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ISSN 2753-3239
ISSN 2753-3239
CCC: 9
PROCEEDINGS OF THE FIFTEENTH INTERNATIONAL CONFERENCE ON COMPUTATIONAL STRUCTURES TECHNOLOGY
PROCEEDINGS OF THE FIFTEENTH INTERNATIONAL CONFERENCE ON COMPUTATIONAL STRUCTURES TECHNOLOGY
Edited by: P. Iványi, J. Kruis and B.H.V. Topping
Paper 3.3
Comparative Analysis of Multi-Objective and Single-Objective Optimization Approaches in Structural Engineering
B. Miller and L. Ziemiański
Department of Structural Mechanics, Rzeszow University of Technology, Poland
Full Bibliographic Reference for this paper
B. Miller, L. Ziemiański, "Comparative Analysis of Multi-Objective and Single-Objective Optimization Approaches in Structural Engineering", in P. Iványi, J. Kruis, B.H.V. Topping, (Editors), "Proceedings of the Fifteenth International Conference on
Computational Structures Technology", Civil-Comp Press, Edinburgh, UK,
Online volume: CCC 9, Paper 3.3, 2024, doi:10.4203/ccc.9.3.3
Keywords: finite element analysis, multi-objective optimization, scalarization, Genetic algorithm, surrogate model, dynamics.
Abstract
This study investigates the effectiveness of multi-objective optimization versus single-objective optimization in structural engineering design. Through a comparative analysis, employing the same objective functions in both approaches across various scenarios, we assess their performance in balancing conflicting objectives while maintaining solution constraints. Single-objective optimization strategies involve formulating constraints based on one objective or constructing a scalar objective function with a single weight coefficient. Our findings reveal that while both approaches yield similar results, they differ significantly in complexity. Multi-objective optimization poses challenges in balancing competing objectives, while single-objective optimization with scalarization requires careful construction of the scalar objective function and weight parameter selection. However, single-objective optimization simplifies the optimization process when one objective is reduced to constraints. Additionally, the inclusion of auxiliary objective functions aids in solution refinement. Overall, our analysis highlights the potential for employing single-objective optimization as an alternative to multi-objective optimization, facilitating problem definition and enabling the incorporation of auxiliary objectives for enhanced optimization outcomes.
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