Optimum design of large steel skeletal structures using chaotic firefly optimization algorithm based on the Gaussian map

Kaveh, A.; Moghanni, R. Mahdipour; Javadi, Seyed Mohammad

doi:10.1007/s00158-019-02263-1

Cited by 26 publications

(6 citation statements)

References 35 publications

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“…Azad et al [4] solved the problem of simultaneous optimization of dimensions and geometry of dynamically excited steel truss structures. Using the well-known Big Bang-Big Crunch algorithm, the minimum weight design of steel truss is carried out under periodic and a periodic excitation; Grzywinski et al [5] proposed a novel and effective Jaya optimization algorithm for optimization the best quality of the supporting dome structure with natural frequency constraints; Habibi et al [6] considered the geometric nonlinearity problem using the total Lagrangian formula, and obtain a nonlinear solution by introducing and minimizing the objective function constrained by the displacement type; Artar et al [7] studied the optimal design of steel space truss towers under seismic load by using the Jaya optimization algorithm; Kaveh et al [8] proposed a new Gaussian diagram-based Chaotic Firefly Algorithm (CGFA) for structural optimization problems. Wong et al [9] investigated a new meta-heuristic algorithm called symbiotic organism search (SOS) for component size optimization of relatively large steel trusses; Ha et al [10] developed an effective method to optimize nonlinear steel frames under several load combinations, considering the panel area for the first time in the optimization design.…”

Section: Introductionmentioning

confidence: 99%

An Improved JSO and Its Application in Spreader Optimization of Large Span Corridor Bridge

Fu,

Wu,

Wang

et al. 2024

CMES

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In this paper, given the shortcomings of jellyfish search algorithm with low search ability in the early stage and easy to fall into local optimal solution, this paper introduces adaptive weight function and elite strategy, improving the global search scope in the early stage and the ability to refine the local development in the later stage. In the numerical study, the benchmark problem of dimensional optimization with a 10-bar truss structure and simultaneous dimensional shape optimization with a 15-bar truss structure is adopted, and the corresponding penalty method is used for constraint treatment. The test results show that the improved jellyfish search algorithm can provide better truss sections as well as weights. Because when the steel main truss of the large-span covered bridge is lifted, the site is limited and the large lifting equipment cannot enter the site, and the original structure does not meet the problem of stress concentration and large deformation of the bolt group, so the spreader is used to lift, and the improved jellyfish search algorithm is introduced into the design optimization of the spreader. The results show that the improved jellyfish algorithm can efficiently and accurately find out the optimal shape and weight of the spreader, and through Midas Civil simulation, the spreader used can meet the requirements of weight and safety.

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Section: Introductionmentioning

confidence: 99%

An Improved JSO and Its Application in Spreader Optimization of Large Span Corridor Bridge

Fu,

Wu,

Wang

et al. 2024

CMES

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“…In recent years, this issue has attracted the attention of many researchers. In some studies, the focus is on the optimization of frame structures (Talatahari et al, 2012b;Kaveh et al, 2019;Talaslioglu, 2019;Es-haghi et al, 2020), and in others, the focus is on space frame (3D truss) structures (Li et al, 2009;Eskandar et al, 2011;Talatahari et al, 2012a;Kaveh & Hosseini, 2014;Panagant et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Enhanced Teacher-Learning Based Algorithm in Real Size Structural Optimization

Es-haghi

Salehi

Strauß

2022

Journal of Civil Engineering and Management

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Space frame structures that are made up of a huge number of members are often used on a large scale, hence their accurate evaluation is important to achieve the optimal design. On the other hand, the use of space Frames and 3D truss structures has become more popular due to its time efficiency. Also, these types of structures can carry loads in longspan buildings and are used in large-scale structures such as halls, hangars, passenger stations, etc. In this study, a novel evolutionary algorithm, named ETLBO, has been proposed for the optimization of space frame design in real-size structures. Despite the existing methods in the literature, the ETLBO method can be used for large-scale space frame structures due to its high speed with sufficient accuracy. At first, four optimization algorithms Particle swarm optimization (PSO), Genetic Algorithm (GA), Differential Evolution (DE), and Teaching–learning-based optimization (TLBO) under structural problems have been evaluated. The results show that the TLBO algorithm performs better in solving problems and has been better in most problems than other algorithms. So, we have tried to improve this algorithm based on a machine learning approach and combination operators. Algorithm improvement is created by adding a crossover operation between the new solution and the best solution in the teacher phase. This change causes a sudden movement and escapes from the local minima for the algorithm. Enhanced algorithm results show that convergence speed and optimal response quality have improved. Finally, using this algorithm, several new practical examples have been optimized.

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“…FA has been shown to perform better than GA or PSO over several numerical benchmarks (Zhou et al, 2019). Due to its simplicity, flexibility, robustness and effectiveness, it has been widely used in many fields to solve optimization problems (Altabeeb et al, 2021;Danandeh Mehr et al, 2019;Garousi-Nejad et al, 2016;Kaveh et al, 2019;Mosavvar and Ghaffari, 2019). Although these works have shown that FA is an effective optimization technique in many optimization problems, its role in exploration will be greatly weakened if the brightest firefly falls into local optimization.…”

Section: Introductionmentioning

confidence: 99%

Flood Control Operation of Reservoir Group Using Yin-Yang Firefly Algorithm

Chen

Wang

Chau

et al. 2021

Water Resour Manage

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Flood control operation (FCO) of a reservoir is a complex optimization problem with a large number of constraints. With the rapid development of optimization techniques in recent years, more and more research efforts have been devoted to optimizing FCO problems. However, for solving large-scale reservoir group optimization problem, this is still a challenging task. In this work, a reservoir group FCO model is established with minimum flood volume stored in each reservoir and minimum peak flow of downstream control point during the dispatch process. At the same time, a flood forecast model for FCO of a reservoir group is developed by coupling Yin-Yang firefly algorithm (YYFA) with ε constrained method. As a case study, the proposed model is applied to a three-reservoir flood control system in Luanhe River Basin consisting of reservoirs, river channels, and downstream control points.Results show that optimal operation of three reservoirs systems can efficiently reduce the occupied storage capacity for flood control and flood peaks at downstream control point of the basin. The proposed method can be extended to FCO of other reservoir groups with similar conditions.

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Optimum design of large steel skeletal structures using chaotic firefly optimization algorithm based on the Gaussian map

Cited by 26 publications

References 35 publications

An Improved JSO and Its Application in Spreader Optimization of Large Span Corridor Bridge

An Improved JSO and Its Application in Spreader Optimization of Large Span Corridor Bridge

Enhanced Teacher-Learning Based Algorithm in Real Size Structural Optimization

Flood Control Operation of Reservoir Group Using Yin-Yang Firefly Algorithm

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