Combined size and shape optimization of structures with a new meta-heuristic algorithm

Dede, Tayfun; Ayvaz, Yusuf

doi:10.1016/j.asoc.2014.12.007

Cited by 84 publications

(49 citation statements)

References 29 publications

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“…Hence, the most efficient algorithm is the one that consumes the fewest Nfe to solve the problem. For all the selected algorithms, the numbers of function evaluations were calculated by applying the Teaching-learning-based optimization (TLBO), which is a meta-heuristic optimization algorithm based on the natural phenomenon of teaching and learning [20]. TLBO optimization algorithm requires only common controlling parameters like population size and a number of generations for its operation.…”

Section: Performance Evaluation Of Various Ai Algorithmsmentioning

confidence: 99%

An Intelligent Content Prefix Classification Approach for Quality of Service Optimization in Information-Centric Networking

et al. 2018

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This research proposes an intelligent classification framework for quality of service (QoS) performance improvement in information-centric networking (ICN). The proposal works towards keyword classification techniques to obtain the most valuable information via suitable content prefixes in ICN. In this study, we have achieved the intelligent function using Artificial Intelligence (AI) implementation. Particularly, to find the most suitable and promising intelligent approach for maintaining QoS matrices, we have evaluated various AI algorithms, including evolutionary algorithms (EA), swarm intelligence (SI), and machine learning (ML) by using the cost function to assess their classification performances. With the goal of enabling a complete ICN prefix classification solution, we also propose a hybrid implementation to optimize classification performances by integration of relevant AI algorithms. This hybrid mechanism searches for a final minimum structure to prevent the local optima from happening. By simulation, the evaluation results show that the proposal outperforms EA and ML in terms of network resource utilization and response delay for QoS performance optimization.

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Section: Performance Evaluation Of Various Ai Algorithmsmentioning

confidence: 99%

An Intelligent Content Prefix Classification Approach for Quality of Service Optimization in Information-Centric Networking

et al. 2018

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“…[23] tested the TLBO algorithm on constrained benchmark test functions with different characteristics, benchmark mechanical design problems and mechanical design optimization problems taken from the literature. After that, some optimization problems related with the distinct discipline and features were investigated using the standard TLBO algorithm and the enhancement version of its [24][25][26][27][28][29][30]. The numerical results presented in the corresponding researches proved exploration and exploitation capacities of TLBO on different kind of optimization problems in comparison to other metaheuristics algorithms used in these optimization cases.…”

Section: Sizing Optimization Of Skeletal Structures Using Teaching-lementioning

confidence: 99%

“…The teaching factor TF placed in the Teaching Phase seems the only tuning parameter although yet TF was decided with the help of TF = round[1 + rand (0,1) {2-1}] in [23]. However, the value TF was taken as 1 or 2 in the studies conducted using TLBO in contrast to the equation given in [26]. For example, [30], [31], and [24] were adopted it as 2 through the TLBO process while [28] taken as [0,1].…”

Section: Sizing Optimization Of Skeletal Structures Using Teaching-lementioning

confidence: 99%

Sizing optimization of skeletal structures using teaching-learning based optimization

Toğan

Mortazavi

2017

Int. J. Optim. Control, Theor. Appl. (IJOCTA)

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Teaching Learning Based Optimization (TLBO) is one of the non-traditional techniques to simulate natural phenomena into a numerical algorithm. TLBO mimics teaching learning process occurring between a teacher and students in a classroom. A parameter named as teaching factor, TF, seems to be the only tuning parameter in TLBO. Although the value of the teaching factor, TF, is determined by an equation, the value of 1 or 2 has been used by the researchers for TF. This study intends to explore the effect of the variation of teaching factor TF on the performances of TLBO. This effect is demonstrated in solving structural optimization problems including truss and frame structures under the stress and displacement constraints. The results indicate that the variation of TF in the TLBO process does not change the results obtained at the end of the optimization procedure when the computational cost of TLBO is ignored.

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“…This paper has used the number of function evaluations (N fe ) to measure the performance of algorithms and defines the optimal model to be the one that consumes the fewest (N fe ) to solve the problem. For GSAVHO and other algorithms, the numbers of function evaluations were calculated by equation (9) (Dede and Ayvaz, 2015):…”

Section: P(t) + Q(t) + R(t) In Which P(t) Q(t) and R(t)mentioning

confidence: 99%

Hybridisation of genetic algorithm with simulated annealing for vertical-handover in heterogeneous wireless networks

Goudarzi

Hassan

Anisi

2017

IJAHUC

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Abstract:To provide the seamless mobility in heterogeneous wireless networks two significant methods, Simulated annealing (SA) and genetic algorithms (GAs) are hybrid. In this paradigm, vertical handovers (VHs) are necessary for seamless mobility. In this paper, the hybrid algorithm has the ability to find the optimal network to connect with a good quality of service (QoS) in accordance with the user's preferences. The intelligent algorithm was developed to provide solutions near to real time and to avoid slow and considerable computations according to the features of the mobile devices. Moreover, a cost function is used to sustain the chosen QoS during transition between networks, which is measured in terms of the bandwidth, BER, ABR, SNR and monetary cost. Simulation results presented that choosing the SA rules would minimise the cost function and the GA-SA algorithm could reduce the number of unnecessary handovers, and thereby avoid the 'Ping-Pong' effect.

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Combined size and shape optimization of structures with a new meta-heuristic algorithm

Cited by 84 publications

References 29 publications

An Intelligent Content Prefix Classification Approach for Quality of Service Optimization in Information-Centric Networking

An Intelligent Content Prefix Classification Approach for Quality of Service Optimization in Information-Centric Networking

Sizing optimization of skeletal structures using teaching-learning based optimization

Hybridisation of genetic algorithm with simulated annealing for vertical-handover in heterogeneous wireless networks

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