• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.8
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• pp.1196-1204
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• 2022

In this paper, we propose an optimization algorithm that maximizes the network lifetime in wireless ad-hoc networks using the broadcast transmission method. The optimization algorithm proposed in this paper applies tabu search algorithm, a metaheuristic method that improves the local search method using the memory structure. The proposed tabu search algorithm proposes efficient encoding and neighborhood search method to the network lifetime maximization problem. By applying the proposed method to design efficient broadcast routing, we maximize the lifetime of the entire network. The proposed tabu search algorithm was evaluated in terms of the energy consumption of all nodes in the broadcast transmission occurring in the network, the time of the first lost node, and the algorithm execution time. From the performance evaluation results under various conditions, it was confirmed that the proposed tabu search algorithm was superior to the previously proposed metaheuristic algorithm.

• Journal of the Korean Crystal Growth and Crystal Technology
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• v.31 no.6
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• pp.258-263
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• 2021

ZnO varistor is a semiconductor device which can serve to protect the circuit from surge voltage because its non-linear I-V characteristics by controlling the microstructure of grain and grain boundaries. In order to obtain desired electrical properties, it is important to control microstructure evolution during the sintering process. In this research, we defined a dataset composed of process conditions of sintering and relative permittivity of sintered body, and collected experimental dataset with DOE. Meta-models can predict permittivity were developed by learning the collected experimental dataset on various machine learning algorithms. By utilizing the meta-model, we can derive optimized sintering conditions that could show the maximum permittivity from the numerical-based HMA (Hybrid Metaheuristic Algorithm) optimization algorithm. It is possible to search the optimal process conditions with minimum number of experiments if meta-model-based optimization is applied to ceramic processing.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.1
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• pp.95-101
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• 2022

The two-sided assembly line balancing problem is a kind of NP-hard problem. This problem primarily can be solved metaheuristic method. This paper suggests parallel clustering algorithm that each left and right-sided workstation assigned by operations with T_i = c^* ± α < c, c^* = ${\lceil}$W/m^*${\rceil}$ such that M^* = ${\lceil}$W/c${\rceil}$ for precedence diagram of two-sided assembly line with total complete time W and cycle time c. This clustering performs forward direction from left to right or reverse direction from right to left. For the 4 experimental data with 17 cycle times, the proposed algorithm can be obtain the minimum number of workstations m^* and can be reduce the cycle time to T_max < c then metaheuristic methods. Also, proposed clustering algorithm maximizes the line efficiency and minimizes the variance between workers operation times.

• Computers and Concrete
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• v.29 no.6
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• pp.375-391
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• 2022

This paper examined the impact of the cross-sectional structure on the structural results under different loading conditions of reinforced concrete (RC) members' management limited in Carbon Fiber Reinforced Polymers (CFRP). The mechanical properties of CFRC was investigated, then, totally 32 samples were examined. Test parameters included the cross-sectional shape as square, rectangular and circular with two various aspect rates and loading statues. The loading involved concentrated loading, eccentric loading with a ratio of 0.46 to 0.6 and pure bending. The results of the test revealed that the CFRP increased ductility and load during concentrated processing. A cross sectional shape from 23 to 44 percent was increased in load capacity and from 250 to 350 percent increase in axial deformation in rectangular and circular sections respectively, affecting greatly the accomplishment of load capacity and ductility of the concentrated members. Two Artificial Intelligence Models as Extreme Learning Machine (ELM) and Particle Swarm Optimization (PSO) were used to estimating the tensile and flexural strength of specimen. On the basis of the performance from RMSE and RSQR, C-Shape CFRC was greater tensile and flexural strength than any other FRP composite design. Because of the mechanical anchorage into the matrix, C-shaped CFRCC was noted to have greater fiber-matrix interfacial adhesive strength. However, with the increase of the aspect ratio and fiber volume fraction, the compressive strength of CFRCC was reduced. This possibly was due to the fact that during the blending of each fiber, the volume of air input was increased. In addition, by adding silica fumed to composites, the tensile and flexural strength of CFRCC is greatly improved.

• Geomechanics and Engineering
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• v.30 no.6
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• pp.489-502
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• 2022

In this study, a Gaussian process regression (GPR) model as well as six GPR-based metaheuristic optimization models, including GPR-PSO, GPR-GWO, GPR-MVO, GPR-MFO, GPR-SCA, and GPR-SSO, were developed to predict fly-rock distance in the blasting operation of open pit mines. These models included GPR-SCA, GPR-SSO, GPR-MVO, and GPR. In the models that were obtained from the Soungun copper mine in Iran, a total of 300 datasets were used. These datasets included six input parameters and one output parameter (fly-rock). In order to conduct the assessment of the prediction outcomes, many statistical evaluation indices were used. In the end, it was determined that the performance prediction of the ML models to predict the fly-rock from high to low is GPR-PSO, GPR-GWO, GPR-MVO, GPR-MFO, GPR-SCA, GPR-SSO, and GPR with ranking scores of 66, 60, 54, 46, 43, 38, and 30 (for 5-fold method), respectively. These scores correspond in conclusion, the GPR-PSO model generated the most accurate findings, hence it was suggested that this model be used to forecast the fly-rock. In addition, the mutual information test, also known as MIT, was used in order to investigate the influence that each input parameter had on the fly-rock. In the end, it was determined that the stemming (T) parameter was the most effective of all the parameters on the fly-rock.

