• 한국항공우주학회지
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• 제40권11호
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• pp.963-971
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• 2012

본 논문에서는 항공기 날개의 최적설계 툴을 개발하고, 다중하중스펙트럼 조건에서 항공기 날개의 구조부재에 대한 최적설계를 수행하였다. 공력하중을 계산하기 위해 2차원 CFD의 해석 결과를 사용하였다. 최적화 설계 변수는 리브 및 스파의 개수 및 위치와 두께를 선정하였다. 각각의 비행유형에 대한 순항속도에서의 공력해석 결과로 응력해석을 수행한 후, 피로하중 스펙트럼 임무선도를 활용하여 피로파손해석을 수행하였다. 다중하중 조건의 적용을 위해 손상누적법을 적용하였다. 응력해석에 이은 파손 해석을 포함하는 항공기 날개의 경량화를 진행하였다. 다변수 문제를 효과적으로 최적화하기 위해 PSO(Particle Swarm Optimization) 알고리즘을 사용하였다.

• Structural Engineering and Mechanics
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• 제63권4호
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• pp.429-438
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• 2017

Structural design has an imperative role in deciding the failure possibility of a Reinforced Concrete (RC) structure. Recent research works achieved the goal of predicting the structural failure of the RC structure with the assistance of machine learning techniques. Previously, the Artificial Neural Network (ANN) has been trained supported by Particle Swarm Optimization (PSO) to classify RC structures with reasonable accuracy. Though, keeping in mind the sensitivity in predicting the structural failure, more accurate models are still absent in the context of Machine Learning. Since the efficiency of multi-objective optimization over single objective optimization techniques is well established. Thus, the motivation of the current work is to employ a Multi-objective Genetic Algorithm (MOGA) to train the Neural Network (NN) based model. In the present work, the NN has been trained with MOGA to minimize the Root Mean Squared Error (RMSE) and Maximum Error (ME) toward optimizing the weight vector of the NN. The model has been tested by using a dataset consisting of 150 RC structure buildings. The proposed NN-MOGA based model has been compared with Multi-layer perceptron-feed-forward network (MLP-FFN) and NN-PSO based models in terms of several performance metrics. Experimental results suggested that the NN-MOGA has outperformed other existing well known classifiers with a reasonable improvement over them. Meanwhile, the proposed NN-MOGA achieved the superior accuracy of 93.33% and F-measure of 94.44%, which is superior to the other classifiers in the present study.

• 융합신호처리학회논문지
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• 제19권2호
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• pp.77-88
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• 2018

본 논문은 입력 포화를 가지는 불확실한 전기 구동 로봇 시스템에 대해 입자 군집 최적화(PSO)를 이용한 방사형 기저 함수 신경망(RBFNN) 기반 분산 적응 추종 제어 기법을 제안한다. 실제적으로 로봇 시스템에서는 구동기의 포화로 인해 입력 전압과 전류 신호 크기가 제한된다. 제안된 제어기는 이러한 입력 포화를 극복하며, 어떠한 로봇 링크 및 구동기의 모델 파라미터들을 요구하지 않는다. 제시된 PSO 기법에서 쓰인 적합도 함수는 추종 오차만이 아니라 전압과 전류의 크기를 포함하는 다중 목적 함수로 표현된다. PSO 기법을 이용하여 제어 이득과 방사형 기저 함수의 개수가 자동으로 조정되어 제어 시스템의 성능이 개선된다. 리아푸노프 안정도 해석에 의해 전체 제어 시스템의 안정도가 보장된다. 제안된 제어 기법의 타당성과 강인성이 시뮬레이션 결과를 통해 검증된다.

• Nuclear Engineering and Technology
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• 제55권6호
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• pp.2125-2138
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• 2023

The printed circuit heat exchanger (PCHE) with airfoil fins has the benefits of high compactness, high efficiency and superior heat transfer performance. A novel multi-objective optimization approach is presented to design the airfoil fin PCHE in this paper. Three optimization design variables (the vertical number, the horizontal number and the staggered number) are obtained by means of dimensionless airfoil fin arrangement parameters. And the optimization objective is to maximize the Nusselt number (Nu) and minimize the Fanning friction factor (f). Firstly, in order to investigate the impact of design variables on the thermal-hydraulic performance, a parametric study via the design of experiments is proposed. Subsequently, the relationships between three optimization design variables and two objective functions (Nu and f) are characterized by an improved particle swarm optimization-backpropagation artificial neural network. Finally, a multi-objective optimization is used to construct the Pareto optimal front, in which the non-dominated sorting genetic algorithm II is used. The comprehensive performance is found to be the best when the airfoil fins are completely staggered arrangement. And the best compromise solution based on the TOPSIS method is identified as the optimal solution, which can achieve the requirement of high heat transfer performance and low flow resistance.

