Chaos-based firefly algorithms for optimization of cyclically large-size braced steel domes with multiple frequency constraints

Kaveh, A.; Javadi, Seyed Mohammad

doi:10.1016/j.compstruc.2019.01.006

Cited by 65 publications

(26 citation statements)

References 34 publications

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“…Therefore, the effect of the outrigger position on seismic demand is determined to find the optimal position. Different optimization methods can be used in solving engineering problems [57][58][59][60][61][62][63].…”

Section: Dynamic Analysismentioning

confidence: 99%

Optimal Location of Energy Dissipation Outrigger in High-rise Building Considering Nonlinear Soil-structure Interaction Effects

Tavakoli¹,

Kamgar

Rahgozar

2020

Period. Polytech. Civil Eng.

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Buckling-restrained braces (BRBs) emerged to improve the seismic performance of high-rise structures as compared to the ordinary diagonal bracing. In this paper, the seismic performance of braced buildings with the BRB outrigger system is investigated to determine the optimal configuration of BRB outrigger, considering the nonlinear SSI effect. For this purpose, the nonlinear dynamic analysis is carried out on four braced buildings with a BRB outrigger system placed on three different soil types. The outrigger configuration changes from first to the top story to capture the seismic performance of different locations of BRB outrigger. It is observed that the outrigger location affects the seismic performance, which is measured in terms of inter-story drift ratio, story displacement, story shear, and energy dissipation capacity. The results are compared to the fixed base condition buildings, which proves considering SSI, shifts the optimal location to the upper story of the structure. Moreover, the effect of soil’s stiffness on the seismic responses of structures and the optimal BRB outrigger location is investigated. Finally, the merits of BRB outrigger are shown by comparing its seismic performance that of the conventional outrigger, under frequent, basic, and rare earthquakes. The results show that the optimal locations of different 2-D buildings rested on the dense soil, medium soil, and soft clay are obtained at 0.6, 0.65, and 0.7 of the building’s height (H), respectively. Also, the results show that the optimum location of the BRB outrigger system based on the energy dissipation criteria is 0.45H to 0.65H.

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Section: Dynamic Analysismentioning

confidence: 99%

Optimal Location of Energy Dissipation Outrigger in High-rise Building Considering Nonlinear Soil-structure Interaction Effects

Tavakoli¹,

Kamgar

Rahgozar

2020

Period. Polytech. Civil Eng.

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“…Since its proposal, the Fa approach has shown very good performance in a high number of optimization problems and applications [57], [60], including successful results in structural optimization problems [61]- [64]. Due to this previous good performance in structural optimization, we evaluate here the performance of Fa as part of an ensemble multimethod algorithm, the CRO-SL.…”

Section: The Brightness Of a Firefly Is Determined By Their Ob-mentioning

confidence: 99%

Submerged Arches Optimal Design With a Multi-Method Ensemble Meta-Heuristic Approach

et al. 2020

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This paper discusses the performance of a multi-method ensemble meta-heuristic for optimization in a problem of submerged arches design. Specifically, the Coral Reefs Optimization algorithm with Substrate Layers (CRO-SL) is proposed. It is a multi-method evolutionary ensemble which combines different searching structures in a single population. In this case, some novel searching strategies such as the Firefly algorithm and the Water Wave Optimization have been proposed to be included in the CRO-SL algorithm. In addition, some other traditional searching structures such as classical crossovers, Gaussian mutation operators, Harmony search and Differential Evolution search strategies have also been implemented in the algorithm. The proposed CRO-SL has been applied to the optimal design of submerged arches in deep waters, considering several design aspects such as the bending moment of the beam and the airspace of the arch. The evaluation of the submerged arches quality is obtained after the simulation of the arches by means of a finite elements computational software, which has been hybridized with the CRO-SL algorithm in the process of fitness calculation. It will be shown that the CRO-SL is able to obtain excellent solutions to submerged arches design problems. For this, the computational performance of each substrate (different searching strategies) in this optimization problem will be discussed and the results obtained will be compared with those in alternative works for similar experiments.

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“…Kaveh and Javadi [48] proposed to combine FA with a logistic map and Gaussian map respectively to improve the performance of the basic FA. An enhanced version of the FA with gender difference was presented by Wang and Song [49].…”

Section: Introductionmentioning

confidence: 99%

Predicting Green Consumption Behaviors of Students Using Efficient Firefly Grey Wolf-Assisted K-Nearest Neighbor Classifiers

Tang

Lin

et al. 2020

IEEE Access

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Understanding the green consumption behaviors of college students is highly demanded to update the public and educational policies of universities. For this purpose, this research is devoted to advance an efficient model for identifying prominent features and predicting the green consumption behaviors of college students. The proposed prediction model is based on the K-Nearest Neighbor (KNN) with an effective swarm intelligence method, which is called OBLFA_GWO. The optimization core takes advantage of the firefly algorithm (FA) and opposition-based learning (OBL) to mitigate the immature convergence of the grey wolf algorithm (GWO). In the proposed prediction framework, OBLFA_GWO is utilized to identify influential features. Then, the enhanced KNN model is used to identify the importance and interrelationships of features in samples and construct an effective and stable predictive model for decision support. Five other wellknown algorithms are employed to validate the effectiveness of the proposed OBLFA_GWO strategy using 13 benchmark test problems. Also, the non-parametric statistical Wilcoxon sign rank and Friedman tests are conducted to validate the significance of the proposed OBLFA_GWO against other peers. Experimental results indicate that the FA and OBL can significantly boost the core exploratory and exploitative trends of GWO in dealing with the optimization tasks. Also, the OBLFA_GWO-based KNN (OBLFA_GWO-KNN) model is compared with four classical classifiers, such as kernel extreme learning machine (KELM), backpropagation neural network method (BPNN), and random forest (RF) and five advanced feature selection methods in terms of four standard evaluation indexes. The experimental results show that the classification accuracy of the proposed OBLFA_GWO-KNN can reach to 96.334 % on the real-life dataset collected from nine universities. Also, the proposed binary OBLFA_GWO algorithm has improved the classification performance of KNN compared to the other peers. Hopefully, the established adaptive OBLFA_GWO-KNN model can be considered as a useful tool for predicting students' behavior of green consumption. INDEX TERMS K-nearest neighbor, firefly algorithm, grey wolf algorithm, opposition-based learning, green consumption behavior, feature selection. The associate editor coordinating the review of this manuscript and approving it for publication was Josue Antonio Nescolarde Selva .

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Chaos-based firefly algorithms for optimization of cyclically large-size braced steel domes with multiple frequency constraints

Cited by 65 publications

References 34 publications

Optimal Location of Energy Dissipation Outrigger in High-rise Building Considering Nonlinear Soil-structure Interaction Effects

Optimal Location of Energy Dissipation Outrigger in High-rise Building Considering Nonlinear Soil-structure Interaction Effects

Submerged Arches Optimal Design With a Multi-Method Ensemble Meta-Heuristic Approach

Predicting Green Consumption Behaviors of Students Using Efficient Firefly Grey Wolf-Assisted K-Nearest Neighbor Classifiers

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