• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.1
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• pp.95-101
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• 2022

The two-sided assembly line balancing problem is a kind of NP-hard problem. This problem primarily can be solved metaheuristic method. This paper suggests parallel clustering algorithm that each left and right-sided workstation assigned by operations with T_i = c^* ± α < c, c^* = ${\lceil}$W/m^*${\rceil}$ such that M^* = ${\lceil}$W/c${\rceil}$ for precedence diagram of two-sided assembly line with total complete time W and cycle time c. This clustering performs forward direction from left to right or reverse direction from right to left. For the 4 experimental data with 17 cycle times, the proposed algorithm can be obtain the minimum number of workstations m^* and can be reduce the cycle time to T_max < c then metaheuristic methods. Also, proposed clustering algorithm maximizes the line efficiency and minimizes the variance between workers operation times.

• Journal of Korea Society of Industrial Information Systems
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• v.27 no.1
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• pp.37-48
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• 2022

In this study, a hybrid meta-heuristic approach is proposed for solving engineering design optimization problems. Various approaches in many literatures have been proposed to solve engineering optimization problems with various types of decision variables and complex constraints. Unfortunately, however, their efficiencies for locating optimal solution do not be highly improved. Therefore, we propose a hybrid meta-heuristic approach for improving their weaknesses. the proposed GA-VNS-HC approach is combining genetic algorithm (GA) for global search with variable neighborhood search (VNS) and hill climbing (HC) for local search. In case study, various types of engineering design optimization problems are used for proving the efficiency of the proposed GA-VNS-HC approach

• Journal of the Korea Institute of Military Science and Technology
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• v.20 no.3
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• pp.441-453
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• 2017

In this paper, a new type of weapon-target assignment(WTA) problem has been suggested that reflects realistic constraints for sharing target with other weapons and shooting double rapid fire. To utilize in rapidly changing actual battle field, the computation time is of great importance. Several metaheuristic methods such as Simulated Annealing, Tabu Search, Genetic Algorithm, Ant Colony Optimization, and Particle Swarm Optimization have been applied to the real-time WTA in order to find a near optimal solution. A case study with a large number of targets in consideration of the practical cases has been analyzed by the objective value of each algorithm.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.8
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• pp.1196-1204
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• 2022

In this paper, we propose an optimization algorithm that maximizes the network lifetime in wireless ad-hoc networks using the broadcast transmission method. The optimization algorithm proposed in this paper applies tabu search algorithm, a metaheuristic method that improves the local search method using the memory structure. The proposed tabu search algorithm proposes efficient encoding and neighborhood search method to the network lifetime maximization problem. By applying the proposed method to design efficient broadcast routing, we maximize the lifetime of the entire network. The proposed tabu search algorithm was evaluated in terms of the energy consumption of all nodes in the broadcast transmission occurring in the network, the time of the first lost node, and the algorithm execution time. From the performance evaluation results under various conditions, it was confirmed that the proposed tabu search algorithm was superior to the previously proposed metaheuristic algorithm.

• The KIPS Transactions:PartB
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• v.16B no.3
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• pp.195-202
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• 2009

In this paper, we propose the regions segmentation method of the white matter and the gray matter for brain MR image by using the ant colony optimization algorithm. Ant Colony Optimization (ACO) is a new meta heuristics algorithm to solve hard combinatorial optimization problem. This algorithm finds the expected pixel for image as the real ant finds the food from nest to food source. Then ants deposit pheromone on the pixels, and the pheromone will affect the motion of next ants. At each iteration step, ants will change their positions in the image according to the transition rule. Finally, we can obtain the segmentation results through analyzing the pheromone distribution in the image. We compared the proposed method with other threshold methods, viz. the Otsu' method, the genetic algorithm, the fuzzy method, and the original ant colony optimization algorithm. From comparison results, the proposed method is more exact than other threshold methods for the segmentation of specific region structures in MR brain image.

• Proceedings of the Korean Information Science Society Conference
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• 2007.10c
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• pp.371-374
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• 2007

본 논문에서는 고정 비용을 고려한 비선형 수송문제(Fixed Charge Non-linear Transportation Problem)에 대해 다룬다. 이는 한 종류의 상품을 다수의 공급처에서 다수의 수급처로 수송할 때, 총 수송비용과 고정 비용이 최소가 되도록 각 공급처와 수급처 간의 수송량을 결정하는 문제이다. 현재 비선형 수송문제에 대한 다양한 해법들이 제안되고 있으며 그 중에서도 메타 휴리스틱을 이용한 해법들이 가장 활발히 연구되고 있다. 본 논문에서는 메타 휴리스틱 방법들중에 가장 널리 이용되고 있는 유전 알고리즘을 이용한 해법을 제시하고자 한다. 유전 알고리즘을 적용함에 있어서 제일 첫 관문은 해의 유전자표현을 어떻게 나타낼 것인가이다. 본 논문에서는 수송문제의 해를 걸침나무로 표현할 수 있다는 점 에 착안하여 다양한 트리 표현법을 수송문제에 적용해 보고 수치 실험을 통해 그 성능에 대한 비교 연구를 한다.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.186-186
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• 2022

