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Advancing truss structure optimization-A multi-objective weighted average algorithm with enhanced convergence and diversity
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DOI：10.1016/j.rineng.2025.104241.png)
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Abstract
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The challenge of achieving equilibrium between exploration and exploitation stands as a critical barrier in multiobjective metaheuristic optimization when applied to complex engineering problems such as truss structure design. The Multi-Objective Weighted Average Algorithm (MOWAA) presents a new methodology which employs adaptive weighted average position control to optimize population movement for enhanced solution quality. The performance evaluation of MOWAA relies on benchmarking it against five state-of-the-art multiobjective optimization algorithms NSGA-II, MOEA/D, MOLCA, MOEDO and MORIME through eight truss structure optimization problems of increasing complexity. The evaluation of performance relies on three key metrics: Hypervolume (HV), Inverted Generational Distance (IGD) and Spacing (SP). MOWAA demonstrates superior performance compared to competing algorithms through its ability to generate Pareto fronts with higher HV values and lower IGD values and more uniform distribution. The enhanced performance of MOWAA demonstrates its superior capability to efficiently explore the objective space for finding optimal weight-minimization and compliance trade-offs. The robustness of MOWAA is proven through statistical validation with the Friedman rank test which establishes MOWAA as the leading approach with statistically significant advantages. MOWAA demonstrates runtime efficiency throughout truss optimization tasks of varying sizes which enables its practical application for real-world structural optimization problems. MOWAA emerges as a sophisticated and efficient optimization method which demonstrates strong capabilities for engineering applications and computational design.
Keywords:
Meta-heuristic optimization
Weighted average position
Truss structures
Performance metrics
Multi-objective
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