• IE interfaces
• /
• v.15 no.4
• /
• pp.338-348
• /
• 2002

In this paper, we present a restricted tabu search(RTS) algorithm that schedules jobs on identical parallel machines in order to minimize the maximum lateness of jobs. Jobs have release times and due dates. Also, sequence-dependent setup times exist between jobs. The RTS algorithm consists of two main parts. The first part is the MATCS(Modified Apparent Tardiness Cost with Setups) rule that provides an efficient initial schedule for the RTS. The second part is a search heuristic that employs a restricted neighborhood generation scheme with the elimination of non-efficient job moves in finding the best neighborhood schedule. The search heuristic reduces the tabu search effort greatly while obtaining the final schedules of good quality. The experimental results show that the proposed algorithm gives better solutions quickly than the existing heuristic algorithms such as the RHP(Rolling Horizon Procedure) heuristic, the basic tabu search, and simulated annealing.

• Nuclear Engineering and Technology
• /
• v.44 no.7
• /
• pp.799-806
• /
• 2012

In recent years, a new algorithm has been introduced to perform the regional overpower protection (ROP) detector layout optimization for $CANDU^{(R)}$ reactors. This algorithm is called DETPLASA. This algorithm has been shown to successfully come up with a detector layout which meets the target trip set point (TSP) value. Knowing that these ROP detectors are placed in a number of safety channels, one expects that there is an optimal placement of the candidate detectors into these channels. The objective of the present paper is to show that a slight improvement to the TSP value can be realized by optimizing the channelization of these ROP detectors. Depending on the size of the ROP system, based on numerical experiments performed in this study, the range of additional TSP improvement is from 0.16%FP (full power) to 0.56%FP.

• Proceedings of the Korean Society of Broadcast Engineers Conference
• /
• 2009.01a
• /
• pp.638-643
• /
• 2009

A computer-generated hologram(CGH) is made for three-dimensional image reconstruction of a virtual object which is a difficult to irradiate the laser light directly. One of the adverse effect factors is quantization of wave front computed by program when a computer-generated hologram is made. Amplitude element is not considered in Kinoform, it needs processing to reduce noise or false image. So several investigation was reported that the improvement of reconstructed image of Kinoform. Means to calculate the most suitable complex amplitude distribution are iterative algorithm, simulated annealing algorithm and genetic Algorithm. Error diffusion method reconstructed to separate the object as for the noise that originated in the quantization error. So it is efficient method to obtain high quality image with not many processing.

• 제어로봇시스템학회:학술대회논문집
• /
• 2000.10a
• /
• pp.396-396
• /
• 2000

Multiperiod optimization is the key step to tackle the supply chain optimization problems. Taking supply and demand uncertainty or prediction into consideration during the process synthesis phase leads to the maximization of the profit for the long range time horizon. In this study, new algorithm based on the Genetic Algorithms is proposed for multiperiod optimization formulated in MINLP, GDP and hybrid MINLP/GDP. In this study, the focus is given especially on the design of the Genetic Algorithm suitable to handle disjunctive programming with the same level of MINLP handling capability. Hybridization with the Simulated Annealing is tried. and many heuristics are adopted for this purpose.

• Proceedings of the KIEE Conference
• /
• 1998.07b
• /
• pp.623-625
• /
• 1998

This paper focuses on the fast convergence in nonlinear parameter optimization which is necessary for the fitting of nonlinear models to data. The simulated annealing(SA) and genetic algorithm(GA), which are widely used for combinatorial optimization problems, are stochastic strategy for search of the ground state and a powerful tool for optimization. However, their main disadvantage is the long convergence time by unnecessary extra works. It is also recognised that gradient-based nonlinear programing techniques would typically fail to find global minimum. Therefore, this paper develops a modified SA which is the SDS(Stochastic deterministic stochastic) algorithm can minimize cost function of optimal problem.

