• Proceedings of the Korea Water Resources Association Conference
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• 2019.05a
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• pp.388-388
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• 2019

하도 홍수추적의 방법은 크게 수리학적 방법과 수문학적 방법으로 구분할 수 있다. 수리학적 홍수추적 방법은 정확하지만 대량의 자료가 필요하고 시간이 오래 걸린다. 이와 반대로 수문학적 홍수추적 방법은 정확성은 떨어지지만 소량의 자료만 있으면 되고 시간이 적게 걸린다. 여러 수문학적 홍수추적에 관한 연구들이 있으며 대표적으로 Muskingum 방법이 있다. Muskingum 방법 중 Linear Muskingum Model(LMM)은 방정식의 구조적 한계 때문에 정확한 홍수추적이 어려웠고, 이를 개선하기위하여 Nonlinear Muskingum Model(NLMM), Nonlinear Muskingum Model Incorporation Lateral Flow(NLMM-L) 및 Advanced Nonlinear Muskingum Model Incorporating Lateral Flow(ANLMM-L)이 제안되었다. 본 연구는 수문학적 홍수추적 중 Muskingum 방법의 결과 차이가 어떤 요인으로 인해 발생하는지 검토하였다. 최적화 알고리즘으로 화음탐색법(Harmony Search, HS)을 사용하였으며 LMM, NLMM, NLMM-L 및 ANLMM-L의 매개변수를 산정하였다. 각 방법에 적용 시 HS의 매개변수에 변화를 주어 민감도 분석을 실시하였으며, 분석을 위한 홍수자료는 The Willson Flood data (1947)를 선택하였다. 오차비교방법은 Sum of Squares(SSQ), Root Mean Square Errors(RMSE), Nash-Sutcliffe Efficiency(NSE)를 비교하였다. 비교 결과 알고리즘의 성능에 의한 차이보다 홍수추적 방법의 차이가 더 영향이 큰 것으로 나타났다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.10
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• pp.2701-2717
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• 2023

In this paper, a hybrid fuzzy-based method is suggested for determining India's best system for power generation. This suggested approach was created using a fuzzy-based combination of the Giza Pyramids Construction (GPC) and Recalling-Enhanced Recurrent Neural Network (RERNN). GPC is a meta-heuristic algorithm that deals with solutions for many groups of problems, whereas RERNN has selective memory properties. The evaluation of the current load requirements and production profile information system is the main objective of the suggested method. The Central Electricity Authority database, the Indian National Load Dispatch Centre, regional load dispatching centers, and annual reports of India were some of the sources used to compile the data regarding profiles of electricity loads, capacity factors, power plant generation, and transmission limits. The RERNN approach makes advantage of the ability to analyze the ideal power generation from energy data, however the optimization of RERNN factor necessitates the employment of a GPC technique. The proposed method was tested using MATLAB, and the findings indicate that it is effective in terms of accuracy, feasibility, and computing efficiency. The suggested hybrid system outperformed conventional models, achieving the top result of 93% accuracy with a shorter computation time of 6814 seconds.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.4
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• pp.527-531
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• 2012

In this paper, a novel DPSO-QI (Distributed PSO with quantum-infusion mechanism) algorithm improving one of the fatal defect, the so-called premature convergence, that degrades the performance of the conventional PSO algorithms is proposed. The proposed scheme has the following two distinguished features. First, a concept of neighborhood of each particle is introduced, which divides the whole swarm into several small groups with an appropriate size. Such a strategy restricts the information exchange between particles to be done only in each small group. It thus results in the improvement of particles' diversity and further minimization of a probability of occurring the premature convergence phenomena. Second, a quantum-infusion (QI) mechanism based on the quantum mechanics is introduced to generate a meaningful offspring in each small group. This offspring in our PSO mechanism improves the ability to explore a wider area precisely compared to the conventional one, so that the degree of precision of the algorithm is improved. Finally, some numerical results are compared with those of the conventional researches, which clearly demonstrates the effectiveness and reliability of the proposed DPSO-QI algorithm.

• Steel and Composite Structures
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• v.34 no.4
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• pp.467-485
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• 2020

Genetic Algorithm (GA) is a meta-heuristic algorithm which is capable of providing robust solutions for optimal design of structural components, particularly those one needs considering many design requirements. Hence, it has been successfully used by engineers in the typology optimization of structural members. As a novel approach, this study employs GA in order for conducting a case study with high constraints on the optimum mechanical properties of reinforced concrete (RC) beams under different load combinations. Accordingly, unified optimum sections through a computer program are adopted to solve the continuous beams problem. Genetic Algorithms proved in finding the optimum resolution smoothly and flawlessly particularly in case of handling many complicated constraints like a continuous beam subjected to different loads as moments shear - torsion regarding the curbs of design codes.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.5
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• pp.2724-2731
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• 2014

As the future national defense plan of government focus on advanced weapon system, military maintenance facility becomes more important. However, military maintenance facility has been managed by director's experience and simple mathematical calculation until now. Thus, the optimization for the management of military maintenance facility is suggested by more scientistic and logical methods in this study. The study follows the procedure below. First, simulation is designed according to the analysis of military maintenance facility. Second, independent variable and dependent variable are defined for optimization. Independent Variable includes the number of maintenance machine, transportation machine, worker in the details of military maintenance facility operation, and dependent variable involves total maintenance time affected by independent variable. Third, warmup analysis is performed to get warmup period, based on the simulation model. Fourth, the optimal combination is computed with evolution strategy, meta-heuristic, to enhance military maintenance management. By the optimal combination, the management of military maintenance facility can gain the biggest effect against the limited cost. In the future, the multipurpose study, to analyze the military maintenance facility covering various weapon system equipments, will be performed.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.22 no.5
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• pp.45-56
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• 2019

