• Journal of Korea Multimedia Society
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• v.9 no.1
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• pp.119-126
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• 2006

The task scheduling in multiprocessor system is one of the key elements in the effective utilization of multiprocessor systems. The optimal assignment of tasks to multiprocessor is, in almost practical cases, an NP-hard problem. Consequently algorithms based on various modern heuristics have been proposed for practical reason. This paper proposes a new task scheduling algorithm using Genetic Algorithm which combines simulated annealing (GA+SA) in multiprocessor environment. In solution algorithms, the Genetic Algorithm (GA) and the simulated annealing (SA) are cooperatively used. In this method, the convergence of GA is improved by introducing the probability of SA as the criterion for acceptance of new trial solution. The objective of proposed scheduling algorithm is to minimize makespan. The effectiveness of the proposed algorithm is shown through simulation studies. In simulation studies, the result of proposed algorithm is better than that of any other algorithms.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.10
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• pp.4887-4907
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• 2017

Accurate traffic flow forecasting is critical to the development and implementation of city intelligent transportation systems. Therefore, it is one of the most important components in the research of urban traffic scheduling. However, traffic flow forecasting involves a rather complex nonlinear data pattern, particularly during workday peak periods, and a lot of research has shown that traffic flow data reveals a seasonal trend. This paper proposes a new traffic flow forecasting model that combines seasonal relevance vector regression with the hybrid chaotic simulated annealing method (SRVRCSA). Additionally, a numerical example of traffic flow data from The Transportation Data Research Laboratory is used to elucidate the forecasting performance of the proposed SRVRCSA model. The forecasting results indicate that the proposed model yields more accurate forecasting results than the seasonal auto regressive integrated moving average (SARIMA), the double seasonal Holt-Winters exponential smoothing (DSHWES), and the relevance vector regression with hybrid Chaotic Simulated Annealing method (RVRCSA) models. The forecasting performance of RVRCSA with different kernel functions is also studied.

• International conference on construction engineering and project management
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• 2007.03a
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• pp.530-539
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• 2007

Many construction projects such as highways, pipelines, tunnels, and high-rise buildings typically contain repetitive activities. Research has shown that the Critical Path Method (CPM) is not efficient in scheduling linear construction projects that involve repetitive tasks. Linear Scheduling Method (LSM) is one of the techniques that have been developed since 1960s to handle projects with repetitive characteristics. Although LSM has been regarded as a technique that provides significant advantages over CPM in linear construction projects, it has been mainly viewed as a graphical complement to the CPM. Studies of scheduling linear construction projects with resource consideration are rare, especially with multiple resource constraints. The objective of this proposed research is to explore a resource assignment mechanism, which assigns multiple critical resources to all activities to minimize the project duration while satisfying the activities precedence relationship and resource limitations. Resources assigned to an activity are allowed to vary within a range at different stations, which is a combinatorial optimization problem in nature. A heuristic multiple resource allocation algorithm is explored to obtain a feasible initial solution. The Simulated Annealing search algorithm is then utilized to improve the initial solution for obtaining near-optimum solutions. A housing example is studied to demonstrate the resource assignment mechanism.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2009.01a
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• pp.516-519
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• 2009

In the printer and the facsimile communication, digital halftoning is extremely important technologies. Error diffusion method is applied easy for color image halftoning. But the problem in error diffusion method is that a quite unrelated color has been generated though it is necessary to express the area of the grayscale in the black and white when the image that there is an area of the grayscale on a part of the color image is processed. The halftoning was assumed to be a combinational optimization problem to solve this problem, and the method of using SA (Simulated Annealing) was proposed. However, new problem existed because the processing time was a great amount compared with error diffusion method. Then, we propose the new error diffusion method.

