• Proceedings of the Korean Society of Computer Information Conference
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• 2023.07a
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• pp.689-691
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• 2023

본 논문에서는 시작 노드와 목적지 노드 까지의 최단경로를 중점으로 가장 인접한 경유지를 선택하고 경유지가 다수일 경우 하모니서치 알고리즘을 통해 최적의 경로를 탐색하는 기법을 제안한다. 본 논문의 기법은 기존의 사용자의 현 위치에서 인접한 경유지를 추천해주는 시스템과 달리 목적지까지의 경로 전체에서 가장 인접한경유지를 선택해준다는 차별점이 있다. 그렇게 선택된 경유지들은 하모니서치 알고리즘을 사용하여 최적의 경로를 제안한다. 본 논문의 기법은 최단경로의 이탈을 최소화한 경유지 선택으로 거리, 시간, 비용적 측면에서 효율적이며 길을 헤매서 생기는 불필요한 지연을 최소화할 수 있다. 자율주행, 비행기, 선박, 드론 등 최적 경로 이탈시 큰 손실을 안기거나 위험한 시스템에 적용시 큰 효과를 거둘 수 있다.

• Journal of the Korean Institute of Intelligent Systems
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• v.27 no.1
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• pp.79-87
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• 2017

The microphysical processes of the numerical weather prediction (NWP) model cover the following : fall speed, accretion, autoconversion, droplet size distribution, etc. However, the microphysical processes and parameters have a significant degree of uncertainty. Parameter estimation was generally used to reduce errors in NWP models associated with uncertainty. In this study, the micro- genetic algorithm and harmony search algorithm were used as an optimization algorithm for estimating parameters. And we estimate parameters of microphysics for the Unified model in the case of precipitation in Korea. The differences which occurred during the optimization process were due to different characteristics of the two algorithms. The micro-genetic algorithm converged to about 1.033 after 440 times. The harmony search algorithm converged to about 1.031 after 60 times. It shows that the harmony search algorithm estimated optimal parameters more quickly than the micro-genetic algorithm. Therefore, if you need to search for the optimal parameter within a faster time in the NWP model optimization problem with large calculation cost, the harmony search algorithm is more suitable.

• 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.

• Proceedings of the Korea Water Resources Association Conference
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• 2016.05a
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• pp.521-521
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• 2016

상수도 관망은 수원에서 수요절점까지 물을 안정적으로 공급하는 것을 목표로 한다. 상수도 관망의 최적설계는 수리학적 제한조건 (절점의 수압, 관로의 유속)을 만족하는 범위에서 비용을 최소화하는 설계안을 얻는 것으로 Savic and Walters (1997)는 유전 알고리즘 (Genetic Algorithms, Holland 1975)을 적용한 상수도 관망 설계 프로그램인 GANET를 제안하였고, Maier et al. (1996)은 개미군집알고리즘 (Ant Colony Optimization Algorithm, Dorigo et al. 1996)을 상수도 관망 최적설계에 적용한 후 그 결과가 유전 알고리즘에 비해 우수함을 증명하는 등 상수도 관망 최적설계에 관한 연구가 활발히 진행되어 오고 있다. 유전알고리즘은 선택, 교차, 돌연변이의 반복계산 과정을 통하여 최적해를 찾는 최적화 기법이다. 이 과정에서 결정변수는 유전자 (Gene)의 집합으로 표현되며, 염색체 (Chromosome) 내에서 근접한 유전 인자들은 일종의 Building Block을 형성하게 된다. Building Block은 좋은 해를 갖는 유전 인자를 높은 확률로 보관하여 지역해에 빠질 가능성을 줄이는 반면, 유전형 (Genotype)이 표현형 (Phenotype)을 충분히 모방하여 표현하지 못한 경우 오히려 최적해의 탐색을 방해할 수 있다는 한계점을 갖는다. 유전 알고리즘을 상수도 관망 최적설계에 적용하였을 때에도 이 한계점은 여실히 드러난다. 관로의 관경을 결정변수로 설정한 후 유전형으로 표현하였을 때, 관망도 상에서 근접하지 않은 두 관로가 염색체 내에서 연속으로 나열된다면 두 관로 간의 연관성이 실제보다 크게 고려되기 때문이다. 한편, 하모니써치 (Harmony Search, Geem et al. 2001) 알고리즘은 즉흥 연주 (Improvisation)를 통해 최상의 화음을 만들어내는 현상으로부터 착안하여 만들어진 최적화기법으로 연산 기법은 무작위선택, 기억회상, 피치조정 등으로 구성되어 있으며, 결정변수에 해당하는 연주자가 독립적으로 행동하며 해를 탐색한다는 점에서 유전알고리즘과 큰 차이를 갖는다. 본 연구에서는 유전알고리즘의 Building Block에 의해 발생하는 오류를 개선하고자, 상수도 관망 최적설계 연구에 많이 사용되는 Hanoi 관망 (Fujiwara and Khang 1990) 관로의 정렬 순서를 여러 가지 기준으로 설정하여 관망데이터를 구축한 후 하모니써치와 유전 알고리즘을 적용하여 최적화를 수행하였고 그 결과를 비교하였다. 그 결과 유전 알고리즘과 달리 하모니써치 알고리즘의 경우, 관로의 나열 순서와 상관없이 우수한 최적해 탐색 결과를 보이는 것을 확인할 수 있었다.

• Journal of the Korea Society for Simulation
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• v.29 no.4
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• pp.95-109
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• 2020

In this paper, an optimal manufacturing system design problem in an automotive body assembly lines is introduced when various costs such as equipment investment costs are considered. Meta-model methodology based on simulation results has been used for estimating the performances of the system such as production rate and work-in-process levels. The objective function is minimizing total cost which satisfies the target production rate. The investment costs such as robots, buffers, transportation equipment, and the inventory holding cost of work-in-process have been included in the objective function. Harmony search method has been used for optimization.

• Journal of the Architectural Institute of Korea Structure & Construction
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• v.33 no.12
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• pp.53-62
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• 2017

Many gradient-based mathematical methods have been developed and are in use for structural size optimization problems, in which the cross-sectional areas or sizing variables are usually assumed to be continuous. In most practical structural engineering design problems, however, the design variables are discrete. The main objective of this paper is to propose an efficient optimization method for structures with discrete-sized variables based on the harmony search (HS) meta-heuristic algorithm that is derived using penalty function. The recently developed HS algorithm was 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. In this paper, a discrete search strategy using the HS algorithm with a static penalty function is presented in detail and its applicability using several standard truss examples is discussed. The numerical results reveal that the HS algorithm with the static penalty function proposed in this study is a powerful search and design optimization technique for structures with discrete-sized members.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.1
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• pp.1-11
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• 2018

In various engineering fields, determining the appropriate parameter set is a cumbersome and difficult task when solving optimization problems. Despite the appropriate parameter setting through parameter sensitivity analysis, there are limits to evaluating whether the parameters are appropriate for all optimization problems. For this reason, kinds of a Self-adaptive Harmony searches have been developed to solve various engineering problems by the appropriate setting of algorithm's own parameters according to the problem. In this study, various types of Self-adaptive Harmony searches were investigated and the characteristics of optimization were categorized. Six algorithms with a differentiation of optimization process were applied and compared with not only the mathematical optimization problem, but also the engineering problem, which has been applied widely in the algorithm performance comparisons. The performance of each algorithm was compared, and the statistical performance indicators were used to evaluate the application results quantitatively.