• Journal of the Korean Institute of Gas
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• v.19 no.1
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• pp.28-37
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• 2015

This study presents the development of a well placement optimization model, combining an artificial neural network, which enables high-speed calculation, with a simulated annealing algorithm. The conventional FDM simulator takes excessive time when used to perform a field scale reservoir simulation. In order to solve this problem, an artificial neural network was applied to the model to allow the simulation to be executed within a short time. Also by using the given result, the optimization method, SA algorithm, was implemented to automatically select the optimal location without taking any subjective experiences into consideration. By comparing the result of the developed model with the eclipse simulator, it was found that the prediction performance of the developed model has become favorable, and the speed of calculation performance has also been improved. Especially, the optimum value was estimated by performing a sensitivity analysis for the cooling rate and the initial temperature, which is the control parameter of SA algorithm. From this result, it was verified that the calculation performance has been improved, as well. Lastly, an optimization for the well placement was performed using the model, and it concluded the optimized place for the well by selecting regions with great productivity.

• International Journal of Internet, Broadcasting and Communication
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• v.12 no.4
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• pp.102-108
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• 2020

One of the key features of a mobile crowd-sensing (MCS) system is task allocation, which aims to recruit workers efficiently to carry out the tasks. Due to various constraints of the tasks (such as specific sensor requirement and a probabilistic guarantee of task completion) and workers heterogeneity, the task allocation become challenging. This assignment problem becomes more intractable because of the deadline of the tasks and a lot of possible task completion order or moving path of workers since a worker may perform multiple tasks and need to physically visit the tasks venues to complete the tasks. Therefore, in this paper, a hybrid search algorithm for task allocation called HST is proposed to address the problem, which employ a traveling salesman problem heuristic to find the task completion order. HST is developed based on the tabu search algorithm and exploits the premature convergence avoiding concepts from the genetic algorithm and simulated annealing. The experimental results verify that our proposed scheme outperforms the existing methods while satisfying given constraints.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2000.11a
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• pp.131-142
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• 2000

Facility layout is the early stage of system design that requires a mid-term or long-term plan. Since improper facility layout might incur substantial logistics cost including material handling and re-installment costs, due consideration must be given to decisions on facility layout. Facility layout is concerned with low to arrange equipment necessary for production in a given space. Its objective is to minimize the sum of all the products of each equipment's amount of flow multiplied by distance. Facility layout also is related to the issue of NP-complete, i.e., calculated amounts exponentially increase with the increase of the number of equipment. This study discusses Hybrid GA developed, as an algorithm for facility layout, to solve the above-mentioned problems. The algorithm, which is designed to efficiently place equipment, automatically produces a horizontal passageway by the block, if a designer provides the width and length of the space to be handled. In addition, this study demonstrates the validity of the Algorithm by comparing with existing algorithms that have been developed. We present a Hybrid GA approach to the facility layout problem that improves on existing work in terms of solution quality and method. Experimental results show that the proposed algorithm is able to produce better solution quality and more practical layouts than the ones obtained by applying existing algorithms.

• Journal of Korea Multimedia Society
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• v.10 no.7
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• pp.912-922
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• 2007

Evaluation of distance teaming systems needs a method that maintains a consistent level of difficulty for each test. In this paper, we propose a new algorithm for test sheet generation based on genetic algorithm. Unlike the existing methods that difficulty of each test item is assigned by tutors, in the proposed method, that can be adjusted by the result of the previous tests and the average difficulty of test sheet can be consistently reserved. We propose the new genetic operators to prevent duplications of test items in a test sheet and apply the adjusted difficulty of each test item. The result of simulation shows that difficulty of the test sheet generated by proposed method can be more regular than the random method and the simulated annealing method.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2003.11a
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• pp.104-108
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• 2003

