Earticle

In digital forensics, searching for specific patterns in binary data is a crucial task almost always performed following data acquisition. As the volume of digital data surges and the capacity of storage media expands, there is an increasing demand for search techniques that ensure accuracy, completeness, and speed. In this paper, we explore methods to enhance search efficiency through multi-threading and memory-mapped files, while also improving the completeness of searches in corrupted data by adjusting the search resumption position after a match is found. By employing multi-threading, the parallel utilization of multiple processor cores increases the computational speed. Additionally, while some threads occupy the processor, others can access the storage medium, reducing idle times for the storage medium, and thereby maximizing the utility of the storage medium's read speed. Memory-mapped files enable direct access to the operating system’s memory region associated with the storage medium, minimizing the caching and copying stages. This reduces computational overhead and memory load compared to standard file input/output operations. In 64-bit systems, memory-mapped files allow access to files larger than physical memory as a contiguous virtual memory space of up to 2⁶⁴ bytes (16,777,216 terabytes), enabling memory-based implementations of search algorithms to be applied to large files without modification. Experimental results show that the proposed approach significantly enhances both the speed and accuracy of searches in large files, outperforming a method that relies on single-threading and standard file input/output.

The large amplitude variations of OFDM signals generate in-band distortion and out-of-band radiation. In recent years, cubic metric (CM) has been verified as a more accurate metric to measure the amplitude variations. In this paper, the PTS technique is used to decrease the CM of OFDM signals. To overcome the search complexity of an exhaustive search based PTS technique, we introduce an improved backtracking search (IBS) optimization algorithm. Simulations are conducted to show the advantages of the proposed IBS based PTS approach compared with the conventional OFDM, and several state-of-the-art methods in terms of search complexity and CM reduction performance.

Harmony search algorithm is an emerging meta-heuristic optimization algorithm, which is inspired by the music improvisation process and can solve different optimization problems. In order to further improve the performance of the algorithm, this paper proposes an improved harmony search algorithm. Key parameters including harmonic memory consideration (HMCR), pitch adjustment rate (PAR), and bandwidth (BW) are optimized as the number of iterations increases. Meanwhile, referring to the genetic algorithm, an improved method to generate a new crossover solutions rather than the traditional mechanism of improvisation. Four complex function optimization and pressure vessel optimization problems were simulated using the optimization algorithm of standard harmony search algorithm, improved harmony search algorithm and exploratory harmony search algorithm. The simulation results show that the algorithm improves the ability to find global search and evolutionary speed. Optimization effect simulation results are satisfactory.

Harmony Search(HS) 알고리즘은 음악 즉흥 연주 프로세스에서 영감을 받은 메타 휴리스틱 최적화 알고리즘으로 다양한 최적화 문제를 해결하는 데 성공적으로 적용되어 왔다. 본 논문에서는 HS의 성능을 더욱 향상시키기 위해 FSH(Fast Harmony Search) 알고리즘을 제안하였다. 이를 위해 본 논문에서는 HM을 이용하여 목적 변수의 경곗값을 새롭게 정의하여 독립적인 두 개의 화음개선과정을 하나로 통합하는 방법을 제안하였다. 그 결과 알고리즘의 처리 시간이 단축되고 대역폭의 명시적인 결정이 더이상 필요하지 않게 되었다. 또한, 무작위 선택의 활용능력이 향상되었다. 수치적 예시 결과는 제안된 알고리즘이 기존의 HS에 비해 더 나은 해를 찾을 수 있으며 속도 또한 빠르다는 것을 보여준다.

Harmony Search(HS) algorithm is an emerging meta-heuristic optimization algorithm, which is inspired by the music improvisation process and has been successfully applied to solve different optimization problems. In order to further improve the performance of HS, this paper proposes a new method which is called Fast Harmony Search(FSH) algorithm. For the purpose, this paper suggest a method to unify two independent improvisation processes by newly defining the boundary value of a object variable using HM. As the result, the process time of the algorithm is shorten and explicit decision of bandwidth is no more needed. Furthermore, exploitative power of random selection is improved. The numerical results reveal that the proposed algorithm can find better solutions and is faster when compared to the conventional HS.

