• Transactions of Materials Processing
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• v.9 no.6
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• pp.644-652
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• 2000

Tube hydroforming is recently drawing attention of automotive industries due to its several advantages over conventional methods. It can produce wide range of products such as subframes, engine cradles, and exhaust manifolds with cheaper production cost by reducing overall number of processes. h successful tube hydroforming depends on the reasonable combination of the internal pressure and axial load at the tube ends. This paper deals with the optimal process design of hydroforming process using the genetic algorithm and neural network. An optimization technique is used in order to minimize the tube thickness variation by determining the optimal loading path in the tube expansion forming and the tube T-shape forming process.

• International Journal of Computer Science & Network Security
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• v.23 no.8
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• pp.85-90
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• 2023

The Ant Colony System (ACS) is a variant of Ant colony optimization algorithm which is well-known in Traveling Salesman Problem. This paper proposed a hybrid method based on genetic algorithm (GA) and ant colony system (ACS), called GACS, to solve traffic routing problem. In the GACS, we use genetic algorithm to optimize the ACS parameters that aims to attain the shortest trips and time through new functions to help the ants to update global and local pheromones. Our experiments are performed by the GACS framework which is developed from VANETsim with the ability of real map loading from open street map project, and updating traffic light in real-time. The obtained results show that our framework acquired higher performance than A-Star and classical ACS algorithms in terms of length of the best global tour and the time for trip.

• Composites Research
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• v.12 no.4
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• pp.42-54
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• 1999

FMLC(Fiber-Metal Laminate Composites) is a new structural material combining thin metal laminate with adhesive fiber prepreg, it nearly include all the advantage of metallic materials, for example: good plasticity, impact resistance, processibility, light weight and excellent fatigue properties. This research studied the optimum design of the FMLC subject to various loading conditions using genetic algorithm. The finite element method based on the shear deformation theory was used for the analysis of FMLC. Tasi-Hill failure criterion and Miser yield criterion were taken as fitness functions of the fiber prepreg and the metal laminate, respectively. The design variables were fiber orientation angles. In genetic algorithm, the tournament selection and the uniform crossover method were used. The elitist model was also used to be effective evolution strategy and the creeping random search method was adopted in order to approach a solution with high accuracy. Optimization results were given for various loading conditions and compared with CFRP(Carbon Fiber Reinforced Plastic). The results show that the FMLC is more excellent than the CFRP in point and uniform loading conditions and it is more stable to unexpected loading because the deviation of failure index is smaller than that of CFRP.

• Journal of Navigation and Port Research
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• v.33 no.8
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• pp.555-562
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• 2009

At container terminals, a major measurement of productivity can be work-efficiency in quay-side. At the apron, containers are loaded onto the ship and unloaded to apron by Q/C(Quay Crane). For improving the productivity of quay crane, the more efficient Y/T(Yard Tractor) operation method is necessary in container terminals. Between quay-side and yard area, current transferring methods is single-cycling which doesn't start loading unless it finishes unloading. Dual-cycling is a technique that can be used to improve the productivity of quay-side and utility of yard tractor by ship loading and unloading simultaneously. Using the dual-cycling at terminals only necessitates an operational change without purchasing extra equipment. Exactly, Y/T operation method has to be changed the dedicate system to pooling system. This paper presents an efficient ship loading and unloading plan in container terminals, which use the dual-cycling. We propose genetic and tabu search algorithm for this problem.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.33 no.3
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• pp.193-199
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• 2020

This study examines a method for identifying stiffness reductions in structures subjected to seismic loads and retrofitting effects using a combination of the finite element method and an advanced genetic algorithm. The novelty of this study is the application of seismic loading and its response to anomalies in the tested structure. The technique described in this study may enable not only detection of damaged elements but also the identification of their locations and the extent of damage due to seismic loading. To demonstrate the feasibility of the method, the advanced genetic algorithm is applied to frame and truss bridge structures subjected to El Centro and Pohang seismic loads. The results reveal the excellent computational efficiency of the method and its ability to prevent severe damage from earthquakes.

