Multi Objective Optimization System for Bridge Design Based on Multi-objective Optimization Theory and Improved Ant Colony Algorithm

Abstract

In the field of bridge design, multi-objective optimization problems have attracted much attention due to their complexity and multiple solutions. The limitations of existing optimization algorithms in dealing with multi-objective problems, especially the trade-off between multiple objectives such as cost, duration, safety and quality. Therefore, in order to achieve the balance and optimization of each optimization objective while satisfying the bridge design constraints, a multi-objective optimization system based on an improved ant colony algorithm is studied and developed. The study is conducted by modeling natural selection and genetic mechanisms to improve the global search capability and diversity of the algorithm. The results showed that the proposed system was significantly superior to the traditional methods in key performance indicators such as optimization speed, objective function value, and robustness. The accuracy, stability, and safety of the proposed system were as high as 92%, 95%, and 91%, respectively, while the corresponding indicators of the traditional system were only about 55%. Specifically, the optimization speed of the proposed system reached 0.95, which was significantly better than that of the traditional system of 0.70, indicating that the proposed system had a significant advantage in convergence speed. The objective function value of the proposed system was 0.92, which was better than 0.75 of the traditional system, indicating that the proposed system could achieve a more optimal solution when solving optimization problems. The proposed system is superior to the traditional system in all evaluation indices, which proves its superior performance in multi-objective optimization of bridge design. The study provides a new optimization strategy for bridge design, which helps to achieve a more efficient, economical and safer bridge design solution.