• The Transactions of the Korean Institute of Electrical Engineers P
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• v.66 no.3
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• pp.123-128
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• 2017

Electric utilities has been considered the necessity to introduce AM(asset management) of electric power facilities in order to reduce maintenance cost of existing facilities and to maximize profit. In order to make decisions in terms of repairs and replacements for power transformers, not only measuring by counting parts and labor costs, but comprehensive comparison including reliability and cost is needed. Therefore, this study is modeling input cost for power transformer during its entire life and also the life cycle cost (LCC) technique is applied. In particular, this paper presents an application of heuristic harmony search(HS) optimization algorithm to the convergence and the validity of economic life assessment of power transformer from LCC technique. This recently developed HS algorithm is conceptualized using the musical process of searching for a perfect state of harmony. It uses a stochastic random search instead of a gradient search so that derivative information is unnecessary. The effectiveness of the proposed identification method has been demonstrated through an economic life assessment simulation of power transformer using HS optimization algorithm.

• Journal of Advanced Navigation Technology
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• v.19 no.6
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• pp.636-642
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• 2015

This paper presents an application of heuristic harmony search (HS) optimization algorithm for the identification of linear continuous time-delay system from step response. Identification model is first-order plus dead time (FOPDT), which describes a linear monotonic process quite well in most chemical processes and HAVC process and is often sufficient for PID controller tuning. This recently developed HS algorithm is conceptualized using the musical process of searching for a perfect state of harmony. It uses a stochastic random search instead of a gradient search so that derivative information is unnecessary. The effectiveness of the identification method has been demonstrated through a number of simulation examples.

• Journal of Korea Water Resources Association
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• v.53 no.8
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• pp.597-604
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• 2020

Muskingum, a hydrologic channel flood routing, is a method of predicting outflow by using the relationship between inflow, outflow, and storage. As many studies for Muskingum model were suggested, parameters were gradually increased and the calculation process was complicated by many parameters. To solve this problem, an optimization algorithm was applied to the parameter estimation of Muskingum model. This study applied the Advanced Nonlinear Muskingum Model considering continuous flow (ANLMM-L) to Wilson flood data and Sutculer flood data and compared results of the Linear Nonsingum Model incorporating Lateral flow (LMM-L), and Kinematic Wave Model (KWM). The Sum of Squares (SSQ) was used as an index for comparing simulated and observed results. Exponential Bandwidth Harmony Search with Centralized Global Search (EBHS-CGS) was applied to the parameter estimation of ANLMM-L. In Wilson flood data, ANLMM-L showed more accurate results than LMM-L. In the Sutculer flood data, ANLMM-L showed better results than KWM, but SSQ was larger than in the case of Wilson flood data because the flow rate of Sutculer flood data is large. EBHS-CGS could be appplied to be appplicable to various water resources engineering problems as well as Muskingum flood routing in this study.

• Proceedings of the Korean Society of Computer Information Conference
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• 2019.01a
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• pp.29-30
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• 2019

본 논문에서는 다중 컨트롤러가 존재하는 분산 SDN 환경에서 과도한 제어 메시지로 인한 과부하된 컨트롤러의 부하를 줄이기 위하여 이주할 스위치를 K-means 군집화와 Harmony Search(HS)를 기반으로 선정 하는 기법을 제안하였다. 기존에 HS를 이용하여 이주할 스위치를 선택하는 기법이 제시되었으나, 시간 소모에 비하여 정확도가 부족한 단점이 있다. 또한 Harmony Memory(HM) 구축을 위해 메모리 소모 또한 크다. 이를 해결하기 위하여 본 논문에서는 유클리드 거리를 기반으로 하는 K-means 군집화를 이용하여 이주할 스위치를 골라내어 HM의 크기를 줄이고 이주 효율을 향상 시킨다.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.16 no.4
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• pp.203-209
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• 2023

Harmony search(HS) does not use the evaluation of individual harmony when referring to HM when constructing a new harmony, but particle swarm optimization(PSO), on the contrary, uses the evaluation value of individual particles and the evaluation value of the population to find a solution. However, in this study, we tried to improve the performance of the algorithm by finding and identifying similarities between HS and PSO and applying the particle improvement process of PSO to HS. To apply the PSO algorithm, the local best of individual particles and the global best of the swam are required. In this study, the process of HS improving the worst harmony in harmony memory(HM) was viewed as a process very similar to that of PSO. Therefore, the worst harmony of HM was regarded as the local best of a particle, and the best harmony was regarded as the global best of swam. In this way, the performance of the HS was improved by introducing the particle improvement process of the PSO into the HS harmony improvement process. The results of this study were confirmed by comparing examples of optimization values for various functions. As a result, it was found that the suggested HS-PSO was much better than the existing HS in terms of accuracy and consistency.