화음탐색법은 2001년 고려대학교 수자원연구실에서 개발한 최적화 알고리즘으로 재즈의 즉흥연주에서 반복적인 연습을 거듭할 수 록 좋은 화음이 만들어지는 현상에 착안하였다. 화음탐색법은 처음 소개된 논문이 Google Scholar 기준 약 3,600여 회(2018년 1월 11일 기준) 인용될 만큼 유전자알고리즘과 견줄만한 세계적인 최적화 알고리즘이 되었고 비단 수자원공학 및 토목공학 뿐 만 아니라 공학 전 분야, 의학, 경영학, 인문학 등 다양한 분야에 적용되고 있다. 본 논문은 화음탐색법을 포함한 최적화 알고리즘이 수자원공학의 다양한 분야에서 널리 적용되기를 바라며 작성된 화음탐색법 총설논문(Review Article)이다. 따라서, 본 논문에서는 먼저 화음탐색법을 간략히 소개하고 적용분야 및 분야별 적용 빈도를 살펴본다. 또한 화음탐색법의 세계화 현황을 관련 학회의 성장과 관련 연구프로젝트의 동향 정리를 통해 알아본다. 마지막으로 국내 수자원공학 분야 연구에 적용된 최적화 알고리즘 현황을 살펴보고 활용의 증대를 위한 몇 가지 제안사항을 전달하며 마무리한다.

Harmony search algorithm (HSA), developed by Hydrosystem lab. in Korea University in 2001, was a new meta-heuristic optimization algorithm inspired by the iterative improvision process of Jazz music players where the best harmony is eventually produced. HSA is now one of the most well-known meta-heuristic algorithms (as proven by its cited number of the first published paper more than 3,600 times as of January 11th 2018 based on Google Scholar citation) and has been applied to diverse research domains such as not only water resources and civil engineering but also in medical science, business, and humanities. This paper is a review article written with the wish for wider application of HSA and other optimization algorithms, especially in the domain of water resources engineering. Therefore, this paper first briefly introduces the mechanism and operators of HSA and then reviews its application area and citation frequency per research domain. In addition, recent globalization of HSA will be investigated and summarized by checking the current status of related international conferences and on-going research projects. After reviewing previous domestic papers with optimization algorithms specifically published in the water resources domain, this paper is finalized by delivering some suggestions to encourage the application of optimization algorithms including HSA.

헬리컬 파일은 한 개 이상의 나선형 원판을 중공형 강재 축에 부착한 후 지반에 회전 관입시켜 지지력을 발현하게 하는 말뚝기초의 한 종류로서 별도로 천공작업 및 그라우팅을 필요로 하지 않고, 시공의 편의성과 말뚝 항타 등에 의한 소음이 없다는 장점이 있어 그 이용이 점차 증가되고 있는 추세이다. 본 연구에서는 Cylindrical shear method로 산정된 헬리컬 파일의 요구 지지력을 최소의 자재비용과 최적의 헬리컬 파일의 형상으로 발현할 수 있도록 하모니서치 알고리즘(Harmony search algorithm)을 적용하여 최적화된 설계법을 제안 하였다. 또한 헬리컬 파일의 최적 설계 결과를 2009 International building code (이하, 2009 IBC)를 참고하여 규격화시킨 설계 결과와 비교 검토하여 두 설계법의 경제성을 분석하였다. 본 논문의 재료비 최소화를 위한 헬리컬 파일 형상의 최적 설계 기법은 헬리컬 파일의 경제성을 향상하여 헬리컬 파일 시장 활성화에 기여할 수 있다.

The helical pile is a manufactured steel pile consisting of one or more helix-shaped bearing plates affixed to a central shaft. This pile is installed by rotating the shaft into the ground to support structural loads. Advantages of the helical pile are no need for boring or grout process, and ability to install a pile foundation with relatively light devices. In this study, an optimized design method for helical piles is proposed to minimize the material cost with consideration of the load bearing capacity obtained by the cylindrical shear method. The harmony search meta-heuristic algorithm was adopted for optimization process. The optimized design was verified by comparing with the 2009 International building code. It is noted that the optimization for the configuration of helical piles along with material cost proves to be an out-performed tool in designing helical pile foundation with economic feasibility.

