A Study On Multi-objective Optimization Of Construction Project Based On Genetic Immune PSO

Multi-objective optimization of construction project has become a hot point in the area of project management. In the past study focused on the qualitative relationship between duration and direct cost of the project. In recent years some studied join the quality objectives of project into the multi-objective model and established a quantitative optimization mode based on duration-cost-quality. But very few of the study include the control about environmental influence and security management. In addition, the traditional mathematic optimization method has many requirements for objective functions. Its solution scope is small, so not suitable for multi-objective function model as a universal algorithm for optimization.Firstly, this dissertation introduces particle swarm optimization, genetic algorithm and immune algorithm briefly. On this basis and through introducing crossover, mutation, memory choice into particle swarm optimization, hybrid algorithm has been improved - the genetic immune particle swarm algorithm, which greatly improved the algorithm's ability to escape local optimum. Next, this method combines interactive method with genetic immune PSO algorithm and develop suitable method for solving engineering problems, interactive multi-objective optimization genetic immune PSO algorithm, which makes the quantitative decision information and qualitative preference information can enter multi-objective optimization and decision-making systems, not easily quantifiable goals such as security management are taken into account, project management objectives are more comprehensive optimized. Subsequently, the article analyzed the relationship between duration, cost and quality of the project through combination of qualitative and quantitative analysis, quantified the relationship between engineering and environmental influence and established a Schedule-Cost-Quality-Environment multi-objective optimization model. Finally, I established a multi-objective optimization model for a practical project, using interactive genetic immune particle swarm algorithm to solve the model. Results achieved the best balance of the project quality-cost-schedule-environment-safety objective, which verified the practicality and effectiveness of the proposed interactive genetic immune particle swarm optimization in multi-objective optimization problems in projects.