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An Effective Couple Method for Reliability-Based Multi-Objective Optimization of Truss Structures with Static and Dynamic Constraints

T. Vo-Duy, D. Duong-Gia, V. Ho-Huu, T. Nguyen-Thoi*

*Corresponding author for this work

    Research output: Contribution to journalArticleScientificpeer-review

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    Abstract

    This paper proposes an effective couple method for solving reliability-based multi-objective optimization problems of truss structures with static and dynamic constraints. The proposed coupling method integrates a single-loop deterministic method (SLDM) into the nondominated sorting genetic algorithm II (NSGA-II) algorithm to give the so-called SLDM-NSGA-II. Thanks to the advantage of SLDM, the probabilistic constraints are treated as approximating deterministic constraints. And therefore the reliability-based multi-objective optimization problems can be transformed into the deterministic multi-objective optimization problems of which the computational cost is reduced significantly. In these reliability-based multi-objective optimization problems, the conflicting objective functions are to minimize the weight and the displacements of the truss. The design variables are cross-section areas of the bars and contraints include static and dynamic constraints. For reliability analysis, the effect of uncertainty of parameters such as force, added mass in the nodes, material properties and cross-section areas of the bars are taken into account. The effectiveness and reliability of the proposed method are demonstrated through three benchmark-type truss structures including a 10-bar planar truss, a 72-bar spatial truss and a 200-bar planar truss. Moreover, the influence of parameters on the reliability-based Pareto optimal fronts is also carried out.

    Original languageEnglish
    Article number1950016
    Number of pages33
    JournalInternational Journal of Computational Methods
    Volume17
    Issue number6
    DOIs
    Publication statusPublished - 2019

    Keywords

    • Multi-objective optimization
    • non-dominated sorting genetic algorithm II (NSGA-II)
    • reliability-based multi-objective optimization
    • single-loop algorithm
    • static and dynamic constraints
    • uncertainty

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