• Journal of Korean Institute of Industrial Engineers
• /
• v.24 no.4
• /
• pp.551-558
• /
• 1998

In this paper, a job shop scheduling system is developed which can cope with the changes of shop floor status with flexibility. This system suggests near optimal sequence of operations by using Genetic Algorithm which considers alternative process plans. The Genetic Algorithm proposed in this paper has some characteristics. The mutation rate is differentiated in order to enhance the chance to escape a local optimum and to assure the global optimum. And it employs the double gene structure to easily make the modeling of the shop floor. Finally, the quality of its solution and the computational time are examined in comparison with the method of a Simulated Annealing.

• Journal of KIISE:Computing Practices and Letters
• /
• v.5 no.2
• /
• pp.209-222
• /
• 1999

개발 과정의 생산성과 프로그램의 신뢰성을 향상시키기 위하여 소프트웨어 재사용이 매우 중요하며 , 효과적인 재사용을 위해서 세밀한 분류 방법과 정확한 검색 방법에 기반한 객체 지향 재사용 라이브러리가 필수적이다. 본 연구에서는 재사용 라이브러리의 다중 클러스터링(multi-way clustering) 분류 방법과 클러스터 기반 선형 검색(cluster-based linear retrieval) 방법에 유전자 알고리즘(genetic algorithm)을 적용한다. 다중 클러스터링은 부품들이 할당된 클러스터 개수, 클러스터 내부 유사도 그리고 클러스터들 사이의 유사도를 최적화하는 클러스터링을 찾아 부품을 세밀히 분류하는 것이고, 클러스터 기반 선형 검색은 주어진 질의와 유사한 부품을 많이 포함하는 클러스터를 검색하는 것이다. 본 논문에서는 유전자 알고리즘이 시뮬레이티드 어닐링 알고리즘(simulated annealing algorithm) 보다 우수한 해를 찾는 것을 실험을 통하여 보이고, 또한 본 알고리즘을 이용한 CORBA 기반의 재사용 클래스 라이브러리(RCL)를 기술한다.

• Proceedings of the Computational Structural Engineering Institute Conference
• /
• 2008.04a
• /
• pp.416-421
• /
• 2008

A Metropolis genetic algorithm (MGA) is a newly-developed hybrid algorithm combining simple genetic algorithm (SGA) and simulated annealing (SA). In the algorithm, favorable features of Metropolis criterion of SA are incorporated in the reproduction operations of SGA. This way, MGA alleviates the disadvantages of finding imprecise solution in SGA and time-consuming computation in SA. It has been successfully applied and the efficiency has been verified for the practical structural design optimization. However, applicability of MGA for the wider range of problems should be rigorously proved through the solution of mathematical optimization problems. Thus, performances of MGA for the typical mathematical problems are investigated and compared with those of conventional algorithms such as SGA, micro genetic algorithm (${\mu}GA$), and SA. And, for better application of MGA, the effects of acceptance level are also presented. From numerical Study, it is again verified that MGA is more efficient and robust than SA, SGA and ${\mu}GA$ in the solution of mathematical optimization problems having various features.

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

• Journal of Integrative Natural Science
• /
• v.2 no.2
• /
• pp.55-58
• /
• 2009

A ligand-receptor docking program is an indispensible tool in modern pharmaceutical design. An accurate prediction of small molecular docking pose to a receptor is essential in drug design as well as molecular recognition. An effective docking program requires the ability to locate a correct binding pose in a surprisingly complex conformational space. However, there is an inherent difficulty to predict correct binding pose. The odds are more demanding than finding a needle in a haystack. This mainly comes from the flexibility of both ligand and receptor. Because the searching space to consider is so vast, receptor rigidity has been often applied in docking programs. Even nowadays the receptor may not be considered to be fully flexible although there have been some progress in search algorithm. Improving the efficiency of searching algorithm is still in great demand to explore other applications areas with inherently flexible ligand and/or receptor. In addition to classical search algorithms such as molecular dynamics, Monte Carlo, genetic algorithm and simulated annealing, rather recent algorithms such as tabu search, stochastic tunneling, particle swarm optimizations were also found to be effective. A good search algorithm would require a good balance between exploration and exploitation. It would be a good strategy to combine algorithms already developed. This composite algorithms can be more effective than an individual search algorithms.