Research On TCQ Optimization Of Engineering Project Based On Particle Swarm Optimization

In recent years,with the increase in the proportion of the total output value of the construction industry and construction and installation engineering costs in the total fixed asset investment,engineering projects have played an important role in the national economy.However,the rapid development of the construction industry and the characteristics of largescale,one-time and large investment in engineering projects have made it more difficult for project managers to manage engineering projects.And the most important and key part is the control of the three goals of project duration(T),cost(C)and quality(Q).The comprehensive optimization of the three has become the key to maximizing the benefits of engineering projects.Therefore,the establishment of the three-objective model and the method of multi-objective optimization have become the focus of academic research.But most of the literature only studies the linearity or non-linearity of the model,such as the linear function,quadratic function,s-type function of duration-cost,duration-quality,etc.Rarely explore the quantitative scope of the pairwise objective function.For example,when the construction period-quality model is established,only the quality level of the project itself is analyzed,and the adverse effects on the outside world during the construction process are not considered.In addition,although the method of studying multi-objective optimization problems has shifted from traditional methods to intelligent algorithms,but only for multi-objective problems of construction projects,there is limited handling of the problem that the algorithm is easy to fall into local optimality,which makes the obtained Pareto The accuracy of the optimal solution set is low.Finally,the processing of Pareto optimal solution decision is very few.This article analyzes the following aspects in view of the deficiencies of previous research.First of all,when establishing the construction period-quality model,not only the quality level of the project itself is analyzed,but also the external quality evaluation after the pollution caused to the environment during the construction process is considered,and the final project quality is obtained by the weighted sum of the two.Secondly,on the basis of the traditional particle swarm algorithm,three improvement strategies are used to establish an external archive set,maintain an external archive set,and an average optimal position,and verify it through a test function.The results show that the performance of the improved algorithm is better.Excellent,which is conducive to finding the Pareto optimal solution with higher accuracy.Finally,a combination weight-grey relational analysis method is proposed to sort multiple groups of Pareto optimal solutions.This paper combs the required parameters in the model based on the project example,establishes a construction period-cost-quality optimization model that meets the project,and uses the improved particle swarm algorithm to solve the model to obtain the Pareto optimal solution,that is,multiple sets of balanced construction period,cost and The combination of quality.The combination weight-grey correlation analysis method is used to sort them,and finally a unique combination of construction period,cost and quality is obtained,which realizes the multi-objective comprehensive optimization of the engineering project.The result was a 3.0% reduction in the expected construction period,a 4.9% reduction in costs,and a quality level of 0.79,which far exceeded the contract requirements.This article focuses on providing target requirements for construction companies to prepare construction plans.Not only can it give a balanced target combination,but it can also provide a better target plan for construction units or project types with different target focus,and provide strong support for project managers to set goals.