• Journal of Korea Society of Industrial Information Systems
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• 제27권1호
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• pp.37-48
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• 2022

In this study, a hybrid meta-heuristic approach is proposed for solving engineering design optimization problems. Various approaches in many literatures have been proposed to solve engineering optimization problems with various types of decision variables and complex constraints. Unfortunately, however, their efficiencies for locating optimal solution do not be highly improved. Therefore, we propose a hybrid meta-heuristic approach for improving their weaknesses. the proposed GA-VNS-HC approach is combining genetic algorithm (GA) for global search with variable neighborhood search (VNS) and hill climbing (HC) for local search. In case study, various types of engineering design optimization problems are used for proving the efficiency of the proposed GA-VNS-HC approach

• Composites Research
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• 제35권1호
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• pp.31-37
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• 2022

In this study, an optimal structural design framework has been developed for the structural design of composite helicopter blades. The optimal design framework is constructed using PSGA (Particle Swarm assisted Genetic Algorithm), which combines the genetic algorithm and particle swarm optimizer. The optimization process consists of a finite element (FE) modeling over the blade section, two-dimensional (2D) cross-sectional FE analysis, and 1D rotating blade analysis. In the design process, the geometric curves and surfaces are formed using the B-spline scheme while discretizing the sections via a FE mesh generation program Gmsh. The blade cross-sections are created in accordance with the design variables when performing the blade structural analysis. The proposed optimization design framework is applied to a modernization of the HART II (Higher-harmonic Aeroacoustics Rotor Test II) blades. It is demonstrated that an improved blade design is reached through the current optimization framework with the satisfaction of all design requirements set for the study.

• Structural Engineering and Mechanics
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• 제70권5호
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• pp.513-524
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• 2019

This study proposed an improved particle swarm optimization (IPSO) method ensemble with kriging model for model updating. By introducing genetic algorithm (GA) and grouping strategy together with elite selection into standard particle optimization (PSO), the IPSO is obtained. Kriging metamodel serves for predicting the structural responses to avoid complex computation via finite element model. The combination of IPSO and kriging model shall provide more accurate searching results and obtain global optimal solution for model updating compared with the PSO, Simulate Annealing PSO (SimuAPSO), BreedPSO and PSOGA. A plane truss structure and ASCE Benchmark frame structure are adopted to verify the proposed approach. The results indicated that the hybrid of kriging model and IPSO could serve for model updating effectively and efficiently. The updating results further illustrated that IPSO can provide superior convergent solutions compared with PSO, SimuAPSO, BreedPSO and PSOGA.

• Steel and Composite Structures
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• 제47권4호
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• pp.513-521
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• 2023

The goal of this work is to create a new and improved GWO (Grey Wolf Optimizer), the so-called Robot GWO (RGWO), for dynamic and static target tracking involving multiple robots in unknown environmental conditions. From applying ourselves with the Gray Wolf Optimization Algorithm (GWO) and how it works, as the name suggests, it is a nature-inspired metaheuristic based on the behavior of wolf packs. Like other nature-inspired metaheuristics such as genetic algorithms and firefly algorithms, we explore the search space to find the optimal solution. The results also show that the improved optimal control method can provide superior power characteristics even when operating conditions and design parameters are changed.

• Proceedings of the IEEK Conference
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• 대한전자공학회 1998년도 하계종합학술대회논문집
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• pp.161-164
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• 1998

광대역 주파수 상에서 외부로부터 입사하는 전자파를 산란시키지 않고 유전체 내부에서 흡수시키기 위해 유전체를 다층으로 배열하여 전파 흡수체를 최적 설계하고자 한다. 수직 및 여러 각도로 전파가 입사하는 경우 각 유전층의 두께, 유전 상수, 손실 탄젠트 등의 설계변수를 유전자 앨거리즘을 이용하여 최적화한다. 다극함수에서의 부분 초기화 유전자 앨거리즘의 성능 향상을 위해 부분 초기화율, 부분 초기화 시점, 스케일 인자의 변화에 따른 성능을 비교, 개선하여 흡수체 설계에 적용하였다.

