• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1995년도 Proceedings of the Korea Automation Control Conference, 10th (KACC); Seoul, Korea; 23-25 Oct. 1995
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• pp.319-322
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• 1995

As a trend toward multi-product batch processes is increasing in Chemical Process Industry (CPI), multi-product batch scheduling has been actively studied. But the optimal production scheduling problems for multi-product batch processes are known as NP-complete. Recently Ku and Karimi [5] have studied Simulated Annealing(SA) and Jung et al.[6] have developed Modified Simulated Annealing (MSA) method which was composed of two stage search algorithms for scheduling of batch processes with UIS and NIS. Jung et al.[9] also have studied the Common Intermediate Storage(CIS) policy which have accepted as a high efficient intermediate storage policy. It can be also applied to pipeless mobile intermediate storage pacilities. In spite of these above researches, there have been no contribution of scheduling of CIS policy for chemical batch processes. In this paper, we have developed another MSA for scheduling chemical batch processes with searching the suitable control parameters for CIS policy and have tested the this algorithm with randomly generated various scheduling problems. From these tests, MSA is outperformed to general SA for CIS batch process system.

• Steel and Composite Structures
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• 제50권5호
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• pp.583-594
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• 2024

In this paper, a Guided Simulated Annealing (GSA) algorithm is presented to optimize 2D and 3D steel frames against Progressive Collapse. Considering the nature of structural optimization problems, a number of restrictions and improvements have been applied to the decision mechanisms of the algorithm without harming the randomness. With these improvements, the algorithm aims to focus relatively on the flawed variables of the analyzed frame. Besides that, it is intended to be more rational by instituting structural constraints on the sections to be selected as variables. In addition to the LRFD restrictions, the alternate path method with nonlinear dynamic procedure is used to assess the risk of progressive collapse, as specified in the US Department of Defense United Facilities Criteria (UFC) Design of Buildings to Resist Progressive Collapse. The entire optimization procedure was carried out on a C# software that supports parallel processing developed by the authors, and the frames were analyzed in SAP2000 using OAPI. Time history analyses of the removal scenarios are distributed to the processor cores in order to reduce computational time. The GSA produced 3% lighter structure weights than the SA (Simulated Annealing) and 4% lighter structure weights than the GA (Genetic Algorithm) for the 2D steel frame. For the 3D model, the GSA obtained 3% lighter results than the SA. Furthermore, it is clear that the UFC and LRFD requirements differ when the acceptance criteria are examined. It has been observed that the moment capacity of the entire frame is critical when designing according to UFC.

• 한국정보처리학회논문지
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• 제6권9호
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• pp.2414-2421
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• 1999

본 논문에서는 Rural Postman Problem(RPP) 해법으로 2가지의 휴리스틱 알고리즘을 제안한다. 첫 번째 휴리스틱 알고리즘으로 냉각 스케줄을 향상시킨 Simulated Annealing(SA) 알고리즘을 제안하였고, 두 번째로는 문제의 특성인 주어진 에지를 모두 나타낼 수 있는 염색체 구성 방법을 포함한 유전자 알고리즘을 제안하였다. 실험 계산을 통하여 제안된 두 방법이 기존의 방법보다 우수함을 보였다.

• 한국컴퓨터그래픽스학회논문지
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• 제4권1호
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• pp.31-40
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• 1998

활성외곽선모델(active contour model)은 물체의 경계를 분할하기 위한 효과적인 방법으로 사용되고 있다. 그런데, 기존 활성외곽선모텔에서는 초기곡선을 분할하고자하는 물체의 경계면에 위치시키고 지역적으로 에너지를 최소화 함에 따라 결과가 초기 곡선의 위치와 형태에 따라 달라지는 단점이 있었다. 본 논문에서는 활성외곽선모델을 B-Spline 곡선에 의해 표현하고, 에너지 최소화 과정에 유전자 알고리즘(Genetic Algorithm: GA)과 시뮬레이티드 어닐링 (Simulated Annealing : SA)을 적용함으로써 기존 활성외곽선모델이 갖는 초기 곡선에 대한 제약성을 개선하고자 했으며, 두가지 방법에 따른 분할 결과와 문제점을 비교하고자 하였다. 제안한 방법의 성능비교를 위하여 이진 합성 영상과 CT 영상, MR 영상을 대상으로 실험을 수행하였다.

• Smart Structures and Systems
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• 제13권3호
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• pp.389-406
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• 2014

Optimal sensor placement techniques play a significant role in enhancing the quality of modal data during the vibration based health monitoring of civil structures, where many degrees of freedom are available despite a limited number of sensors. The literature has shown a shift in the trends for solving such problems, from expansion or elimination approach to the employment of heuristic algorithms. Although these heuristic algorithms are capable of providing a global optimal solution, their greatest drawback is the requirement of high computational effort. Because a highly efficient optimisation method is crucial for better accuracy and wider use, this paper presents an improved simulated annealing (SA) algorithm to solve the sensor placement problem. The algorithm is developed based on the sensor locations' coordinate system to allow for the searching in additional dimensions and to increase SA's random search performance while minimising the computation efforts. The proposed method is tested on a numerical slab model that consists of two hundred sensor location candidates using three types of objective functions; the determinant of the Fisher information matrix (FIM), modal assurance criterion (MAC), and mean square error (MSE) of mode shapes. Detailed study on the effects of the sensor numbers and cooling factors on the performance of the algorithm are also investigated. The results indicate that the proposed method outperforms conventional SA and Genetic Algorithm (GA) in the search for optimal sensor placement.