• Geomechanics and Engineering
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• v.31 no.2
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• pp.129-147
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• 2022

The properties of soil are naturally highly variable and thus, to ensure proper safety and reliability, we need to test a large number of samples across the length and depth. In pile foundations, conducting field tests are highly expensive and the traditional empirical relations too have been proven to be poor in performance. The study proposes a state-of-art Particle Swarm Optimization (PSO) hybridized Artificial Neural Network (ANN), Extreme Learning Machine (ELM) and Adaptive Neuro Fuzzy Inference System (ANFIS); and comparative analysis of metaheuristic models (ANN-PSO, ELM-PSO, ANFIS-PSO) for prediction of bearing capacity of pile foundation trained and tested on dataset of nearly 300 dynamic pile tests from the literature. A novel ensemble model of three hybrid models is constructed to combine and enhance the predictions of the individual models effectively. The authenticity of the dataset is confirmed using descriptive statistics, correlation matrix and sensitivity analysis. Ram weight and diameter of pile are found to be most influential input parameter. The comparative analysis reveals that ANFIS-PSO is the best performing model in testing phase (R² = 0.85, RMSE = 0.01) while ELM-PSO performs best in training phase (R² = 0.88, RMSE = 0.08); while the ensemble provided overall best performance based on the rank score. The performance of ANN-PSO is least satisfactory compared to the other two models. The findings were confirmed using Taylor diagram, error matrix and uncertainty analysis. Based on the results ELM-PSO and ANFIS-PSO is proposed to be used for the prediction of bearing capacity of piles and ensemble learning method of joining the outputs of individual models should be encouraged. The study possesses the potential to assist geotechnical engineers in the design phase of civil engineering projects.

• Geomechanics and Engineering
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• v.31 no.4
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• pp.339-352
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• 2022

Studying slope stability is an important branch of civil engineering. In this way, engineers have employed machine learning models, due to their high efficiency in complex calculations. This paper examines the robustness of various novel optimization schemes, namely equilibrium optimizer (EO), Harris hawks optimization (HHO), water cycle algorithm (WCA), biogeography-based optimization (BBO), dragonfly algorithm (DA), grey wolf optimization (GWO), and teaching learning-based optimization (TLBO) for enhancing the performance of adaptive neuro-fuzzy inference system (ANFIS) in slope stability prediction. The hybrid models estimate the factor of safety (F_S) of a cohesive soil-footing system. The role of these algorithms lies in finding the optimal parameters of the membership function in the fuzzy system. By examining the convergence proceeding of the proposed hybrids, the best population sizes are selected, and the corresponding results are compared to the typical ANFIS. Accuracy assessments via root mean square error, mean absolute error, mean absolute percentage error, and Pearson correlation coefficient showed that all models can reliably understand and reproduce the F_S behavior. Moreover, applying the WCA, EO, GWO, and TLBO resulted in reducing both learning and prediction error of the ANFIS. Also, an efficiency comparison demonstrated the WCA-ANFIS as the most accurate hybrid, while the GWO-ANFIS was the fastest promising model. Overall, the findings of this research professed the suitability of improved intelligent models for practical slope stability evaluations.

• Structural Engineering and Mechanics
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• v.47 no.4
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• pp.513-530
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• 2013

In this paper, an improved version of particle swarm optimization based optimum design algorithm (IPSO) is presented for the steel grillage systems. The optimum design problem is formulated considering the provisions of American Institute of Steel Construction concerning Load and Resistance Factor Design. The optimum design algorithm selects the appropriate W-sections for the beams of the grillage system such that the design constraints are satisfied and the grillage weight is the minimum. When an improved version of the technique is extended to be implemented, the related results and convergence performance prove to be better than the simple particle swarm optimization algorithm and some other metaheuristic optimization techniques. The efficiency of different inertia weight parameters of the proposed algorithm is also numerically investigated considering a number of numerical grillage system examples.

• ETRI Journal
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• v.30 no.1
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• pp.129-140
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• 2008

In this paper, we propose a novel ant colony optimization (ACO)-based test scheduling method for testing network-on-chip (NoC)-based systems-on-chip (SoCs), on the assumption that the test platform, including specific methods and configurations such as test packet routing, generation, and absorption, is installed. The ACO metaheuristic model, inspired by the ant's foraging behavior, can autonomously find better results by exploring more solution space. The proposed method efficiently combines the rectangle packing method with ACO and improves the scheduling results by dynamically choosing the test-access-mechanism widths for cores and changing the testing orders. The power dissipation and variable test clock mode are also considered. Experimental results using ITC'02 benchmark circuits show that the proposed algorithm can efficiently reduce overall test time. Moreover, the computation time of the algorithm is less than a few seconds in most cases.

• Journal of Internet Computing and Services
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• v.21 no.3
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• pp.21-28
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• 2020

Fireworks Algorithm (FWA) is a relatively novel swarm-based metaheuristic algorithm for global optimization. To solve the low-efficient local searching problem and convergence of the FWA, this paper presents a Variable Amplitude Coefficient Fireworks Algorithm with Uniform Local Search Operator (namely VACUFWA). Firstly, the explosive amplitude is used to adjust improving the convergence speed dynamically. Secondly, Uniform Local Search (ULS) enhances exploitation capability of the FWA. Finally, the ULS and Variable Amplitude Coefficient operator are used in the VACUFWA. The comprehensive experiment carried out on 13 benchmark functions. Its results indicate that the performance of VACUFWA is significantly improved compared with the FWA, Differential Evolution, and Particle Swarm Optimization.