Research on Improved Two-Level Multi-objective Optimization Model Based on TOPSIS

TOPSIS, as an effective tool to solve complex problems, is based on minimizing the difference between the positive ideal solution (PIS) and the negative ideal solution. In this study, we propose a new dual multi-objective optimization algorithm with higher precision to achieve higher problem-solving efficiency. In algorithm optimization, the distance function principle can transform the complex two objectives into two conflicts on one level. Then, the membership function of fuzzy set theory and the linear transformation of FGP can effectively eliminate the contradictions between the two objectives, thereby achieving the best results. The improved two-level multi-objective optimization algorithm (MBLMOO) is compared with Abo-Sinna’s fuzzy planning algorithm and A. Baky’s improved TOPSIS algorithm on the test functions studied by Abo-Sinna. The results show that the proposed MBLMOO algorithm outperforms fuzzy methods and the TOPSIS algorithm regarding optimization effectiveness.