Many-Objective Optimization of a 120-Bar 3D Dome Truss Structure Using Three Metaheuristics

This paper presents a study on the many-objective optimization of a 120-bar 3D dome truss structure using three algorithms: non-dominated sorting genetic algorithm II (NSGA-II), ant lion optimizer (ALO), and dragonfly algorithm (DA). Mass, compliance, first natural frequency, and buckling factor are among the objective functions. Given the limited availability of optimization methods for many-objective truss optimization problems, this study intends to assess the performance of contemporary algorithms to create future approaches that are more efficient. The performance analysis of the studied algorithms used to solve the truss problem with many objectives is the main contribution of this paper. The analysis is conducted on different dimensions of the truss structure, and two metrics are used to assess the algorithms’ performance. The proposed approach utilizes NSGA-II, ALO, and DA as the optimization techniques and benchmarks them against other algorithms. The effectiveness of these algorithms is then compared using the defined metrics. This evaluation helps researchers to choose the best methods for tackling challenges involving many-objective truss optimization by illuminating the advantages and disadvantages of each algorithm. By evaluating the performance of three algorithms on the considered truss structure problem with many objectives, this study contributes to the advancement of structural optimization. The results of the study demonstrate that the NSGA-II excel compared to other two algorithms, ALO and DA, in the many-objective optimization of the dome truss construction with 120 bars. NSGA-II consistently showed superior performance in terms of the defined metrics used to assess the algorithms’ effectiveness. The findings can guide the development of more effective techniques, ultimately leading to improved designs with enhanced structural performance for complex truss problems. This research fills a gap in the current literature by focusing on many-objective optimization of truss structures and provides a foundation for future studies to build upon in this domain.