Urban green areas are generally composed of relatively small and fragmented patches, but it is a critical factor for the quality of an urban environment. They have positive effects such as increasing green connectivity, reducing runoff, and mitigating urban heat. But, there is a lack of urban greening plans that consider the comprehensive effects of green space in real urban areas. To fill this gap in this literature, this study identifies a planning model that determines the optimal locations for maximizing green areas' multiple effects(e.g., heat mitigation and enhancement of connectivity) by using unused lots. This model also considers minimizing costs using meta-heuristic optimization algorithms. As a results, we finds 50 optimal plans that considers two effects within the limited cost in Nowon-gu. The optimal plans show the trade-off effect between connectivity, heat mitigation and cost. They also show the critical unused land lots for urban greening that are commonly selected in various plans. These optimal plans can effectively inform quantitative effectiveness of green space and their trade-off. We expect that our model will contribute to the improvement of green planning processes in reality.

• Geomechanics and Engineering
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• v.31 no.2
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• pp.129-147
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• 2022

The properties of soil are naturally highly variable and thus, to ensure proper safety and reliability, we need to test a large number of samples across the length and depth. In pile foundations, conducting field tests are highly expensive and the traditional empirical relations too have been proven to be poor in performance. The study proposes a state-of-art Particle Swarm Optimization (PSO) hybridized Artificial Neural Network (ANN), Extreme Learning Machine (ELM) and Adaptive Neuro Fuzzy Inference System (ANFIS); and comparative analysis of metaheuristic models (ANN-PSO, ELM-PSO, ANFIS-PSO) for prediction of bearing capacity of pile foundation trained and tested on dataset of nearly 300 dynamic pile tests from the literature. A novel ensemble model of three hybrid models is constructed to combine and enhance the predictions of the individual models effectively. The authenticity of the dataset is confirmed using descriptive statistics, correlation matrix and sensitivity analysis. Ram weight and diameter of pile are found to be most influential input parameter. The comparative analysis reveals that ANFIS-PSO is the best performing model in testing phase (R² = 0.85, RMSE = 0.01) while ELM-PSO performs best in training phase (R² = 0.88, RMSE = 0.08); while the ensemble provided overall best performance based on the rank score. The performance of ANN-PSO is least satisfactory compared to the other two models. The findings were confirmed using Taylor diagram, error matrix and uncertainty analysis. Based on the results ELM-PSO and ANFIS-PSO is proposed to be used for the prediction of bearing capacity of piles and ensemble learning method of joining the outputs of individual models should be encouraged. The study possesses the potential to assist geotechnical engineers in the design phase of civil engineering projects.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.47 no.12
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• pp.856-864
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• 2019

The weapon-target assignment (WTA) problem is a matter of effectively allocating weapons to a number of threats. The WTA in a rapidly changing dynamic environment of engagement must take into account both of properties of the threat and the weapon and the effect of the previous decision. We propose a method of applying the Greedy Randomized Adaptive Search Procedure (GRASP) algorithm, a kind of meta-heuristic method, to derive optimal solution for a dynamic WTA problem. Firstly, we define a dynamic WTA problem and formulate a mathematical model for applying the algorithm. For the purpose of the assignment strategy, the objective function is defined and time-varying constraints are considered. The dynamic WTA problem is then solved by applying the GRASP algorithm. The optimal solution characteristics of the formalized dynamic WTA problem are analyzed through the simulation, and the algorithm performance is verified via the Monte-Carlo simulation.

• Journal of the Korea Society of Computer and Information
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• v.13 no.3
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• pp.147-152
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• 2008

Ant System(AS) is new meta heuristic for hard combinatorial optimization problem. It is a population based approach that uses exploitation of positive feedback as well as greedy search. It was first proposed for tackling the well known Traveling Salesman Problem. In this paper, AS is applied to the Multicast Routing Problem. Multicast Routing is modeled as the NP-complete Steiner tree problem. This is the shortest path from source node to all destination nodes. We proposed new AS to resolve this problem. The proposed method selects the neighborhood node to consider all costs of the edge and the next node in state transition rule. Also, The edges which are selected elite agents are updated to additional pheromone. Simulation results of our proposed method show fast convergence and give lower total cost than original AS and $AS_{elite}$.

• Journal of Electrical Engineering and Technology
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• v.13 no.3
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• pp.1043-1051
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• 2018

The coordination of directional overcurrent relay (DOCR) is employed in this work, considering gray wolf optimizer (GWO), a recently designed optimizer that employs the hunting and leadership attitude of gray wolves for searching a global optimum. In power system protection coordination problem, the objective function to be optimized is the sum of operating time of all the main relays. The coordination of directional overcurrent relays is formulated as a linear programming problem. The proposed optimization technique aims to minimize the time dial settings (TDS) of the relays. The calculation of the Time Dial Setting (TDS) setting of the relays is the core of the coordination study. In this article two case studies of IEEE 6-bus system and IEEE 30-bus system are utilized to see the efficiency of this algorithm and the results had been compared with the other algorithms available in the reference and it was observed that the proposed scheme is quite competent for dealing with such problems. From analyzing the obtained results, it has been found that the GWO approach provides the most globally optimum solution at a faster convergence speed. GWO has achieved a lot of relaxation due to its easy implementation, modesty and robustness. MATLAB computer programming has been applied to see the effectiveness of this algorithm.