• Wind and Structures
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• v.20 no.4
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• pp.489-508
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• 2015

In this paper, a novel methodology is proposed to obtain optimum location of outriggers. The method utilizes genetic algorithm (GA) for shape and size optimization of outrigger-braced tall structures. In spite of previous studies (simplified methods), current study is based on exact modeling of the structure in a computer program developed on Matlab in conjunction with OpenSees. In addition to that, exact wind loading distribution is calculated in accordance with ASCE 7-10. This is novel since in previous studies wind loading distributions were assumed to be uniform or triangular. Also, a new penalty coefficient is proposed which is suitable for optimization of tall buildings. Newly proposed penalty coefficient improves the performance of GA and results in a faster convergence. Optimum location and number of outriggers is investigated. Also, contribution of factors like central core and outrigger rigidity is assessed by analyzing several design examples. According to the results of analysis, exact wind load distribution and modeling of all structural elements, yields optimum designs which are in contrast of simplified methods results. For taller frames significant increase of wind pressure changes the optimum location of outriggers obtained by simplified methods. Ratio of optimum location to the height of the structure for minimizing weight and satisfying serviceability constraints is not a fixed value. Ratio highly depends on height of the structure, core and outriggers stiffness and lateral wind loading distribution.

• Journal of Navigation and Port Research
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• v.28 no.6
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• pp.517-523
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• 2004

The container load planning is one of key factors for efficient operations of handling equipments at container ports. When the number of containers are large, finding a good solution using the conventional genetic algorithm is very time consuming. To obtain a good solution with considerably small effort, in this paper a pseudo-parallel genetic algorithm(PPGA) based on both the migration model and the ring topology is developed The performance of the PPGA is demonstrated through a test problem of determining the optimal loading sequence of the containers.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.8 no.1
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• pp.67-73
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• 1999

This paper presents resizing design optimization method by utilizing genetic algorithm(GA), which consists of three basic operators : reproduction, crossover and mutation. The fitness and penalty function for resizing optimization problem are defined, and the flowchart of the developed computer program along with the descriptions of each modules is presented. Also, modelling for flexible-body dynamic analysis is presented. The model is composed of bodies, joints, and force elements such as translational spring-damper-actuator. The design objects si to determine the wall thickness for minimum weight under dynamic displacement constraint.

• Geomechanics and Engineering
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• v.34 no.5
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• pp.561-575
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• 2023

This study investigates to provide a fast solution to the problem of bearing capacity in layered soils with easily obtainable parameters that does not require the use of any charts or calculations of different parameters. Therefore, a hybrid approach including both the finite element (FE) method and machine learning technique have been applied. Firstly, a FE model has been generated which is validated by the results of in-situ loading tests. Then, a total of 192 three-dimensional FE analyses have been performed. A data set has been created utilizing the soil properties, footing sizes, layered conditions used in the FE analyses and the ultimate bearing capacity values obtained from the FE analyses to be used in multigene genetic programming (MGGP). Problem has been modeled with five input and one output parameter to propose a bearing capacity formula. Ultimate bearing capacity values estimated from the proposed formula using data set consisting of 20 data independent of total data set used in MGGP modelling have been compared to the bearing capacities calculated with semi-empirical methods. It was observed that the MGGP method yielded successful results for the problem considered. The proposed formula provides reasonable predictions and efficient enough to be used in practice.

• Proceedings of the Korea Society for Simulation Conference
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• 2001.10a
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• pp.263-268
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• 2001

In this paper, we present a new heuristic algorithm fur buffer allocation in FMS (Flexible Manufacturing System). It is conducted by using a genetic algorithm and simulation. First, we model the system by using a simulation software, \"Arena\". Then, we apply a genetic algorithm to achieve an optimal solution. VBA blocks, which are kinds of add-in functions in Arena, are used to connect Arena with the genetic algorithm. The system being modeled has seven workstations, one loading/unloading station, and three AGVs (Automated Guided Vehicle). Also it contains three products, which each have their own machining order and processing times. We experimented with two kinds of buffer allocation problems with a proposed heuristic algorithm, and we will suggest a simple heuristic approach based on processing times and workloads to validate our proposed algorithm. The first experiment is to find a buffer profile to achieve the maximum throughput using a finite number of buffers. The second experiment is to find the minimum number of buffers to achieve the desired throughput. End of this paper, we compare the result of a proposed algorithm with the result of a simple buffer allocation heuristic based on processing times and workloads. We show that the proposed algorithm increase the throughput by 7.2%.t by 7.2%.