• Earthquakes and Structures
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• 제17권1호
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• pp.63-73
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• 2019

This paper studies damage detection as an optimization problem. A new objective function based on changes in natural frequencies, and Natural Frequency Vector Assurance Criterion (NFVAC) was developed. Due to their easy and fast acquisition, natural frequencies were utilized to detect structural damages. Moreover, they are sensitive to stiffness reduction. The method presented here consists of two stages. Firstly, Finite Element Model (FEM) is updated. Secondly, damage severities and locations are determined. To minimize the proposed objective function, a new bio-inspired optimization algorithm called salp swarm was employed. Efficiency of the method presented here is validated by three experimental examples. The first example relates to three-story shear frame with two single damage cases in the first story. The second relates to a five-story shear frame with single and multiple damage cases in the first and third stories. The last one relates to a large-scale eight-story shear frame with minor damage case in the first and third stories. Moreover, the performance of Salp Swarm Algorithm (SSA) was compared with Particle Swarm Optimization (PSO). The results show that better accuracy is obtained using SSA than using PSO. The obtained results clearly indicate that the proposed method can be used to determine accurately and efficiently both damage location and severity in multi-story shear frames.

• Structural Engineering and Mechanics
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• 제84권4호
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• pp.489-502
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• 2022

This paper is aimed at developing an optimization-based Finite Element model updating approach for structural damage identification and quantification. A modal flexibility-based error function is introduced, which uses modal assurance criterion to formulate the updating problem as an optimization problem. Because of the inexplicit input/output relationship between the candidate solutions and the error function's output, a robust and efficient optimization algorithm should be employed to evaluate the solution domain and find the global extremum with high speed and accuracy. This paper proposes a new multi-stage Selective Particle Swarm Optimization (SPSO) algorithm to solve the optimization problem. The proposed multi-stage strategy not only fixes the premature convergence of the original Particle Swarm Optimization (PSO) algorithm, but also increases the speed of the search stage and reduces the corresponding computational costs, without changing or adding extra terms to the algorithm's formulation. Solving the introduced objective function with the proposed multi-stage SPSO leads to a smart feedback-wise and self-adjusting damage detection method, which can effectively assess the health of the structural systems. The performance and precision of the proposed method are verified and benchmarked against the original PSO and some of its most popular variants, including SPSO, DPSO, APSO, and MSPSO. For this purpose, two numerical examples of complex civil engineering structures under different damage patterns are studied. Comparative studies are also carried out to evaluate the performance of the proposed method in the presence of measurement errors. Moreover, the robustness and accuracy of the method are validated by assessing the health of a six-story shear-type building structure tested on a shake table. The obtained results introduced the proposed method as an effective and robust damage detection method even if the first few vibration modes are utilized to form the objective function.

• Journal of Korean Neurosurgical Society
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• 제63권1호
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• pp.99-107
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• 2020

Objective : The purpose of this study was to report the results of pedicle subtraction osteotomy (PSO) for fixed sagittal imbalance with a minimum 2-year follow-up. Besides, authors evaluated the effect of adjunctive multi-level posterior column osteotomy (PCO) on achievement of additional lumbar lordosis (LL) during PSO. Methods : A total of 31 consecutive patients undergoing PSO for fixed sagittal imbalance were enrolled and analyzed. Correction angle of osteotomized vertebra (PSO angle) and other radiographic parameters including pelvic incidence (PI), thoracic kyphosis, LL, and sagittal vertical axis (SVA) were evaluated. Clinical outcomes and surgical complications were also assessed. Results : The mean age was 66.0±9.3 years with a mean follow-up period of 33.2±10.5 months. The mean number of fused segments was 9.6±3.5. The mean operative time and surgical bleeding were 475.9±160.5 minutes and 1406.1±932.1 mL, respectively. The preoperative SRS-22 score was 2.3±0.7 and improved to 3.2±0.8 at the final follow-up. The mean PI was 54.5±9.5°. LL was changed from 7.0±28.9° to -50.2±13.2°. The PSO angle was 33.7±13.5° (15.6±20.1° preoperatively, -16.1±19.4° postoperatively). The difference of correction angle of LL (57.3°) was greater about 23.6° than which of PSO angle (33.7°). SVA was improved from 189.5±93.0 mm, preoperatively to 12.4±40.8 mm, postoperatively. There occurred six, eight, and 14 cases of complications at intraoperative, early (<2 weeks) postoperative, and late (≥2 weeks) postoperative period, respectively. Additional operations were needed in nine patients due to the complications. Conclusion : PSO could provide satisfactory results for patients with fixed sagittal imbalance regarding clinical and radiographic outcomes. Additional correction of LL could be achieved with conduction of adjunctive multi-level PCOs during PSO.

