• Steel and Composite Structures
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• 제39권1호
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• pp.81-93
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• 2021

Splitting tensile strength (STS) is an important mechanical parameter of concrete. This study offers novel methodologies for the early prediction of this parameter. Artificial neural network (ANN), which is a leading predictive method, is synthesized with two metaheuristic algorithms, namely atom search optimization (ASO) and equilibrium optimizer (EO) to achieve an optimal tuning of the weights and biases. The models are applied to data collected from the published literature. The sensitivity of the ASO and EO to the population size is first investigated, and then, proper configurations of the ASO-NN and EO-NN are compared to the conventional ANN. Evaluating the prediction results revealed the excellent efficiency of EO in optimizing the ANN. Accuracy improvements attained by this algorithm were 13.26 and 11.41% in terms of root mean square error and mean absolute error, respectively. Moreover, it raised the correlation from 0.89958 to 0.92722. This is while the results of the conventional ANN were slightly better than ASO-NN. The EO was also a faster optimizer than ASO. Based on these findings, the combination of the ANN and EO can be an efficient non-destructive tool for predicting the STS.

• Steel and Composite Structures
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• 제51권4호
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• pp.417-440
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• 2024

Artificial neural networks (ANN) have been the focus of several studies when it comes to evaluating the pile's bearing capacity. Nonetheless, the principal drawbacks of employing this method are the sluggish rate of convergence and the constraints of ANN in locating global minima. The current work aimed to build four ANN-based prediction models enhanced with methods from the black hole algorithm (BHA), league championship algorithm (LCA), shuffled complex evolution (SCE), and symbiotic organisms search (SOS) to estimate the carrying capacity of piles in cold climates. To provide the crucial dataset required to build the model, fifty-eight concrete pile experiments were conducted. The pile geometrical properties, internal friction angle 𝛗 shaft, internal friction angle 𝛗 tip, pile length, pile area, and vertical effective stress were established as the network inputs, and the BHA, LCA, SCE, and SOS-based ANN models were set up to provide the pile bearing capacity as the output. Following a sensitivity analysis to determine the optimal BHA, LCA, SCE, and SOS parameters and a train and test procedure to determine the optimal network architecture or the number of hidden nodes, the best prediction approach was selected. The outcomes show a good agreement between the measured bearing capabilities and the pile bearing capacities forecasted by SCE-MLP. The testing dataset's respective mean square error and coefficient of determination, which are 0.91846 and 391.1539, indicate that using the SCE-MLP approach as a practical, efficient, and highly reliable technique to forecast the pile's bearing capacity is advantageous.

• Structural Engineering and Mechanics
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• 제65권3호
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• pp.233-241
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• 2018

In this paper, the Colliding Bodies Optimization (CBO), Enhanced Colliding Bodies Optimization (ECBO) and Vibrating Particles System (VPS) algorithms and the force method are used for the simultaneous analysis and design of truss structures. The presented technique is applied to the design and analysis of some planer and spatial trusses. An efficient method is introduced using the CBO, ECBO and VPS to design trusses having members of prescribed stress ratios. Finally, the minimum weight design of truss structures is formulated using the CBO, ECBO and VPS algorithms and applied to some benchmark problems from literature. These problems have been designed by using displacement method as analyzer, and here these are solved for the first time using the force method. The accuracy and efficiency of the presented method is examined by comparing the resulting design parameters and structural weight with those of other existing methods.

• Smart Structures and Systems
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• 제26권4호
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• pp.451-468
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• 2020

Although model updating has been widely applied using a specific optimization algorithm with a single objective function using frequencies, mode shapes or frequency response functions, there are few studies that investigate hybrid optimization algorithms for real structures. Many of them did not take into account the sensitivity of the updating parameters to the model outputs. Therefore, in this paper, optimization algorithms and sensitivity analysis are applied for model updating of a real cable-stayed bridge, i.e., the Kien bridge in Vietnam, based on experimental data. First, a global sensitivity analysis using Morris method is employed to find out the most sensitive parameters among twenty surveyed parameters based on the outputs of a Finite Element (FE) model. Then, an objective function related to the differences between frequencies, and mode shapes by means of MAC, COMAC and eCOMAC indices, is introduced. Three metaheuristic algorithms, namely Gravitational Search Algorithm (GSA), Particle Swarm Optimization algorithm (PSO) and hybrid PSOGSA algorithm, are applied to minimize the difference between simulation and experimental results. A laboratory pipe and Kien bridge are used to validate the proposed approach. Efficiency and reliability of the proposed algorithms are investigated by comparing their convergence rate, computational time, errors in frequencies and mode shapes with experimental data. From the results, PSO and PSOGSA show good performance and are suitable for complex and time-consuming analysis such as model updating of a real cable-stayed bridge. Meanwhile, GSA shows a slow convergence for the same number of population and iterations as PSO and PSOGSA.

