• Journal of the Korea Society of Computer and Information
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• v.6 no.1
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• pp.25-30
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• 2001

This paper proposes an enhanced Simulated Annealing(SA) algorithm for Rural Postman Problems(RPPs). In SA, the cooling schedule is an important factor for SA algorithms. Hence, in this paper a cooling schedule is proposed for SA for RPPs. In the simulation. the results of the SA using the proposed cooling schedule and the results of the SA using the existing cooling schedules are compared and analyzed. In the simulation results, the proposed method obtained the better results than the existing methods.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.256-256
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• 2000

In this parer, we present a real-time path planning algorithm which is integrated the artificial potential field(APF) and simulated annealing(SA) methods for mobile robot. The APF method in path planning has gained popularity since 1990's. It doesn't need the modeling of the complex configuration space of robot, and is easy to apply the path planning with simple computation. However, there is a major problem with APF method. It is the formation of local minima that can trap the robot before reaching its goal. So, to provide local minima recovery, we apply the SA method. The effectiveness of the proposed algorithm is verified through simulation.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.30 no.3
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• pp.44-53
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• 2007

Forming central warehouses for a number of stores can save costs in the continuous review inventory model due to economy of scale and information sharing. In this paper, transportation costs are included in this inventory model. Hence, the tradeoff between inventory-related costs and transportation costs is required. The main concern of this paper is to determine the number and location of central warehouses. Transportation costs are dependent on the distance from several central warehouses to each store. Hence, we develop an efficient simulated annealing algorithm using distance-based local search heuristic and merging heuristic to determine the location and the number of central warehouses. The objective of this paper is to minimize total costs such as holding, setup, penalty, and transportation costs. The performance of the proposed approach is tested by using some computational experiments.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.15 no.8 s.101
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• pp.911-917
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• 2005

There were many attempts to reduce noise behind the noise barrier using active control techniques. Omoto(1993) Shao(1997) and Yang(2001) tried to actively control the diffracted noise behind the barrier and main concerns were about the arrangement methods for the control sources. Baek (2004) tried to get better results using the simulated annealing method and the sequential searching technique. The main goal of this study is to develop and compare the performance of several optimization techniques including those mentioned above, hybrid version of simulated annealing and genetic algorithm for the optimal control source positions of active noise barrier system. The simulation results show fairly similar performance lot the small size of searching problem. However, as the number of control sources are increased, the performance of simulated annealing algorithm and genetic algorithm are better than the others. Simulations are also made to show the performance of the selected optimal control source positions not only at the receiver position but at the surrounding volume of the receiver position and plotted the noise reduction level in 3-D.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.2
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• pp.287-295
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• 2010

In this paper, we introduce a novel approach to optimization algorithm which is a distributed Mean field Genetic algorithm (MGA) implemented in MPI(Message Passing Interface) environments. Distributed MGA is a hybrid algorithm of Mean Field Annealing(MFA) and Simulated annealing-like Genetic Algorithm(SGA). The proposed distributed MGA combines the benefit of rapid convergence property of MFA and the effective genetic operations of SGA. The proposed distributed MGA is applied to the channel routing problem, which is an important issue in the automatic layout design of VLSI circuits. Our experimental results show that the composition of heuristic methods improves the performance over GA alone in terms of mean execution time. It is also proved that the proposed distributed algorithm maintains the convergence properties of sequential algorithm while it achieves almost linear speedup as the problem size increases.

• International Journal of Control, Automation, and Systems
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• v.3 no.2
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• pp.211-216
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• 2005

In electrical impedance tomography (EIT), various image reconstruction algorithms have been used in order to compute the internal resistivity distribution of the unknown object with its electric potential data at the boundary. Mathematically, the EIT image reconstruction algorithm is a nonlinear ill-posed inverse problem. This paper presents a simulated annealing technique as a statistical reconstruction algorithm for the solution of the static EIT inverse problem. Computer simulations with 32 channels synthetic data show that the spatial resolution of reconstructed images by the proposed scheme is improved as compared to that of the mNR algorithm at the expense of increased computational burden.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2002.04a
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• pp.687-692
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• 2002

This paper reviews the existing methods to divide the gear ratios of a multi-stage gear train, and proposes a simulated annealing-based algorithm to find the best gear ratios. The existing methods have their own limitations to be used in practical design, and are also problematic to be automated in a design system. However, the proposed algorithm is a general one which can be applied to gear trains having any number of stages, and offers a satisfactory result in a very short time. It is expected to be useful as a design sub-module of the design system for multi-stage gear drives.

• Journal of Institute of Control, Robotics and Systems
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• v.9 no.2
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• pp.168-175
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• 2003

A new algorithm based on Genetic Algorithms is proposed f3r solving process optimization problems formulated in MINLP, GDP and hybrid MINLP/GDP. This work is focused 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 experimented and many heuristics are adopted. Real and binary coded Genetic Algorithm initiates the global search in the entire search space and at every stage Simulated Annealing makes the candidates to climb up the local hills. Multi-Niche Crowding method is adopted as the multimodal function optimization technique. and the adaptation of probabilistic parameters and dynamic penalty systems are also implemented. New strategies to take the logical variables and constraints into consideration are proposed, as well. Various test problems selected from many fields of process systems engineering are tried and satisfactory results are obtained.

• Journal of Fisheries and Marine Sciences Education
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• v.29 no.2
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• pp.544-551
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• 2017

Metaheuristic algorithms are efficient techniques for a class of mathematical optimization problems without having to deeply adapt to the inherent nature of each problem. They are very useful for structural design optimization in which the cost of gradient computation can be very expensive. Among them, the characteristics of simulated annealing and genetic algorithms are briefly discussed. In Metropolis genetic algorithm, favorable features of Metropolis criterion in simulated annealing are incorporated in the reproduction operations of simple genetic algorithm. Numerical examples of structural design optimization are presented. The example structures are truss, breakwater and steel box girder bridge. From the theoretical evaluation and numerical experience, performance and applicability of metaheuristic algorithms for structural design optimization are discussed.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.50 no.1
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• pp.28-37
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• 2001

This paper presents an efficient algorithm for the loss minimization of distribution system by automatic sectionalizing switch operation in large scale distribution systems. Simulated annealing is particularly well suited for large combinational optimization problem, but the use of this algorithm is also responsible for an excessive computation time requirement. Tabu search attempts to determine a better solution in the manner of a greatest-descent algorithm, but it can not give any guarantee for the convergence property. The hybrid algorithm of two methods with two tabu lists and the proposed perturbation mechanism is applied to improve the computation time and convergence property Numerical examples demonstrate the validity and effectiveness of the proposed methodology using a KEPCO's distribution system.