강수 및 침투 등으로 발생하는 지하수위의 변동을 예측하는 것은 지하수 자원의 활용 및 관리에 필수적이다. 지하수위의 변동은 지하수 자원의 활용 및 관리뿐만이 아닌 홍수 발생과 지반의 응력상태 등에 직접적인 영향을 미치기 때문에 정확한 예측이 필요하다. 본 연구는 인공신경망 중 다층퍼셉트론(Multi Layer Perceptron, MLP)을 이용한 지하수위 예측성능 향상을 위해 MLP의 구조 중 Optimizer를 개량하였다. MLP는 입력자료와 출력자료간 최적의 상관관계(가중치 및 편향)를 찾는 Optimizer와 출력되는 값을 결정하는 활성화 함수의 연산을 반복하여 학습한다. 특히 Optimizer는 신경망의 출력값과 관측값의 오차가 최소가 되는 상관관계를 찾는 연산자로써 MLP의 학습 및 예측성능에 직접적인 영향을 미친다. 기존의 Optimizer는 경사하강법(Gradient Descent, GD)을 기반으로 하는 Optimizer를 사용했다. 하지만 기존의 Optimizer는 미분을 이용하여 상관관계를 찾기 때문에 지역탐색 위주로 진행되며 기존에 생성된 상관관계를 저장하는 구조가 없어 지역 최적해로 수렴할 가능성이 있다는 단점이 있다. 본 연구에서는 기존 Optimizer의 단점을 개선하기 위해 지역탐색과 전역탐색을 동시에 고려할 수 있으며 기존의 해를 저장하는 구조가 있는 메타휴리스틱 최적화 알고리즘을 이용하였다. 메타휴리스틱 최적화 알고리즘 중 구조가 간단한 화음탐색법(Harmony Search, HS)과 GD의 결합모형(HS-GD)을 MLP의 Optimizer로 사용하여 기존 Optimizer의 단점을 개선하였다. HS-GD를 이용한 MLP의 성능검토를 위해 이천시 지하수위 예측을 실시하였으며 예측 결과를 기존의 Optimizer를 이용한 MLP 및 HS를 이용한 MLP의 예측결과와 비교하였다.

• Journal of the Korean Institute of Intelligent Systems
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• v.26 no.5
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• pp.335-342
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• 2016

When dynamics changes occurred in an existing Bayesian Network (BN), the related parameters embedding on the BN have to be updated to new parameters adapting to changed patterns. In this case, these parameters have to be updated with the consideration of the causalities in the BN. This research suggests a framework for updating parameters dynamically using Expectation Maximization (EM) algorithm and Harmony Search (HS) algorithm among several Meta-Heuristics techniques. While EM is an effective algorithm for estimating hidden parameters, it has a limitation that the generated solution converges a local optimum in usual. In order to overcome the limitation, this paper applies HS for tracking the global optimum values of Maximum Likelihood Estimators (MLE) of parameters. The proposed method suggests a learning and propagation framework of BN with dynamic changes for overcoming disadvantages of EM algorithm and converging a global optimum value of MLE of parameters.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.6
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• pp.778-785
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• 2020

Wireless mesh networks consist of mesh clients, mesh routers and mesh access points. The mesh router connects wireless network services to the mesh client, and the mesh access point connects to the backbone network using a wired link and provides Internet access to the mesh client. In this paper, a limited number of mesh routers and mesh access points are used to propose optimization algorithms for network design for wireless mesh networks. The optimization algorithm in this paper has been applied with a sub-subscription algorithm, which is one of the meta-heuristic methods, and is designed to minimize the transmission delay for the placement of mesh routers and mesh access points, and produce optimal results within a reasonable time. The proposed algorithm was evaluated in terms of transmission delay and time to perform the algorithm for the placement of mesh routers and mesh access points, and the performance evaluation results showed superior performance compared to the previous meta-heuristic methods.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.16 no.1
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• pp.245-251
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• 2016

This paper suggests a heuristic algorithm to obtain optimal solution of minimum number of aircraft delay in multi-airport arrivals/departures problem. This single airport arrivals/departures problem can be solved by mathematical optimization method only. The linear programming or genetic algorithm that is a kind of metaheuristic method is used for a multi-airport arrivals/departures problem. Firstly, the proposed algorithm selects the median minimum delays capacity in various arrivals/departures capacities at an airport for the number of aircraft in $i^{th}$ time interval (15 minutes) at each airport. Next, we suggest reallocate method for arrival aircraft between airports. This algorithm better result of the number of delayed aircraft then genetic algorithm.