교차로에서 발생하는 지체나 정체는 인접한 도로에 영향을 미치며, 이는 도시 전체의 교통 혼잡으로 이어질 수 있다. 따라서 교차로는 단순한 도로의 연결점이 아니라, 교통 시스템 전반을 제어하는 핵심 지점이라 할 수 있다. 이러한 이유로 교차로의 혼잡을 최소화하는 일은 교통공학자에게 주어진 중요한 과제 중 하나이다. 최근에는 교차로 성능 향상을 위한 방안으로 인공지능(AI) 알고리즘에 관한 관심이 높아지면서, 지능형 교통 신호 제어에 관한 연구가 활발히 진행되고 있다. 특히 유전 알고리즘(GA)과 입자 군집 최적화(PSO)와 같은 AI 기반 신호 제어 모델을 활용하여, 주행 시간, 지연 시간, 정지 횟수를 최소화하거나 교차로 용량을 극대화하는 다목적 최적화 기법이 많이 연구되고 있다. 이에 본 연구에서는 CO₂ 배출량을 다목적함수로 포함한 하이브리드 Tabu search-Ant colony 알고리즘을 제안한다. 제안된 알고리즘은 말레이시아 알로스타(Alor Setar)의 신호화 교차로에 적용되었으며, SIDRA 분석을 통해 성능을 평가하였다. 그 결과, 최적화된 116초의 신호주기는 대기열 길이 20 %, CO₂ 배출량 18 %, 정지 횟수 25 % 감소와 함께 교차로 용량은 15 % 향상되는 것으로 나타났다. 이러한 결과는 제안된 알고리즘이 지속가능하면서도 효율적인 교통 신호 제어에 효과적임을 보여주는 실증적 근거라 할 수 있다.

Congestion or delays occurring at intersections can significantly impact adjacent roadways, potentially leading to widespread urban traffic congestion. Accordingly, intersections should not be viewed merely as points of connection between roads, but rather as critical control nodes within the broader transportation system. Minimizing congestion at intersections is thus a major challenge for transportation engineers. In recent years, growing interest in artificial intelligence(AI) algorithms has led to increased research on intelligent traffic signal control systems aimed at enhancing intersection performance. Many studies have focused on multi-objective optimization models employing AI-based algorithms such as Genetic Algorithms(GA) and Particle Swarm Optimization(PSO), targeting reductions in travel time, delays, and the number of stops, or improvements in intersection capacity. This study proposes a hybrid Tabu Search-Ant Colony Optimization algorithm that incorporates CO₂ emissions as a key objective within a multi-objective framework. The proposed algorithm was applied to a signalized intersection in Alor Setar, Malaysia, and its performance was evaluated using SIDRA analysis. The optimized signal cycle length of 116 seconds resulted in a 20 % reduction in queue length, an 18 % reduction in CO₂ emissions, a 25 % decrease in the number of stops, and a 15 % improvement in intersection capacity. These findings provide empirical evidence that the proposed algorithm is effective in achieving sustainable and efficient traffic signal control.

Electronic circuit optimization design is important research field in engineering applications. Circuit scale and the time of evaluate the circuit are the challenge problems for circuit optimization design algorithm and the traditional optimization algorithms cannot solve the two problems very well. Cultural Algorithms are a class of computational models derived from observing the cultural evolution process in nature. Aiming at the disadvantages of basic Cultural Algorithms like being trapped easily into a local optimum, this paper improves the basic Cultural Algorithms and proposes the improved cultural algorithm (ICA) to solve the overcomes of the basic Cultural Algorithms. The new algorithm keeps not only the fast convergence speed characteristic of basic Cultural Algorithms, but effectively improves the capability of global searching as well. For the case studies, the new algorithm means has proved to be efficient and the experiment results show that the new means have got the better results.

