• Proceedings of the Korean Society of Precision Engineering Conference
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• 1997.04a
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• pp.589-594
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• 1997

We have already presented the constraint satisfaction algorithm which could solve the losed loop problem in constraint network by using local constraint propagation, variable elimination and constraint modularization. With this algorithm, we have implemented a knowledge-based system (intelligent CAD) for supporting machine design interactively. In this paper, we present newer constraint satisfaction algorithm which can solve inequalities or under-constrained problems in constraint network, interactively and efficiently. This algorithm is a hybrid type of using both declarative description (constraint represention) and optimization algorithm (Simulated Annealing), simultaneously. The under-constrained problems are represented by constraint networks and satisfied completely with this algorithm. The usefulness of our algorithm will be illustrated by the application to a gear design.

• 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.

• Journal of the Korean Society for Precision Engineering
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• v.14 no.9
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• pp.116-123
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• 1997

We have already presented the constraint satisfaction algorithm which could solve the closed loop porblem in constraint network by using local constraint propagation, variable elimination and constraint modularization. With this algorithm, we have implemented a knowledge-based system (intelligent CAD) for supporting machine design interactively. In this paper, we present newer constraint satisfaction algorithm which can solve inequalities or under-constrained problems in constraint network, interactively and effi- ciently. This algorithm is a hybrid type of using both declarative description (constraint representation) and optimization algorithm (Simulated Annealing), simultaneously. The under-constrained problems are represented by constraint networks and satisfied completely with this algorithm. The usefulness of our algorithm will be illustrated by the application to a gear design.

• 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.

• 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.

• Journal of the Korean Society of Marine Environment & Safety
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• v.5 no.2
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• pp.87-97
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• 1999

This study is concerned with developing a hybrid heuristic algorithm for solving the redundancy optimization problem which is very important in system safety, This study develops a HH(Hybrid Heuristic) method combined with two strategies to alleviate the risks of being trapped at a local optimum. One of them is to construct the populations of the initial solutions randomly. The other is the additional search with SA(Simulated Annealing) method in final step. Computational results indicate that HH performs consistently better than the KY method proposed in Kim[8]. Therefore, the proposed HH is believed to an attractive to other heuristic methods.

• Journal of Power Electronics
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• v.17 no.5
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• pp.1327-1337
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• 2017

Photovoltaic (PV) systems are influenced by disproportionate impacts on energy production caused by frequent mismatch cases. The occurrence of multiple maximum power points (MPPs) adds complexity to the tracking process in various PV systems. However, current maximum-power point tracking (MPPT) techniques exhibit limited performance. This paper introduces an enhanced simulated annealing (ESA)-based GMPPT technique against multiple MPP issues in P-V curve with different PV system structures. The proposed technique not only distinguishes global and local MPPs but also performs rapid convergence speed and high tracking accuracy of irradiance changing and restart capability detection. Moreover, the proposed global maximum power tracking algorithm can be applied in the central converter of DMPPT and hybrid PV system to meet various application scenarios. Its effectiveness is verified by simulation and test results.

• Journal of Convergence for Information Technology
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• v.11 no.8
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• pp.207-211
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• 2021

This paper addresses a two-stage flexible flow shop scheduling problem in which there is one machine in stage 1 and two identical machines in stage 2. The objective is the minimization of the total completion time. The problem is formulated by a mixed integer quadratic programming (MIQP) and a hybrid simulated annealing (HSA) is proposed to solve the MIQP. The HSA adopts the exploration capabilities of a genetic algorithm and incorporates a simulated annealing to reduce the premature convergence. Extensive computational tests on randomly generated problems are carried out to evaluate the performance of the HSA.

• 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.

• Journal of the Semiconductor & Display Technology
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• v.20 no.3
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• pp.57-64
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• 2021

The domestic semiconductor industry can produce various products that will satisfy customer needs by diversifying assembly parts and increasing compatibility between them. It is necessary to improve the production line as a method to reduce the work-in-process inventory (WIP) in the assembly line, the idle time of the worker, and the idle time of the process. The improvement of the production line is to balance the capabilities of each process as a whole, and to determine the timing of product input or the order of the work process so that the time required between each process is balanced. The purpose of this study is to find the optimal WIP and buffer size through SA (Simulated Annealing) that minimizes lead time while matching the number of two parts in a parallel assembly line with bottleneck process. The WIP level and buffer size obtained by the SA algorithm were applied to the CONWIP and DBR systems, which are the existing production systems, and the simulation was performed by applying them to the new hybrid production system. Here, the Hybrid method is a combination of CONWIP and DBR methods, and it is a production system created by setting new rules. As a result of the Simulation, the result values were derived based on three criteria: lead time, production volume, and work-in-process inventory. Finally, the effect of the hybrid production method was verified through comparative analysis of the result values.