• 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.

• Industrial Engineering and Management Systems
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• 제8권1호
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• pp.22-28
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• 2009

We consider facility layout problems, where mn facility units are assigned into mn cells. These cells are arranged into a rectangular pattern with m rows and n columns. In order to solve this cell type facility layout problem, many approximation algorithms with improved local search methods were studied because it was quite difficult to find exact optimum of such problem in case of large size problem. In this paper, new algorithms based on Simulated Annealing (SA) method with two neighborhood generation methods are proposed. The new neighborhood generation method adopts the exchanging operation of facility units in accordance with adjacent preference. For evaluating the performance of the neighborhood generation method, three algorithms, previous SA algorithm with random 2-opt neighborhood generation method, the SA-based algorithm with the new neighborhood generation method (SA1) and the SA-based algorithm with probabilistic selection of random 2-opt and the new neighborhood generation method (SA2), are developed and compared by experiment of solving same example problem. In case of numeric examples with problem type 1 (the optimum layout is given), SA1 algorithm could find excellent layout than other algorithms. However, in case of problem type 2 (random-prepared and optimum-unknown problem), SA2 was excellent more than other algorithms.

• 전자공학회논문지CI
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• 제38권5호
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• pp.27-36
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• 2001

본 논문은 온라인 벡터 양자화기 설계에 stochastic relaxation (SR) 개념을 응용함으로써 SR 방법에 기초한 새로운 온라인 학습 알고리즘을 제안한다. 이는 전통적인 Kohonen 학습법 (KLA)이 안고 있는 극소점(local minimum)으로의 수렴 문제를 개선시켜준다. SR 방법의 응용은 simulated annealing (SA) 개념을 사용하느냐 안 하느냐에 따라 둘로 나눌 수 있는데, 이를 구분하기 위해 SA 개념을 이용하는 SR 알고리즘을 LOVQ-SA로, SA 개념을 이용하지 않는 알고리즘을 OLVQ SR로 부르기로 한다. 제안된 방법들은 KLA와 결합되어 있으며 KLA의 특성을 보존하도록 설계되었다. 이는 제안된 방법들의 수렴의 속도 및 안정성을 향상시켜준다. 제안된 방법의 우수성을 입증하기 위하여 Gauss-Markov 신호원과 음성 및 영상 자료에 대한 벡터양자화 실험을 하였으며 실험결과를 통하여 제안된 방법이 KLA 보다 일관되게 우수한 코드북을 생성함을 보인다.

• 인터넷정보학회논문지
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• 제6권6호
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• pp.99-106
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• 2005

멀티프로세서 시스템에서 스케줄링은 매우 중요한 부분이지만, 최적의 해를 구하는 것이 복잡하여 다양한 휴리스틱 방법들에 의한 스케줄링 알고리즘들이 제안되고 있다. 최근 유전 알고리즘을 사용한 멀티프로세서 스케줄링 알고리즘들이 제시되고 있지만, 제시된 알고리즘 대부분은 한가지만의 목적을 가지는 단순한 알고리즘이다. 본 논문에서는 유전 알고리즘을 이용한 새로운 스케줄링 알고리즘을 제시한다. 또한, 해를 구하는 과정에서 시뮬레이티드 어닐링 (simulated annealing)의 확률을 이용하여 유전 알고리즘의 성능을 개선시킨다. 제시된 알고리즘은 태스크들의 최종 수행 완료 시간 (makespan)을 최소화하는 것과 사용된 프로세서의 수를 최소화하는 두 가지의 목표를 가진다. 모의 실험을 통하여 제시된 알고리즘이 다른 알고리즘보다 최종 수행 완료 시간과 사용된 프로세서의 수에서 더 나은 결과를 보임을 확인할 수 있었다.

• 경영과학
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• 제15권2호
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• pp.153-159
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• 1998

Clustering or classification is a very fundamental task that may occur almost everywhere for the purpose of grouping. Optimal clustering is an example of very complicated combinatorial optimization problem and it is hard to develop a generally applicable optimal algorithm. In this paper we propose a general-purpose algorithm for the optimal clustering based on SA(simulated annealing). Among various iterative global optimization techniques imitating natural phenomena that have been proposed and utilized successfully for various combinatorial optimization problem, simulated annealing has its superiority because of its convergence property and simplicity. We first present a version of accelerated simulated annealing(ASA) and then we apply ASA to develop an efficient clustering algorithm. Application examples are also given.