Estimating the phase boundary in two-phase flow is crucial for designing and optimizing industrial processes. Electrical impedance tomography (EIT) is a promising technique for imaging phase distribution in such flows. This paper proposes using a cuckoo search (CS) optimization algorithm to estimate the phase boundary with EIT. The boundary is parameterized using the Fourier series, and the coefficients are determined by the CS algorithm. The CS algorithm iteratively seeks the phase boundary configuration by minimizing a cost function. Computer simulations and phantom experiments demonstrate the effectiveness of this method in estimating phase boundaries in two-phase flow.

Many 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. This paper proposes an efficient optimization method for structures with discrete-sized variables based on the harmony search (HS) meta-heuristic algorithm. 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 is presented in detail and its effectiveness and robustness, as compared to current discrete optimization methods, are demonstrated through a standard truss example. The numerical results reveal that the proposed method is a powerful search and design optimization tool for structures with discrete-sized members, and may yield better solutions than those obtained using current method.

This paper considers the use of the Partial Transmit Sequence (PTS) technique to reduce the Peak-to-Average Power Ratio (PAPR) of an Orthogonal Frequency Division Multiplexing signal in wireless communication systems. Search complexity is very high in the traditional PTS scheme because it involves an extensive random search over all combinations of allowed phase vectors, and it increases exponentially with the number of phase vectors. In this paper, a suboptimal metaheuristic algorithm for phase optimization based on an improved harmony search (IHS) is applied to explore the optimal combination of phase vectors that provides improved performance compared with existing evolutionary algorithms such as the harmony search algorithm and firefly algorithm. IHS enhances the accuracy and convergence rate of the conventional algorithms with very few parameters to adjust. Simulation results show that an improved harmony search-based PTS algorithm can achieve a significant reduction in PAPR using a simple network structure compared with conventional algorithms.

A pallet, typically a form of tertiary packaging, is a flat x'structure used as a base for the unitization of goods in the supply chain. Standard pallets, such as T-11 and T-12, are used throughout the logistics industry to reduce the cost and enhance the efficiency of transportation. However, it is impossible to handle special cargo using a standard pallet due to its size and weight, so customized pallets have been developed and are now in use. This study suggests a pallet size optimization method to calculate the optimal pallet size, which minimizes the loss of space on a pallet. The main input features are the specifications and the storage quantity of each cargo, and an optimization method that modified the Neighborhood Search Algorithm calculates the optimal pallet size. A comparative analysis was conducted through simulation to verify the optimality of the developed algorithm.

J. Kennedy and R. Eberhart first introduced the concept called as Particle Swarm Optimization (PSO). They applied it to optimize continuous nonlinear functions and demonstrated the effectiveness of the algorithm. Since then a considerable number of researchers have attempted to apply this concept to a variety of optimization problems and obtained reasonable results. In PSO, individuals communicate and exchange simple information with each other. The information among individuals is communicated in the swarm and the information between individuals and their swarm is also shared. Finally, the swarm approaches the optimal behavior. It is reported that reasonable approximate solutions of various types of test functions are obtained by employing PSO. However, if more precise solutions are required, additional algorithms and/or hybrid algorithms would be necessary. For example, the heading vector of the swarm can be slightly adjusted under some conditions. In this paper, we propose a hybrid algorithm to obtain more precise solutions. In the algorithm, when a better solution in the swarm is found, the neighborhood of a certain distance from the solution is searched. Then, the algorithm returns to the original PSO search. By this hybrid method, we can obtain considerably better solutions in less iterations than by the standard PSO method.

One of the important parts of the in-core fuel management is loading pattern optimization (LPO). The loading pattern optimization as a reasonable design of the in-core fuel management can improve both economic and safe aspects of the nuclear reactor. This work proposes the hybrid of fuzzy logic controller with harmony search algorithm (HS) for loading pattern optimization in a pressurized water reactor. The music improvisation process to find a pleasing harmony is inspiring the harmony search algorithm. In this work, the adjustment of the harmony search algorithm parameters such as the bandwidth and the pitch adjustment rate are increasing performance of the proposed algorithm which is done through a fuzzy logic controller. Hence, membership functions and fuzzy rules are designed to improve the performance of the HS algorithm and achieve optimal results. The objective of the method is finding an optimum core arrangement according to safety and economic aspects such as reduction of power peaking factor (PPF) and increase of effective multiplication factor (Keff). The proposed approach effectiveness has been tried in two cases, Michalewicz's bivariate function problem and NEACRP LWR core. The results show that by using fuzzy harmony search algorithm the value of the fitness function is improved by 15.35%. Finally, with regard to the new solutions proposed in this research it could be used as a trustworthy method for other optimization issues of engineering field.