• Proceedings of the KIEE Conference
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• 대한전기학회 2000년도 학술대회 논문집 전문대학교육위원
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• pp.104-106
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• 2000

This paper presents an algorithm for optimal reactive power dispatch problem based on Improved Genetic Algorithm(IGA). Optimal Reactive Power Dispatch (ORPD) is particularized to the minimization of transmission line losses by suitable selection of generator reactive power outputs and transformer tap setting. For the objective, in this paper, Loss Re-Distribution Algorithm(LRDA) is new applied to the equality constraint of ORPD. The proposed method has been evaluated on the IEEE 30 bus system. Results of the application of the method are compared with a base case.

• Journal of the Korean Institute of Intelligent Systems
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• 제20권3호
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• pp.441-446
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• 2010

Each robot decides and behaviors themselves surrounding circumstances in the swarm robot system. Robots have to conduct tasks allowed through cooperation with other robots. Therefore each robot should have the ability to learn and evolve in order to adapt to a changing environment. In this paper, we proposed learning based on Q-learning algorithm and evolutionary using Harmony Search algorithm and are trying to improve the accuracy using Harmony Search Algorithm, not the Genetic Algorithm. We verify that swarm robot has improved the ability to perform the task.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2003년도 ICCAS
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• pp.645-650
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• 2003

Legged robots can avoid an obstacle by crawling-over or striding, according to the obstacle’s nature and the current state of the robot. Thus, it can be observed that the mobility efficiency to reach a destination is improved by such action. Moreover, if robots have many legs like 4-legged or 6-legged types, then the robot movement range is affected by the order of swing leg. In this paper, the avoidance action of a quadruped robot is generated by a neural network (NN) whose inputs are information on the position of the destination, the obstacle configuration and the robot's self-state. To realize a free gait in static walking, the order of swing leg is determined using an another NN whose inputs are the amount of movements and the robot’s self-state. The design parameter of the latter NN is adjusted by using genetic algorithm (GA).

• Proceedings of the Korean Society of Soil and Groundwater Environment Conference
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• 한국지하수토양환경학회 2001년도 추계학술발표회
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• pp.204-207
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• 2001

An optimization process for the design of groundwater remediation is developed by simultaneously considering the well location and the pumping rate. This process uses two independent models: simulation and optimization model. Groundwater flow and contaminant transport are simulated with MODFLOW and MT3D in simulation model. In optimization model, the location and pumping rate of each well are determined and evaluated by the genetic algorithm. In a homogeneous and symmetric domain, the developed model is tested using sequential pairs for pumping rate of each well, and the model gives more improved result than the model using sequential pairs. In application cases, the suggested optimal design shows that the main location of wells is on the centerline of contaminate distribution. The resulting optimal design also shows that the well with maximum pumping rate is replaced with the further one from the contaminant source along flow direction and that the optimal pumping rate declines when more cleanup time is given. But the optimal pumping rate is not linearly proportional to the cleanup time and the minimum total pumping volume does not coincide with the optimal pumping rate.

• Proceedings of the Korean Institute of Building Construction Conference
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• 한국건축시공학회 2010년도 춘계 학술논문 발표대회 1부
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• pp.143-147
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• 2010

Due to the diversification and complication of construction projects, controlling risks from the early design-planning phase gives huge impact on success of the construction project. As a part of managing uncertainties it is also important to estimate the project cost several times. Especially, estimating project cost in the early stage gives effects on making a budget for projects. This study estimated the apartment project cost using case-based reasoning(CBR), which is the process of solving new problems based on the past problems. For this, we deduced the apartment cost influence factors which can be gathered in the early stage of project. Based on the factors we established the database for apartment project and calculated the attribute value, attribute similarity and case similarity. Although we retrieve the most similar case from the database, it is very hard to utilize it directly due to the uniqueness of each project. So, Genetic Algorithm(GA) was applied in revising the cost of the retrieved-case. Therefore, the accuracy of the prediction was improved by GA optimization.