Size and Shape Optimization of a Guyed Mast Structure under Wind, Ice and Seismic Loading

Cucuzza, Raffaele; Rosso, Marco Martino; Aloisio, Angelo; Melchiorre, Jonathan; Giudice, Mario Lo; Marano, Giuseppe Carlo

doi:10.3390/app12104875

Cited by 22 publications

(8 citation statements)

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“…The solution of real-world, large-scale optimization problems within the field of structural engineering can only be achieved with a synergy of the following aspects (Melchiorre et al 2021;Lagaros et al 2022;Cucuzza et al 2022): (i) efficient numerical modelling of the physical problem; (ii) reliable numerical optimization algorithms for enhanced structural design; (iii) rationalized modelling of the system uncertainties (in the case of probabilistic design problems); and (iv) exploitation of modern High-Performance Computing (HPC) facilities. The original OCP (Lagaros 2014) and its evolution HP-OCP offer all aforementioned capabilities, including efficient numerical modelling tools and several MOAs with a great variety of embedded features and CHTs, while its implementation enables its straightforward upgrade to incorporate new tools and features.…”

Section: Optimization Computing Platform and Pity Beetle Algorithmmentioning

confidence: 99%

Constraint handling techniques for metaheuristics: a state-of-the-art review and new variants

et al. 2023

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Metaheuristic optimization algorithms (MOAs) are computational randomized search processes which draw inspiration from physical and biological phenomena, with an application spectrum that extends to numerous fields, ranging from engineering design to economics. MOAs were originally developed for solving unconstrained NP-complete problems, and hence their application to constrained optimization problems (COPs) requires the implementation of specialized techniques that facilitate the treatment of performance and bound constraints. While considerable research efforts have been oriented towards the development and subsequent enhancement of novel constraint handling techniques (CHTs) for MOAs, a systematic review of such techniques has not been conducted hitherto. This work presents a state-of-the-art review on CHTs used with MOAs and proposes eight novel variants based on the feasibility rules and ε-constrained techniques. The distinctive feature of the new variants is that they consider the level and number of constraint violations, besides the objective function value, for selection of individuals within a population. The novel variant performance is evaluated and compared with that of four well-known CHTs from the literature using the metaheuristic pity beetle algorithm, based upon 20 single-objective benchmark COPs. The computational results highlight the accuracy, effectiveness, and versatility of the novel variants, as well as their performance superiority in comparison with existing techniques, stemming from their distinctive formulation. The complete code can be downloaded from GitHub (https://github.com/nikoslagaros/MOAs-and-CHTs).

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Section: Optimization Computing Platform and Pity Beetle Algorithmmentioning

confidence: 99%

Constraint handling techniques for metaheuristics: a state-of-the-art review and new variants

et al. 2023

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“…Architecture and structure are two essential and interconnected elements that have undergone continuous evolution in response to societal changes. This dynamic relationship has played a crucial role in shaping various structural typologies [1,2]. In particular, this paradigm finds significant relevance in understanding the unique characteristics of long-span spatial structures, with particular reference to shell and gridshell structures [3,4].…”

Section: Introductionmentioning

confidence: 99%

Investigating the Influence of the Improved Multibody Rope Approach on the Structural Behavior of Dakar Mosque Gridshell Structure

Melchiorre,

Invernizzi,

Manuello Bertetto

2024

Buildings

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Gridshell structures are characterized by an impressive strength-to-weight ratio, allowing their application in large-span roofing structures. However, their complex construction process and maintenance limited their widespread application. In recent years, the development of parametric and computational design tools has rekindled interest in this type of structure. Among these techniques, the Multibody Rope Approach (MRA) is a form-finding method based on the dynamic equilibrium of a system of masses (nodes) connected by ropes, which allows optimizing the structural shape starting from the dual geometry of the funicular network. To optimize the construction process, an improved version of the MRA, i-MRA, has been recently developed by the authors with the goal of uniforming the size of the structural components. To investigate the impact of the i-MRA method on the structural behavior of gridshell structures, the practical case of the design of a mosque roof is here analyzed. The comparison is carried out in terms of structural performance with respect to permanent and equivalent quasi-static loads. In addition, free-vibration natural-frequency shift is obtained by performing linear modal analysis. Finally, the global behavior with respect to buckling and elastic instability is assessed solving the relevant eigenvalue problem. The results demonstrate that for the roofing of the Dakar mosque, the structural configuration obtained through i-MRA is superior in terms of both construction efficiency and structural performance. The achieved shape exhibits a more uniform distribution of stresses induced by the applied loads together with very limited structural element typologies.

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“…Many optimization problems in both academia and industry, such as structure optimization [1][2][3], production scheduling [4], and battery model optimization [5], involve constraints. These constrained optimization problems (COPs) can be formulated as follows: minimize f (x), x = (x 1 , x 2 , .…”

Section: Introductionmentioning

confidence: 99%

A two-stage adaptive penalty method based on co-evolution for constrained evolutionary optimization

Wang

Guo

Huang

et al. 2023

Complex Intell. Syst.

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Penalty function method is popular for constrained evolutionary optimization. However, it is non-trivial to set a proper penalty factor for a constrained optimization problem. This paper takes advantage of co-evolution to adjust the penalty factor and proposes a two-stage adaptive penalty method. In the co-evolution stage, the population is divided into multiple subpopulations, each of which is associated with a penalty factor. Through the co-evolution of these subpopulations, the performance of penalty factors can be evaluated. Since different penalty factors are used, the subpopulations will evolve along different directions. Thus, exploration can be enhanced. In the shuffle stage, all subpopulations are merged into a population and the best penalty factor from the co-evolution stage is used to guide the evolution. In this manner, the information interaction among subpopulations can be facilitated; thus, exploitation can be promoted. By executing these two stages iteratively, the feasible optimum could be obtained finally. In the two-stage evolutionary process, the search algorithm is designed based on two trial vector generation strategies of differential evolution. Additionally, a restart mechanism is designed to help the population avoid stagnating in the infeasible region. Extensive experiments demonstrate the effectiveness of the proposed method.

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Size and Shape Optimization of a Guyed Mast Structure under Wind, Ice and Seismic Loading

Cited by 22 publications

References 50 publications

Constraint handling techniques for metaheuristics: a state-of-the-art review and new variants

Constraint handling techniques for metaheuristics: a state-of-the-art review and new variants

Investigating the Influence of the Improved Multibody Rope Approach on the Structural Behavior of Dakar Mosque Gridshell Structure

A two-stage adaptive penalty method based on co-evolution for constrained evolutionary optimization

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