An improved A-star algorithm is proposed in this paper for path planning in the radioactive environment of nuclear facilities. The heuristic function is defined as the estimated dose from the current node to the end point. A dynamic weighting scheme is introduced to balance the actual cost and the estimated cost, and it is applied to the actual cost function. This modification improves the efficiency of path search while maintaining a low cumulative dose for staff. The effect of the number of search neighborhoods (3-8) on path planning is then studied. The results show that the cumulative dose of the modified algorithm is comparable to the traditional A-star algorithm, but the average execution point is reduced by 53.06 % and the computation time is shortened by 24.23 %. Compared with the Probabilistic Roadmap Method (PRM), PRM has the shortest computation time due to its random sampling characteristics, but the cumulative dose and path length are higher than the two A-star algorithms. For models of varying complexity, optimizing the number of search neighborhoods further enhances search efficiency without affecting path planning results. This improvement significantly enhances the application efficiency of the A-star algorithm in the radioactive environment of nuclear facilities, providing a more effective solution for path planning in complex radioactive environments.

무선 센서 네트워크에서 해양 수색 및 구조를 목적으로 노드의 소모 에너지를 최소화하기 위한 최적화 알고리즘을 제안한다. 해양 환경에서 수색 및 구조작업은 감시하는 측에서 주로 이루어지며, 구조되는 측에서는 수동적으로 기다려야 한다. 이에 반해 자가 구성 이 가능한 무선 센서 네트워크는 해양 수색 및 구조작업에서 능동적으로 구조 신호를 보낼 수 있는 시스템을 구축할 수 있다. 본 논문에서는 많은 수의 노드가 배치된 네트워크에서 노드의 소모 에너지를 최소화하기 위하여 시뮬레이티드 어닐링 알고리즘을 제안한다. 네트워크에서 노드의 밀도가 높으면 일반적으로 알고리즘 계산양이 급격히 늘어난다. 따라서 제안된 알고리즘은 적정한 실행 시간 내에 최적의 결과를 찾기 위해 새로운 이웃해 생성 동작을 제안하고 알고리즘의 효율성을 높인다. 제안된 알고리즘은 노드의 소모 에너지와 알고리즘 실행시간 면에서 성능 평가를 하였으며, 성능 평가 결과에서 기존의 방식에 비해 성능이 우수하였다.

In wireless sensor networks, we propose an optimization algorithm in order to minimize the consumed energy of nodes for maritime search and rescue. In the marine environment, search and rescue operations are mainly performed on the surveillance side and passively on the rescued side. A self-configurable wireless sensor network can build a system that can send rescue signals in the operations. A simulated annealing algorithm is proposed to minimize the consumed energy of nodes in the networks with many nodes. As the density of nodes becomes higher, the algorithmic computation will increase highly. To search the good result in a proper execution time, the proposed algorithm proposes a new neighborhood generating operation and improves the efficiency of the algorithm. The proposed algorithm was evaluated in terms of the consumed energy of the nodes and algorithm execution time, and the proposed algorithm performed better than other optimization algorithms in the performance results.

