysis on the relationship of time, cost and quality. Taking into account the uncertain environmental factors and the resource constraints in the construction, we improve the trade-off model, so it can meet the realistic more appropriately.In order to solve the proposed fuzzy multi-objective trade-off model, this article, based on standard Particle Swarm Optimization algorithm, analyzes the feature and mechanism of part operators in Genetic Algorithm and Immune Algorithm. The operators are introduced into PSO algorithm, and we can obtain an improved PSO-Immune Genetic PSO algorithm. We adopt the test of function optimization and multi-objective optimization to implement convergence performance verification. It is proved to be an excellent new algorithm in high-dimensional, non-convex and discrete, constrained to find global optimal and Pareto optimal front.We apply the Fuzzy Time-Cost-Quality Trade-off model on a practical project case and solve the case using IGPSO. Several fuzzy Pareto solutions which meet the constrains are obtained. The results verify the validity of time-cost-quality trade-off problem and the efficiency of application of IGPSO in solving fuzzy multi-objective optimization model.