• Journal of Electrical Engineering and Technology
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• 제10권4호
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• pp.1441-1452
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• 2015

This paper proposes a new Hybrid Particle Swarm Optimization (HPSO) method that integrates the Evolutionary Programming (EP) and Particle Swarm Optimization (PSO) techniques. The proposed method is applied to solve Economic Dispatch(ED) problems considering prohibited operating zones, ramp rate limits, capacity limits and power balance constraints. In the proposed HPSO method, the best features of both EP and PSO are exploited, and it is capable of finding the most optimal solution for the non-linear optimization problems. For validating the proposed method, it has been tested on the standard three, six, fifteen and twenty unit test systems. The numerical results show that the proposed HPSO method is well suitable for solving non-linear economic dispatch problems, and it outperforms the EP, PSO and other modern metaheuristic optimization methods reported in the recent literatures.

• Journal of Computing Science and Engineering
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• 제8권2호
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• pp.87-93
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• 2014

Coronary artery disease (CAD) is currently a prevalent disease from which many people suffer. Early detection and treatment could reduce the risk of heart attack. Currently, the golden standard for the diagnosis of CAD is angiography, which is an invasive procedure. In this article, we propose an algorithm that uses data mining techniques, a fuzzy expert system, and the imperialist competitive algorithm (ICA), to make CAD diagnosis by a non-invasive procedure. The ICA is used to adjust the fuzzy membership functions. The proposed method has been evaluated with the Cleveland and Hungarian datasets. The advantage of this method, compared with others, is the interpretability. The accuracy of the proposed method is 94.92% by 11 rules, and the average length of 4. To compare the colonial competitive algorithm with other metaheuristic algorithms, the proposed method has been implemented with the particle swarm optimization (PSO) algorithm. The results indicate that the colonial competition algorithm is more efficient than the PSO algorithm.

• ETRI Journal
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• 제30권1호
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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.

• 한국산학기술학회논문지
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• 제19권9호
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• pp.304-315
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• 2018

화음탐색법은 근래에 개발된 메타휴리스틱 알고리즘 중 하나로, 다양한 분야의 최적화 문제에 적용되어 많은 연구자들에게 널리 알려진 바 있다. 하지만 최적화 문제의 복잡성이 날로 증가하여 기존 화음탐색법으로는 최적해를 효율적으로 탐색할 수 없는 경우가 증가하고 있다. 이를 개선하기 위해 기존 매개변수 설정의 변경 및 다른 메타휴리스틱 알고리즘의 특성과의 융합 등을 통해 화음탐색법의 성능을 향상시킨 연구가 다수 존재한다. 본 연구에서는 기존 화음탐색법의 매개변수설정 방법과 해탐색 성능을 개선한 모방 화음탐색법 (Copycat Harmony Search, CcHS)을 제시하였다. 모방 화음탐색법의 성능을 검증하기 위하여 대표적인 수학적 최적화 문제에 적용하여 기존에 개발되었던 향상된 형태의 화음탐색법 알고리즘들과 결과를 비교하였다. 모방 화음탐색법은 모든 수학적 최적화 문제에서 다른 알고리즘보다 전역해에 가까운 해를 찾음으로써 최적해 탐색의 효율성을 입증하였다. 또한, 알고리즘의 공학문제의 적용성을 분석하기 위하여 기존에 널리 적용되었던 상수도관망 최적설계 문제에 CcHS를 적용하였다. 그 결과 본 연구에서는 기존 화음탐색법이 제안한 최소 설계비용보다 약 21.91% 더 저렴한 비용을 제시하였다.

• 한국인터넷방송통신학회논문지
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• 제22권1호
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• pp.95-101
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• 2022

NP-난제로 알려진 양측 조립라인 균형문제는 주로 메타휴리스틱 방법들을 적용하여 해를 구하고 있다. 본 논문은 총 작업완료시간 W와 순환시간 c가 주어진 양측 조립라인의 선행순서도에서 좌측, 우측과 좌·우측 무관으로 공정들을 분류하고, 좌측과 우측 각각에 대해 M^* = ${\lceil}$W/c${\rceil}$개의 작업대에 T_i = c^* ± α < c, c^* = ${\lceil}$W/m^*${\rceil}$이 되도록 공정들을 할당하는 병렬군집 알고리즘을 제안하였다. 제안된 알고리즘을 4개의 실험데이터, 17개의 c에 적용한 결과, 기존의 메타휴리스틱 방법들에 비해 최소 작업대 수 m^*를 구하였으며, T_max < c로 순환시간을 단축하였다. 또한, 제안된 알고리즘은 휴리스틱 방법임에도 불구하고, 조립라인 효율성의 극대화와 작업자간 작업시간 편차를 최소화시킬 수 있었다.

• Geomechanics and Engineering
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• 제30권6호
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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.