• Journal of Electrical Engineering and Technology
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• 제5권2호
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• pp.179-190
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• 2010

This paper describes a new stochastic heuristic algorithm in engineering problem optimization especially in power system applications. An improved particle swarm optimization (PSO) called adaptive particle swarm optimization (APSO), mixed with simulated annealing (SA), is introduced and referred to as APSO-SA. This algorithm uses a novel PSO algorithm (APSO) to increase the convergence rate and incorporate the ability of SA to avoid being trapped in a local optimum. The APSO-SA algorithm efficiency is verified using some benchmark functions. This paper presents the application of APSO-SA to find the optimal location, type and size of flexible AC transmission system devices. Two types of FACTS devices, the thyristor controlled series capacitor (TCSC) and the static VAR compensator (SVC), are considered. The main objectives of the presented method are increasing the voltage stability index and over load factor, decreasing the cost of investment and total real power losses in the power system. In this regard, two cases are considered: single-type devices (same type of FACTS devices) and multi-type devices (combination of TCSC, SVC). Using the proposed method, the locations, type and sizes of FACTS devices are obtained to reach the optimal objective function. The APSO-SA is used to solve the above non.linear programming optimization problem for better accuracy and fast convergence and its results are compared with results of conventional PSO. The presented method expands the search space, improves performance and accelerates to the speed convergence, in comparison with the conventional PSO algorithm. The optimization results are compared with the standard PSO method. This comparison confirms the efficiency and validity of the proposed method. The proposed approach is examined and tested on IEEE 14 bus systems by MATLAB software. Numerical results demonstrate that the APSO-SA is fast and has a much lower computational cost.

• Industrial Engineering and Management Systems
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• 제12권2호
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• pp.151-160
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• 2013

In the past decades, several algorithms based on evolutionary approaches have been proposed for solving job shop scheduling problems (JSP), which is well-known as one of the most difficult combinatorial optimization problems. Most of them have concentrated on finding optimal solutions of a single objective, i.e., makespan, or total weighted tardiness. However, real-world scheduling problems generally involve multiple objectives which must be considered simultaneously. This paper proposes an efficient particle swarm optimization based approach to find a Pareto front for multi-objective JSP. The objective is to simultaneously minimize makespan and total tardiness of jobs. The proposed algorithm employs an Elite group to store the updated non-dominated solutions found by the whole swarm and utilizes those solutions as the guidance for particle movement. A single swarm with a mixture of four groups of particles with different movement strategies is adopted to search for Pareto solutions. The performance of the proposed method is evaluated on a set of benchmark problems and compared with the results from the existing algorithms. The experimental results demonstrate that the proposed algorithm is capable of providing a set of diverse and high-quality non-dominated solutions.

• Advances in Computational Design
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• 제5권3호
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• pp.209-231
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• 2020

In order to evaluate the performance of three heuristic optimization algorithms, namely, simulated annealing (SA), genetic algorithm (GA) and particle swarm optimization (PSO) for optimal stacking sequence of laminated composite plates with respect to critical buckling load and non-dimensional natural frequencies, a multi-objective optimization procedure is developed using the weighted summation method. Classical lamination theory and first order shear deformation theory are employed for critical buckling load and natural frequency computations respectively. The analytical critical buckling load and finite element calculation schemes for natural frequencies are validated through the results obtained from literature. The comparative study takes into consideration solution and computational time parameters of the three algorithms in the statistical evaluation scheme. The results indicate that particle swarm optimization (PSO) considerably outperforms the remaining two methods for the special problem considered in the study.