Finite element (FE) model updating is a procedure to minimize the differences between analytical and experimental results, which can be usually posed as an optimization problem. This paper aims to introduce a hybrid optimization algorithm (GA-SA), which consists of a Genetic algorithm (GA) stage and an Adaptive Simulated Annealing (ASA) stage, to FE model updating for a shrunk shaft. A good agreement of the first four natural frequencies has been achieved obtained from GASA based updated model (FEgasa) and experiment. In order to prove the validity of GA-SA, comparisons of natural frequencies obtained from the initial FE model (FEinit), GA based updated model (FEga) and ASA based updated model (FEasa) are carried out. Simultaneously, the FRF comparisons obtained from different FE models and experiment are also shown. It is concluded that the GA, ASA, GA-SA are powerful optimization techniques which can be successfully applied to FE model updating, the natural frequencies and FRF obtained from all the updated models show much better agreement with experiment than that obtained from FEinit model. However, FEgasa is proved to be the most reasonable FE model, and also FEasa model is better than FEga model.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.33 no.2
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• pp.48-55
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• 2010

Reliability has been considered as a one of the major design measures in various industrial and military systems. The main objective is to suggest a mathematical programming model and a hybrid parallel genetic algorithm(HPGA) for the problem that determines the optimal component reliability to maximize the system reliability under cost constraint in this study. Reliability optimization problem has been known as a NP-hard problem and normally formulated as a mixed binary integer programming model. Component structure, reliability, and cost were computed by using HPGA and compared with the results of existing meta-heuristic such as Ant Colony Optimization(ACO), Simulated Annealing(SA), Tabu Search(TS) and Reoptimization Procedure. The global optimal solutions of each problem are obtained by using CPLEX 11.1. The results of suggested algorithm give the same or better solutions than existing algorithms, because the suggested algorithm could paratactically evolved by operating several sub-populations and improving solution through swap and 2-opt processes.

• ETRI Journal
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• v.37 no.4
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• pp.717-726
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• 2015

In this paper, we study an asymmetric two-way relaying network where two source nodes intend to exchange information with the help of multiple relay nodes. A hybrid time-division broadcast relaying scheme with joint relay selection (RS) and power allocation (PA) is proposed to realize energy-efficient transmission. Our scheme is based on the asymmetric level of the two source nodes' target signal-to-noise ratio indexes to minimize the total power consumed by the relay nodes. An optimization model with joint RS and PA is studied here to guarantee hybrid relaying transmissions. Next, with the aid of our proposed intelligent optimization algorithm, which combines a genetic algorithm and a simulated annealing algorithm, the formulated optimization model can be effectively solved. Theoretical analyses and numerical results verify that our proposed hybrid relaying scheme can substantially reduce the total power consumption of relays under a traffic asymmetric scenario; meanwhile, the proposed intelligent optimization algorithm can eventually converge to a better solution.

• Communications for Statistical Applications and Methods
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• v.14 no.1
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• pp.215-227
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• 2007

Variable selection algorithm for Sliced Inverse Regression using penalty function is proposed. We noted SIR models can be expressed as generalized eigenvalue decompositions and incorporated penalty functions on them. We found from small simulation that the HARD penalty function seems to be the best in preserving original directions compared with other well-known penalty functions. Also it turned out to be effective in forcing coefficient estimates zero for irrelevant predictors in regression analysis. Results from illustrative examples of simulated and real data sets will be provided.

• Journal of the Korean Society of Marine Environment & Safety
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• v.5 no.2
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• pp.87-97
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• 1999

This study is concerned with developing a hybrid heuristic algorithm for solving the redundancy optimization problem which is very important in system safety, This study develops a HH(Hybrid Heuristic) method combined with two strategies to alleviate the risks of being trapped at a local optimum. One of them is to construct the populations of the initial solutions randomly. The other is the additional search with SA(Simulated Annealing) method in final step. Computational results indicate that HH performs consistently better than the KY method proposed in Kim[8]. Therefore, the proposed HH is believed to an attractive to other heuristic methods.

• Journal of the Korean Operations Research and Management Science Society
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• v.22 no.4
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• pp.37-50
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• 1997

We consider the channel allocation problem for the earth orbit (LEO) satellite systems. This problm is known to be NP-complete and a couple of heuristic algorithms have been developed. In this paper, we convert the problem into a simpler form through the concept of pattern. And we suggest another algorithm based on Simulated Annealing for this simplified problem. The results of performance comparison show that our method works very well. Simulation results are reported.