journal article·2025-03-01Open access

Pareto equilibrium solution algorithm based on the metrics planning method and its application to multi-objective aerospace optimization

Ziqi Fang, Zhili Tang

AIP Advances· Volume 15· Issue 3

Abstract

In high-dimensional multi-objective optimization problems, there are many Pareto solutions obtained by using high-dimensional multi-objective optimization algorithms, but there is only one meaningful optimal solution for the engineering problem; to find the unique solution, this paper develops a high-dimensional Pareto equilibrium solution algorithm based on the metrics planning method. First, the optimization problem of high-dimensional objectives is integrated into a single-objective problem based on the reference point according to the metrics planning method. Then, the weighting values of each objective function in the integrated single-objective problem are calculated using the weight allocation method. Finally, optimization experiments on the test functions show that the approach can effectively find the ideal optimal solution located in the set of Pareto solutions of the high-dimensional multi-objective optimization problem. This paper further applies the algorithm to the multi-objective optimization design of a liquid rocket engine injector and obtains the ideal Pareto optimal solution, which indicates that the high-dimensional Pareto equilibrium solution algorithm based on metrics planning in this paper is an effective method to help engineers find the meaningful solution from the numerous solutions, and it is promising in multi-objective aerospace optimization problems.
Source: crossref · Quality: baseline

Research context

OpenAlex topics: Advanced Multi-Objective Optimization Algorithms · Advanced Aircraft Design and Technologies · Metaheuristic Optimization Algorithms Research

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

College of Aerospace Engineering, Nanjing University of Aeronautics and Astronautics , Nanjing 210016,

References (27)

Show source-provided references
1. 1976
2. An adaptive genetic algorithm based on population diversity strategy
3. A review of multi-objective optimisation and decision making using evolutionary algorithms10.1504/ijbic.2019.101640 ↗
4. A Gaussian error correction multi-objective positioning model with NSGA-II10.1002/cpe.5464 ↗
5. A many-objective optimization based intelligent intrusion detection algorithm for enhancing security of vehicular networks in 6G10.1109/tvt.2021.3057074 ↗
6. A hybrid indicator many-objective optimization algorithm for the selection and delivery of disaster relief materials problem10.1002/cpe.5948 ↗
7. Hybrid many-objective particle swarm optimization algorithm for green coal production problem10.1016/j.ins.2020.01.018 ↗
8. Production scheduling optimization on high dimensional multi-objective flexible stamping workshop based on improved NSGA-II algorithm10.13330/j.issn.1000-3940.2023.1 ↗
9. Research on high-dimensional multi-objective optimization scheduling model of centralized charging station10.14107/j.cnki.kzgc.20210875 ↗
10. High-dimensional multi-objective optimization of aero-engine based on POD-PCE-Kriging model10.13224/j.cnki.jasp.20220740 ↗
11. Multi-objective optimization of a muzzle brake to enhance overall performance10.1063/5.0145731 ↗
12. Multi-objective optimization of parameters for magnetically coupled resonant wireless energy transmission systems10.1063/5.0131124 ↗
13. Evolutionary many-objective optimization: A survey10.14107/j.cnki.kzgc.20230186 ↗
14. E. Zitzler , M.Laumanns, and L.Thiele, “SPEA2: Improving the strength Pareto evolutionary algorithm,” TIK Report, 2001, Vol. 103.
15. PESA-II: Region-based selection in evolutionary multiobjective optimization
16. A fast and elitist multiobjective genetic algorithm: NSGA-II10.1109/4235.996017 ↗
17. Privacy protection based on many-objective optimization algorithm10.1002/cpe.5342 ↗
18. A set-based genetic algorithm for interval many-objective optimization problems10.1109/tevc.2016.2634625 ↗
19. MOEA/D: A multiobjective evolutionary algorithm based on decomposition10.1109/tevc.2007.892759 ↗
20. Solving multiobjective optimization problems using an artificial immune system10.1007/s10710-005-6164-x ↗
21. Multiobjective evolutionary algorithms: A comparative case study and the strength Pareto approach10.1109/4235.797969 ↗
22. M. P. Hansen and A.Jaszkiewicz, “Evaluating the quality of approximations to the non-dominated set,” IMM Technical Report No. IMM-REP-1998-7, 1998.
23. A review of weighting methods in multi-indicator comprehensive evaluation
24. 2004
25. Variable interaction in multi-objective optimization problems
26. CFD-based design optimization for single element rocket injector
27. Response surface approximation of Pareto optimal front in multi-objective optimization10.1016/j.cma.2006.07.010 ↗