• Proceedings of the KIEE Conference
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• 2003.07a
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• pp.369-371
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• 2003

본 논문에서는 차세대 대체 에너지원으로서 주목받고 있으며, 상용화되고 있는 분산전원이 도입된 배전계통에서 고장이 발생한 경우, 경험적 최적화 알고리즘인 타부 탐색법을 적용한 고장 복구 알고리즘을 제안하고자 한다. 배전계통 고장복구 문제는 배전 선로상에 고장 발생시 최적의 부하절체를 함으로써 건전 정전구간을 최소화하는 것을 목적으로 한다. 배전 자동화시스템에서 분산전원 계통을 자동화하여 분산전원의 동작 상태를 감시하고 고장검출, 계통분리 또는 원격스위치를 제어함으로써 고장복구 방법을 제시한다. 제안한 알고리즘의 유용성을 입증하기 위해 참고문헌의 예제 계통에 제안한 방법을 적용해 본 결과, 제안한 알고리즘이 해의 탐색속도 및 해의 성능면에서 우수함을 확인하였다.

• The KIPS Transactions:PartB
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• v.11B no.1
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• pp.63-70
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• 2004

Port operation is largely divided into gate operation, yard operation and berth operation. Operation strategy and optimal resource allocation for three parts are important in the productivity of the port operation.. Especially the resource allocation planning in berth operation needs optimization, because it is directly connected with the processing time in shipping. Berth planning is not independent on recourse allocation but interrelated with yard stacking area allocation. Therefore, we design the optimized module of berth planning and give priority to interrelationship with yard space allocation, while existing studies design independent resource allocation in berth planning. We suggest constraints by mathematical method, and they are related to yard stacking area allocation with existing constraints. Then we look for solutions, use tabu search to optimize them, and design optimized the berth planning module. In the performance test of optimized module design of berth planning, we find that the berth planning with yard stacking area allocation takes less processing time than without yard stacking area allocation.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.50 no.3
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• pp.138-144
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• 2001

• Transactions of the Korean Society of Mechanical Engineers A
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• v.27 no.5
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• pp.798-806
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• 2003

Structural optimization has been carried out in continuous or discrete design space. Methods for continuous design have been well developed though they are finding the local optima. On the contrary, the existing methods for discrete design are extremely expensive in computational cost or not robust. In this research, an algorithm using tabu search is developed fur the discrete structural designs. The tabu list and the neighbor function of the Tabu concepts are introduced to the algorithm. It defines the number of steps, the maximum number for random searches and the stop criteria. A tabu search is known as the heuristic approach while genetic algorithm and simulated annealing algorithm are attributed to the stochastic approach. It is shown that an algorithm using the tabu search with random moves has an advantage of discrete design. Furthermore, the suggested method finds the reliable optimum for the discrete design problems. The existing tabu search methods are reviewed. Subsequently, the suggested method is explained. The mathematical problems and structural design problems are investigated to show the validity of the proposed method. The results of the structural designs are compared with those from a genetic algorithm and an orthogonal array design.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2006.05a
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• pp.164-170
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• 2006

This paper proposes a RSM-based hybrid evolutionary algorithm (RHEA) which combines the merits of the popular programs such as genetic algorithm (GA), tabu search method, response surface methodology (RSM). This algorithm, for improving the convergent speed that is thought to be the demerit of genetic algorithm, uses response surface methodology and simplex method. The mutation of GA offers random variety to finding the optimum solution. In this study, however, systematic variety can be secured through the use of tabu list. Efficiency of this method has been proven by applying traditional test functions and comparing the results to GA. And it was also proved that the newly suggested algorithm is very effective to find the global optimum solution to minimize the weight for avoiding the resonance of fresh water tank that is placed in the rear of ship. According to the study, GA's convergent speed in initial stages is improved by using RSM method. An optimized solution is calculated without the evaluation of additional actual objective function. In a summary, it is concluded that RHEA is a very powerful global optimization algorithm from the view point of convergent speed and global search ability.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.16 no.6 s.111
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• pp.665-673
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• 2006

This paper proposes a RSM-based hybrid evolutionary Algorithm (RHEA) which combines the merits of the popular programs such as genetic algorithm (GA), tabu search method and response surface methodology (RSM). This algorithm, for improving the convergent speed that is thought to be the demerit of genetic algorithm, uses response surface methodology and simplex method. The mutation of GA offers random variety to finding the optimum solution. In this study, however, systematic variety can be secured through the use of tabu list. Efficiency of this method has been proven by applying traditional left functions and comparing the results to GA. It was also proved that the newly suggested algorithm is very effective to find the global optimum solution to minimize the weight for avoiding the resonance of fresh water tank that is placed in the after body area of ship. According to the study, GA's convergent speed in initial stages is improved by using RSM method. An optimized solution is calculated without the evaluation of additional actual objective function. In a summary, it is concluded that RHEA is a very powerful global optimization algorithm from the view point of convergent speed and global search ability.

• Proceedings of the KIPE Conference
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• 2010.07a
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• pp.51-52
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• 2010

In order to simplify the offline identification of induction motor parameters, a method based on optimization using a Tabu Search algorithm is proposed. The Tabu Search algorithm is used to minimize the error between the actual data and an estimated model. The robustness of the method is shown by identifying parameters of the induction motor in three different cases. The simulation results show that the method successfully estimates the motor parameters.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.53 no.5
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• pp.323-330
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• 2004

In this paper, we propose a design method of PID controller using an improved Tabu Search. Tabu Search is improved by neighbor solution creation using Gaussian random distribution and generalized Hermite Biehler Theorem for stable bounds. The range of admissible proportional gains are determined first in closed form. Next the optimal PID gains are selected by improved Tabu Search. The results of Computer simulations represent that the proposed Tabu Search algorithm shows a fast convergence speed and a good control performance.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.38 no.1
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• pp.48-57
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• 2010

A comparison of two kinds of scheduling optimization algorithms is presented in this paper. As satellite control and operation techniques have been developed, satellite missions became more complicated and overall quantity of missions also increased. These changes require more specific consideration and a huge amount of computation for the satellite mission scheduling. Therefore, it is a good strategy to make a scheduling optimization algorithm for the efficient satellite mission scheduling operation. In this paper, two kinds of scheduling optimization algorithms are designed with tabu-search algorithm and genetic algorithm respectively. These algorithms are applied for the same mission scenario and the results of each algorithm are compared and analyzed.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.2
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• pp.209-214
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• 2020

The equitable partitioning problem(EPP) is classified as [0/1] binary skill existence or nonexistence and integer skill levels such as [1,2,3,4,5]. There is well-known a polynomial-time optimal solution finding algorithm for binary skill EPP. On the other hand, tabu search a kind of metaheuristic has apply to integer skill level EPP is due to unknown polynomial-time algorithm for it and this problem is NP-hard. This paper suggests heuristic greedy algorithm with polynomial-time to find the optimal solution for integer skill level EPP. This algorithm descending sorts of skill level frequency for each field and decides the lower bound(LB) that more than the number of group, packing for each group bins first, than the students with less than LB allocates to each bin additionally. As a result of experimental data, this algorithm shows performance improvement than the result of tabu search.