• Journal of Korea Water Resources Association
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• v.55 no.11
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• pp.903-911
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• 2022

Groundwater, one of the resources for supplying water, fluctuates in water level due to various natural factors. Recently, research has been conducted to predict fluctuations in groundwater levels using Artificial Neural Network (ANN). Previously, among operators in ANN, Gradient Descent (GD)-based Optimizers were used as Optimizer that affect learning. GD-based Optimizers have disadvantages of initial correlation dependence and absence of solution comparison and storage structure. This study developed Gradient Descent combined with Harmony Search (GDHS), a new Optimizer that combined GD and Harmony Search (HS) to improve the shortcomings of GD-based Optimizers. To evaluate the performance of GDHS, groundwater level at Icheon Yullhyeon observation station were learned and predicted using Multi Layer Perceptron (MLP). Mean Squared Error (MSE) and Mean Absolute Error (MAE) were used to compare the performance of MLP using GD and GDHS. Comparing the learning results, GDHS had lower maximum, minimum, average and Standard Deviation (SD) of MSE than GD. Comparing the prediction results, GDHS was evaluated to have a lower error in all of the evaluation index than GD.

• Journal of the Korea Convergence Society
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• v.12 no.11
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• pp.81-90
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• 2021

There are various optimization problems in real world and research continues to solve them. An optimization problem is the problem of finding a combination of parameters that maximizes or minimizes the objective function. Harmony search is a population-based metaheuristic algorithm for solving optimization problems and it is designed to mimic the improvisation of jazz music. Harmony search has been actively applied to optimization problems in various fields such as civil engineering, computer science, energy, medical science, and water quality engineering. Harmony search has a simple working principle and it has the advantage of finding good solutions quickly in constrained optimization problems. Especially there are various application cases showing high accuracy with a low number of iterations by improving the solution through the empirical derivative. In this paper, we explain working principle of Harmony search and classify the leading research in recent 3 years, review them according to category, and suggest future research directions. The research is divided into review by field, algorithmic analysis and theory, and application to real world problems. Application to real world problems is classified according to the purpose of optimization and whether or not they are hybridized with other metaheuristic algorithms.

• International Journal of Highway Engineering
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• v.20 no.3
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• pp.59-64
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• 2018

PURPOSES : We propose a parameter-setting-free harmony-search (PSF-HS) algorithm to determine the individual sound power levels of noise sources in the cases of industrial or road noise environment. METHODS :In terms of using methods, we use PSF-HS algorithm because the optimization parameters cannot be fixed through finding the global minimum. RESULTS:We found that the main advantage of the PSF-HS heuristic algorithm is its ability to find the best global solution of individual sound power levels through a nonlinear complex function, even though the parameters of the original harmony-search (HS) algorithm are not fixed. In an industrial and road environment, high noise exposure is harmful, and can cause nonauditory effects that endanger worker and passenger safety. This study proposes the PSF-HS algorithm for determining the PWL of an individual machine (or vehicle), which is a useful technique for industrial (or road) engineers to identify the dominant noise source in the workplace (or road field testing case). CONCLUSIONS : This study focuses on providing an efficient method to determine sound power levels (PWLs) and the dominant noise source while multiple machines (or vehicles) are operating, for comparison with the results of previous research. This paper can extend the state-of-the-art in a heuristic search algorithm to determine the individual PWLs of machines as well as loud machines (or vehicles), based on the parameter-setting-free harmony-search (PSF-HS) algorithm. This algorithm can be applied into determining the dominant noise sources of several vehicles in the cases of road cross sections and congested housing complex.

• Journal of Korean Society of Steel Construction
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• v.27 no.2
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• pp.131-142
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• 2015

In this paper, we present an optimization of truss structures subjected to stress, buckling, and natural frequency constraints. The main objective of the present study is to propose an efficient HA-SA algorithm for solving the truss optimization subject to multiple constraints. The procedure of hybrid HA-SA is a search method which a design values in harmony memory of harmony search are used as an initial value designs in simulated annealing search method. The efficient optimization of HA-SA is illustrated through several optimization examples. The examples of truss structures are used 10-Bar truss, 52-Bar truss (Dome), and 72-Bar truss for natural frequency constraints, and used 18-Bar truss and 47-Bar (Tower) truss for stress and buckling constraints. The optimum results are compared to those of different techniques. The numerical results are demonstrated the advantages of the HA-SA algorithm in truss optimization with multiple constraints.

• Structural Engineering and Mechanics
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• v.45 no.2
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• pp.183-199
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• 2013

Current methods of analysis of trusses depend on matrix formulations based on equilibrium equations which are in fact derived from energy principles, and compatibility conditions. Recently it has been shown that the minimum energy principle, by itself, in its pure and unmodified form, can well be exploited to analyze structures when coupled with an optimization algorithm, specifically with a meta-heuristic algorithm. The resulting technique that can be called Total Potential Optimization using Meta-heuristic Algorithms (TPO/MA) has already been applied to analyses of linear and nonlinear plane trusses successfully as coupled with simulated annealing and local search algorithms. In this study the technique is applied to both 2-dimensional and 3-dimensional trusses emphasizing robustness, reliability and accuracy. The trials have shown that the technique is robust in two senses: all runs result in answers, and all answers are acceptable as to the reliability and accuracy within the prescribed limits. It has also been shown that Harmony Search presents itself as an appropriate algorithm for the purpose.