• 한국지능시스템학회논문지
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• 제8권3호
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• pp.71-79
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• 1998

본 논문은 유전알고리즘과 random tabu 탐색법을 조합한 새로운 최적화 알고리즘을 제안한다. 유전알고리즘과 전역적인 최적해에 대한 탐색능력이 우수하고, random tabu 탐색법은 최적해에의 수렴속도가 매우 빠른 알고리즘이다. 본 논문에서는 이 두 알고리즘의 장점을 이용해서 수렴정도와 수렴속도가 더욱 향상된 최적알고리즘을 제안하여 알고리즘의 수렴성능을 조사하고, 실제 최적화문제로서 지진응답을 최소로 하기위한 배관지지대의 최적배치문제에 적용하여 기존의 방법과 비교를 통하여 유용성을 검토하였다.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 1997년도 추계학술대회논문집; 한국과학기술회관; 6 Nov. 1997
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• pp.184-189
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• 1997

This paper introduces a new combined method by genetic algorithm and random tabu search method as optimization algorithm. Genetic algorithm can search the global optimum and tabu search method is very fast in speed. The optimizing ability of new combined method is identified by comparing other optimizing algorithm and used for optimum design of damping plate.

• 대한전기학회논문지:시스템및제어부문D
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• 제53권5호
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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.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 1996년도 추계학술대회논문집; 한국과학기술회관, 8 Nov. 1996
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• pp.301-306
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• 1996

A torsional damper is generally used to reduce the torsional vibration which occurs at a crankshaft of a multi-cylinder high speed diesel engine. Vibration amplitude should be estimate by the appropriate simulation model to determine the optimum specifications of damper. In this paper a new method which was based on the random tabu search method(RTSM) would be introduced for the viscous damper design to optimize the damping performance. The result was ascertained by comparing with conventional rubber damper.

• 대한기계학회논문집A
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• 제27권5호
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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.

• 한국소음진동공학회논문집
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• 제12권6호
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• pp.478-487
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• 2002

This paper presents a hybrid method to compute the solutions of an optimization Problem. The present hybrid algorithm is the synthesis of an artificial life algorithm and the random tabu search method. The artificial life algorithm has the most important feature called emergence. The emergence is the result of dynamic interaction among the individuals consisting of the system and is not found in an individual. The conventional artificial life algorithm for optimization is a stochastic searching algorithm using the feature of artificial life. Emergent colonies appear at the optimum locations in an artificial ecology. And the locations are the optimum solutions. We combined the feature of random-tabu search method with the conventional algorithm. The feature of random-tabu search method is to divide any given region into sub-regions. The enhanced artificial life algorithm (EALA) not only converge faster than the conventional artificial life algorithm, but also gives a more accurate solution. In addition, this algorithm can find all global optimum solutions. The enhanced artificial life algorithm is applied to the optimum design of high-speed, short journal bearings and its usefulness is verified through an optimization problem.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 1994년도 추계학술대회논문집; 한국종합전시장, 18 Nov. 1994
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• pp.121-125
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• 1994

본 논문에서는 Hu에 의해 고안된 random 탐색법과 조합된 tabu 탐색법(radnom tabu 탐색법)을 결합계수를 구하는 학습 알고리즘으로 직접 사용하여 국소적 최적해에 수렴하는 것을 방지하고, 수렴정도를 개선하는 새로운 방법을 제안한다. 이 방법을 배타적 논리합 문제에 적용하여 역전파법 및 tabu 탐색법을 이용한 오차역전파법과 비교한다. 그리고, 각 파라메터가 오차함수의 수렴에 미치는 영향을 조사한다.

• 한국지능시스템학회논문지
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• 제5권3호
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• pp.83-91
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• 1995

본 연구에서는 종래에 학습법으로 널리 이용되고 있는 역전파학습법의 문제점으로 지적되어 온 학습에 많은 시간이 걸리는 점과 국소적 최적해에 해가 수렴하여 오차가 충분히 작게 되지 않는 등의 문제점을 해결하기 위해, Hu에 의해 고안된 random tabu 탐색법을 이용하여 신경회로망의 연결강도를 최적화하는 학습알고리즘을 새로이 제안하였다. 그리고 이 방법을 배타적 논리합 문제에 적용하여 기존의 역전파학습법과 학습상수 $, $에 tabu탐색법을 이용한 결과와 비교 검토하여 본 방법이 국소적 최적해에 수렴하지 않고 수렴정도를 개선할 수 있음을 확인하였다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2003년도 ICCAS
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• pp.377-381
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• 2003

The design of speed controller for induction motor using tabu search is studied. The proposed sensorless vector control for Induction Motor is composed of two parts. The first part is for optimizing the initial parameters of input-output. The second part is for real time changing parameters of input-output using tabu search. Proposed tabu search is improved by neighbor solution creation using Gaussian random distribution. In order to show the usefulness of the proposed method, we apply the proposed controller to the sensorless speed control of an actual AC induction Motor System. The performance of this approach is verified through simulation.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제18권4호
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• pp.843-859
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• 2024

The university course scheduling problem (UCSP) aims at optimally arranging courses to corresponding rooms, faculties, students, and timeslots with constraints. Previously, the university staff solved this thorny problem by hand, which is very time-consuming and makes it easy to fall into chaos. Even some meta-heuristic algorithms are proposed to solve UCSP automatically, while most only utilize one single algorithm, so the scheduling results still need improvement. Besides, they lack an in-depth analysis of the inner algorithms. Therefore, this paper presents a novel and practical approach based on Tabu search and simulated annealing algorithms for solving USCP. Firstly, the initial solution of the UCSP instance is generated by one construction heuristic algorithm, the first fit algorithm. Secondly, we defined one union move selector to control the moves and provide diverse solutions from initial solutions, consisting of two changing move selectors. Thirdly, Tabu search and simulated annealing (SA) are combined to filter out unacceptable moves in a parallel mode. Then, the acceptable moves are selected by one adaptive decision algorithm, which is used as the next step to construct the final solving path. Benefits from the excellent design of the union move selector, parallel tabu search and SA, and adaptive decision algorithm, the proposed method could effectively solve UCSP since it fully uses Tabu and SA. We designed and tested the proposed algorithm in one real-world (PKNU-UCSP) and ten random UCSP instances. The experimental results confirmed its effectiveness. Besides, the in-depth analysis confirmed each component's effectiveness for solving UCSP.