• Journal of the Korean Society for Precision Engineering
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• v.18 no.6
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• pp.50-58
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• 2001

In this paper, a method to apply the simulated annealing method to three dimensional layout design problem which has multiple constraint conditions and evaluation criteria, was suggested. A program to support three dimensional layout design was developed according to the suggested method. This program was applied to the layout design of the wireless unmaned ocean space submergible boat. The layout result was improved 19.0% for the result of layout design expert. By this, it was verified that the suggested method has validity in supporting three dimensional layout design problem.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2003.05a
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• pp.1077-1084
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• 2003

The performance of simulated annealing (SA) algorithm such as solution optimality and computation time mainly depends on how to determine the SA-related parameters Several schemes have been suggested to improve the performance of SA and several parameter design methods have been utilized to select parameter values of each scheme. In this paper, we propose a new SA algorithm design method that can determine schemes as well as parameter values simultaneously The new SA algorithm design method is based on the Taguchl method which primarily selects the design parameters for a product or process to minimize the effect of noise parameters. so that the response is close to the desired target with minimum variation. To show the effectiveness of the proposed method, extensive computation experiments are conducted.

• The Transactions of the Korea Information Processing Society
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• v.7 no.4
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• pp.1141-1149
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• 2000

This paper proposes new cost error measurement method and analyzes the optimistic and pessimistic cost errors statistically which is resulted from an asynchronous parallel Simulated annealing (SA) in distributed memory multicomputers. The traditional cost error measurement scheme has inherent problems which are corrected in the new method. At each temperature the new method predicts the amount of cost error that an algorithm will tolerate and still converge by the hill-climbing nature of SA. This method also explains three interesting phenomenon of he cost error analytically. So the new cost error measurement method provides a single mechanism for the occurrence of cost error and its control.

• Journal of the Korean Operations Research and Management Science Society
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• v.29 no.1
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• pp.43-56
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• 2004

The purpose of clustering is to partition a set of objects into several clusters based on some appropriate similarity measure. In most cases, clustering is considered without any prior information on the number of clusters or the structure of the given data, which makes clustering is one example of very complicated combinatorial optimization problems. In this paper we propose a general-purpose clustering method that can determine the proper number of clusters as well as efficiently carry out clustering analysis for various types of data. The method is composed of two stages. In the first stage, two different hierarchical clustering methods are used to get a reasonably good clustering result, which is improved In the second stage by ASA(accelerated simulated annealing) algorithm equipped with specially designed perturbation schemes. Extensive experimental results are given to demonstrate the apparent usefulness of our ASA clustering method.

• Steel and Composite Structures
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• v.45 no.4
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• pp.579-590
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• 2022

One of the biggest problems in structural steel calculation is the design of structures using the lowest possible material weight, making this a slow and costly process. To achieve this objective, several optimization methods have been developed and tested. Nevertheless, a method that performs very efficiently when applied to different problems is not yet available. Based on this assumption, this work proposes a hybrid metaheuristic algorithm for geometric and dimensional optimization of space trusses, called Simulated Squirrel Search Algorithm, which consists of an association of the well-established neighborhood shifting algorithm (Simulated Annealing) with a recently developed promising population algorithm (Squirrel Search Algorithm, or SSA). In this study, two models are tried, being respectively, a classical model from the literature (25-bar space truss) and a roof system composed of space trusses. The structures are subjected to resistance and displacement constraints. A penalty function using Fuzzy Logic (FL) is investigated. Comparative analyses are performed between the Squirrel Search Algorithm (SSSA) and other optimization methods present in the literature. The results obtained indicate that the proposed method can be competitive with other heuristics.

• The KIPS Transactions:PartB
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• v.14B no.7
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• pp.523-530
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• 2007

This paper proposes a new stochastic optimization algorithm for hidden Markov models (HMMs) used as a recognizer of automatic lipreading. The proposed method combines a global stochastic optimization method, the simulated annealing technique, and the local optimization method, which produces fast convergence and good solution quality. We mathematically show that the proposed algorithm converges to the global optimum. Experimental results show that training HMMs by the method yields better lipreading performance compared to the conventional training methods based on local optimization.