• Journal of Software Engineering Society
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• v.24 no.2
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• pp.35-45
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• 2011

Simulated annealing has been studied a long times. And it is one of the effective techniques for test data generation. But basic SA methods showed bad performance because of neighborhood selection strategies in the case of large input domain. To overcome this limitation, we propose new neighborhood selection approach, Branch Distance. We performs case studies based on the proposed approach to evaluate it's performance and to compare it whit basic SA and Random test generation. The results of the case studies appear that proposed approach show better performance than the other approach.

• Journal of Korea Game Society
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• v.19 no.4
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• pp.97-108
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• 2019

Recently interests in voice game commands have been increasing due to the diversity and convenience of the input method, but also by the distance between commands. The command distance is the phonetic difference between command utterances, and as such distance increases, the recognition rate improves. In this paper, we propose an IP(Integer Programming) modeling of the problem which is to select a combination of commands from given candidate commands for maximizing the average distance. We also propose a SA(Simulated Annealing)-based algorithm for solving the problem. We analyze the characteristics of our method using experiments under various conditions such as the number of commands, allowable command length, and so on.

• The KIPS Transactions:PartB
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• v.14B no.7
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• pp.541-548
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• 2007

Due to the mathematical convergence property, Simulated Annealing (SA) has been one of the most popular optimization algorithms. However, because of its problem of slow convergence in the practical use, many variations of SA like Fast SA (FSA) have been developed for faster convergence. In this paper, we propose and prove that Greedy SA (GSA) also finds the global optimum in probability in the continuous space optimization problems. Because the greedy selection does not allow the cost to become worse, GSA is expected to have faster convergence than the conventional FSA that uses Metropolis selection. In the computer simulation, the proposed method is shown to have as good performance as FSA with Metropolis selection in the viewpoints of the convergence speed and the quality of the found solution. Furthermore, the greedy selection does not concern the cost value itself but uses only dominance of the costs of solutions, which makes GSA invariant to the problem scaling.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.10B
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• pp.973-980
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• 2009

Many studies which have been done for efficient installation and management of wireless sensor networks (WSN) include energy savings, key managements and sensor deployments. Sensor deployment problem is one of the most important and fundamental issues among them in that the topic is directly related with the system cost and performance. In this paper, we suggest a sensor deployment scheme that reduces the system cost of WSN while satisfying the fundamental system requirements of connectivity between sensor nodes and sensing coverage. Using graphical information system(GIS) which contains region-dependent information related with connectivity condition, the initial positions of sensors in the procedure simulated annealing (SA) are determined. The GIS information helps in reducing system cost reduction not only at the initial deployment of SA but also at the final deployment of SA which is shown by computer simulations.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.535-538
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• 2003

We employ Genetic Programming (GP) which is optimized with Simulated Annealing (SA) to recognize characteristic of a plan. Its result is described in Laplace function. The algorithm proceeds with automatic PID designs for the plant.

• Journal of applied mathematics & informatics
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• v.16 no.1_2
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• pp.337-354
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• 2004

SA, TS, GA and ACS are four of the main algorithms for solving challenging problems of intelligent systems. In this paper we consider Examination Timetabling Problem that is a common problem for all universities and institutions of higher education. There are many methods to solve this problem, In this paper we use Simulated Annealing, Tabu Search, Genetic Algorithm and Ant Colony System in their basic frameworks for solving this problem and compare results of them with each other.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.8
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• pp.3516-3541
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• 2018

Task scheduling is one of the most challenging aspects of cloud computing nowadays, and it plays an important role in improving overall performance in, and services from, the cloud, such as response time, cost, makespan, and throughput. A recent cloud task-scheduling algorithm based on the symbiotic organisms search (SOS) algorithm not only has fewer specific parameters, but also incurs time complexity. SOS is a newly developed metaheuristic optimization technique for solving numerical optimization problems. In this paper, the basic SOS algorithm is reduced, and chaotic local search (CLS) is integrated into the reduced SOS to improve the convergence rate. Simulated annealing (SA) is also added to help the SOS algorithm avoid being trapped in a local minimum. The performance of the proposed SA-CLS-SOS algorithm is evaluated by extensive simulation using the Matlab framework, and is compared with SOS, SA-SOS, and CLS-SOS algorithms. Simulation results show that the improved hybrid SOS performs better than SOS, SA-SOS, and CLS-SOS in terms of convergence speed and makespan.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.42 no.1
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• pp.8-20
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• 2019

The vehicle routing problem is one of the vibrant research problems for half a century. Many studies have extensively studied the vehicle routing problem in order to deal with practical decision-making issues in logistics. However, developments of new logistics strategies have inevitably required investigations on solution methods for solving the problem because of computational complexity and inherent constraints in the problem. For this reason, this paper suggests a simulated annealing (SA) algorithm for a variant of vehicle routing problem introduced by a previous study. The vehicle routing problem is a multi-depot and multi-trip vehicle routing problem with multiple heterogeneous vehicles restricted by the maximum permitted weight and the number of compartments. The SA algorithm generates an initial solution through a greedy-type algorithm and improves it using an enhanced SA procedure with three local search methods. A series of computational experiments are performed to evaluate the performance of the heuristic and several managerial findings are further discussed through scenario analyses. Experiment results show that the proposed SA algorithm can obtain good solutions within a reasonable computation time and scenario analyses show that a transportation system visiting non-dedicated factories shows better performance in truck management in terms of the numbers of vehicles used and trips for serving customer orders than another system visiting only dedicated factories.

• The Korean Journal of Applied Statistics
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• v.22 no.3
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• pp.627-634
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• 2009

Recently, the general contour Monte Carlo has been proposed by Liang (2004) as a space annealing version(ACMC) for optimization problems. The algorithm can be applied successfully to determine the ground configurations for the prediction of protein folding. In this approach, we use the distances between the consecutive $C_{\alpha}$ atoms along the peptide chain and the mapping sequences between the 20-letter amino acids and a coarse-grained three-letter code. The algorithm was tested on the real proteins. The comparison showed that the algorithm made a significant improvement over the simulated annealing(SA) and the Metropolis Monte Carlo method in determining the ground configurations.

• Journal of Electrical Engineering and Technology
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• v.7 no.4
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• pp.493-500
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• 2012

In this paper the performance of meta-heuristics algorithms such as GA (Genetic Algorithm), DE (Differential Evolution), PSO (Particle Swarm Optimization) and SA (Simulated Annealing) for the problem of TTC enhancement using FACTS devices are compared. In addition to that in the assessment procedure of TTC two novel techniques are proposed. First the optimization algorithm which is used for TTC enhancement is simultaneously used for assessment of TTC. Second the power flow is done using Broyden - Shamanski method with Sherman - Morrison formula (BSS). The proposed approach is tested on WSCC 9 bus, IEEE 118 bus test systems and the results are compared with the conventional Repeated Power Flow (RPF) using Newton Raphson (NR) method which indicates that the proposed method provides better TTC enhancement and computational efficacy than the conventional procedure.