정수계획법은 조합 최적화 문제의 최적해를 매우 효과적으로 탐색할 수 있는 기법인 반면에 대상 문제가 선형적으로 표현되어야만 적용이 가능하다는 단점이 있다. 본 논문에서는 정수계획 법의 뛰어난 탐색 능력과 이웃해 탐색 기법의 유연성을 결합함으로써 비선형 최적화 문제를 효과적으로 해결하는 방안을 제시하고 있다. 먼저 1단계에서는 주어진 문제로부터 선형적으로 표현 가능한 부문제만을 대상으로 정수계획 법을 적용한다. 2단계에서는 전체 문제를 대상으로 이웃해 탐색 기법을 적용하되 1단계의 결과를 초기해로 설정한 후 탐색을 수행한다. 비선형 최대 커버링 문제를 대상으로 한 실험 결과, 이와 같은 간단한 결합만으로도 이웃해 탐색 기법만을 적용했을 때보다 훨씬 좋은 해를 도출할 수 있음을 확인하였다. 이는 기본적으로 정수계획법의 탁월한 성능에 기인한 것으로 판단된다.

Integer programming is a very effective technique for searching optimal solution of combinatorial optimization problems. However, its applicability is limited to linear models. In this paper, I propose an effective method for solving a nonlinear optimization problem by integrating the powerful search performance of integer programming and the flexibility of neighborhood search algorithms. In the first phase, integer programming is executed with subproblem which can be represented as a linear form from the given problem. In the second phase, a neighborhood search algorithm is executed with the whole problem by taking the result of the first phase as the initial solution. Through the experimental results using a nonlinear maximal covering problem, I confirmed that such a simple integration method can produce far better solutions than a neighborhood search algorithm alone. It is estimated that the success is primarily due to the powerful performance of integer programming.

이동 무선 통신 네트워크를 설계할 때 기지국(BTS), 기지국 콘트롤러(BSC), 이동 교환국(MSC)의 위치는 매우 중요한 파라미터들이다. 기지국의 위치를 설계할 때는 여러 가지 복잡한 변수들을 잘 조합하여 비용이 최소가 되도록 설계해야 한다 이러한 문제를 해결하는데 필요한 알고리듬이 전역 최적화 알고리듬이며, 지금까지 전역 최적화 검색 기술로는 Random Walk, Simulated Annealing, Tabu Search, Genetic Algorithm이 사용되어 왔다. 본 논문은 이동 통신 시스템의 기지국, 기지국 콘트롤러, 이동 교환국의 위치 최적화에 위의 4가지 알고리듬들을 적용하여 각 알고리듬의 결과를 비교 분석하며 알고리듬에 의한 최적화 과정을 보여준다.

In the design of mobile wireless communication network, BSC(Base Station Location), BSC(Base Station Controller) and MSC(Mobile Switching Center) are the most important parameters. Designing base station location, the cost must be minimized by combining various, complex parameters. We can solve this Problem by combining optimization algorithm, such as Simulated Annealing, Tabu Search, Genetic Algorithm, Random Walk Algorithm that have been used extensively for global optimization. This paper shows the 4 kinds of algorithm to be applied to the optimization of base station location for communication system and then compares, analyzes the results and shows optimization process of algorithm.

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.

기상수치예보모델의 강수물리과정은 강수 발생과 연관된 입자의 낙하속도, 부착 및 자동전환, 입자크기분포 등의 과정을 다룬다. 하지만 수치예보모델의 미세물리과정과 모수에는 상당한 불확실성이 내포되어 있다. 수치예보모델의 불확실성을 줄이기 위하여 일반적으로 모수 추정을 사용한다. 이 연구에서는 모수 추정을 위한 최적화 알고리즘으로 마이크로 유전알고리즘과 하모니탐색 알고리즘을 사용하고 우리나라에서 발생한 강수사례에 대해 통합모델의 강수물리과정에서 사용하는 모수를 최적화하였다. 두 알고리즘의 서로 다른 특성으로 인해 최적화 과정 중의 차이가 보였다. 마이크로 유전알고리즘은 440회 수행 후 약 1.033의 적합도로 수렴하였고 하모니탐색 알고리즘은 60번 수행 후 약 1.031의 적합도로 수렴하였다. 이를 통해 하모니탐색 알고리즘이 마이크로 유전알고리즘보다 더 빨리 최적의 모수를 탐색하는 것을 알 수 있었다. 따라서 계산비용이 방대한 기상수치예보모델의 최적화 문제에서 빠른 시간 내에 최적의 모수를 탐색해야 한다면 하모니 탐색 알고리즘이 더 적합하다는 것을 확인하였다.

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.