• The Transactions of the Korean Institute of Electrical Engineers A
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• v.53 no.10
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• pp.556-564
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• 2004

This paper presents an application of parallel Genetic Algorithm-Tabu Search(GA-TS) algorithm to search an optimal solution of a reconfiguration in distribution system. The aim of the reconfiguration of distribution systems is to determine switch position to be opened for loss minimization in the radial distribution systems, which is a discrete optimization problem. This problem has many constraints and very difficult to solve the optimal switch position because it has many local minima. This paper develops parallel GA-TS algorithm for reconfiguration of distribution systems. In parallel GA-TS, GA operators are executed for each processor. To prevent solution of low fitness from appearing in the next generation, strings below the average fitness are saved in the tabu list. If best fitness of the GA is not changed for several generations, TS operators are executed for the upper 10% of the population to enhance the local searching capabilities. With migration operation, best string of each node is transferred to the neighboring node aster predetermined iterations are executed. For parallel computing, we developed a PC-cluster system consisting of 8 PCs. Each PC employs the 2 GHz Pentium Ⅳ CPU and is connected with others through ethernet switch based fast ethernet. To show the usefulness of the proposed method, developed algorithm has been tested and compared on a distribution systems in the reference paper. From the simulation results, we can find that the proposed algorithm is efficient and robust for the reconfiguration of distribution system in terms of the solution qualify. speedup. efficiency and computation time.

• KIEE International Transactions on Power Engineering
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• v.5A no.2
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• pp.116-124
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• 2005

This paper presents an application of the parallel Genetic Algorithm-Tabu Search (GA- TS) algorithm, and that is to search for an optimal solution of a reconfiguration in distribution systems. The aim of the reconfiguration of distribution systems is to determine the appropriate switch position to be opened for loss minimization in radial distribution systems, which is a discrete optimization problem. This problem has many constraints and it is very difficult to solve the optimal switch position because of its numerous local minima. This paper develops a parallel GA- TS algorithm for the reconfiguration of distribution systems. In parallel GA-TS, GA operators are executed for each processor. To prevent solution of low fitness from appearing in the next generation, strings below the average fitness are saved in the tabu list. If best fitness of the GA is not changed for several generations, TS operators are executed for the upper 10$\%$ of the population to enhance the local searching capabilities. With migration operation, the best string of each node is transferred to the neighboring node after predetermined iterations are executed. For parallel computing, we developed a PC-cluster system consisting of 8 PCs. Each PC employs the 2 GHz Pentium IV CPU and is connected with others through switch based rapid Ethernet. To demonstrate the usefulness of the proposed method, the developed algorithm was tested and is compared to a distribution system in the reference paper From the simulation results, we can find that the proposed algorithm is efficient and robust for the reconfiguration of distribution system in terms of the solution quality, speedup, efficiency, and computation time.

• Korean Journal of Cognitive Science
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• v.4 no.2
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• pp.181-200
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• 1994

In the learning phase of multilyer feedforword neural network,there are problems such that local minimum,learning praralysis and slow learning speed when backpropagation algorithm used.To overcome these problems, the genetic algorithm has been used as learing method in the multilayer feedforword neural network instead of backpropagation algorithm.However,because the genetic algorith, does not have any mechanism for fine-tuned local search used in backpropagation method,it takes more time that the genetic algorithm converges to a global optimal solution.In this paper,we suggest a new GA-BP method which provides a fine-tunes local search to the genetic algorithm.GA-BP method uses gradient descent method as one of genetic algorithm's operators such as mutation or crossover.To show the effciency of the developed method,we applied it to the 3-parity bit problem with analysis.

• Journal of the Korea Society of Computer and Information
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• v.28 no.12
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• pp.79-87
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• 2023

In this paper, we propose a novel genetic algorithm to reduce the overall span time by optimizing the skid insertion sequence in the shipyard subassembly process. We represented a solution by a permutation of a set of skid ids and applied genetic operators suitable for such a representation. In addition, we combined the genetic algorithm and the existing heuristic algorithm called UniDev which is properly modified to improve the search performance. In particular, the slow skid search part in UniDev was changed to a greedy algorithm. Through extensive large-scaled simulations, it was observed that the span time of our method was stably minimized compared to Multi-Start search and a genetic algorithm combined with UniDev.

• Journal of Korean Institute of Industrial Engineers
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• v.26 no.2
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• pp.183-192
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• 2000

This paper considers the problem of scheduling a set of n jobs on m parallel machines to minimize total weighted tardiness. For the problem a genetic algorithm is proposed, in which solutions are encoded using the random key method suggested by Bean and new crossover operators are employed to increase performance of the algorithm. The algorithm is compared with the Modified Due-Date (MDD) algorithm after series of tests to find appropriate values for genetic parameters. Results of computational tests on randomly generated test problems show that the suggested algorithm performs better than the MDD algorithm and gives good solutions in a reasonable amount of computation time.

• Proceedings of the Korea Society for Simulation Conference
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• 1998.10a
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• pp.142-146
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• 1998

In this paper, we suggests a WIP(work in process) of FMS analysis methods based on the Genetic algorithm. We conjoined both the assignment and the scheduling problem in order to create a new representation scheme for a chromosome and a mutation operators.

• Computers and Concrete
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• v.22 no.5
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• pp.493-500
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• 2018

Due to the fact that the ratio of their height to their openings is very large compared to normal beams, there are difficulties in the design and analysis of deep beams, which differ in behavior. In this study, the optimum horizontal and vertical reinforcement diameters of 5 different beams were determined by using genetic algorithms (GA) due to the openness/height ratio (L/h), loading condition and the presence of spaces in the body. In this study, the effect of different mutation operators and improved double times sensitive mutation (DTM) operator on GA's performance was investigated. In the study following random mutation (RM), boundary mutation (BM), non-uniform random mutation (NRM), Makinen, Periaux and Toivanen (MPT) mutation, power mutation (PM), polynomial mutation (PNM), and developed DTM mutation operators were applied to five deep beam problems were used to determine the minimum reinforcement diameter. The fitness values obtained using developed DTM mutation operator was higher than obtained from existing mutation operators. Moreover; obtained reinforcement weight of the deep beams using the developed DTM mutation operator lower than obtained from the existing mutation operators. As a result of the analyzes, the highest fitness value was obtained from the applied double times sensitive mutation (DTM) operator. In addition, it was found that this study, which was carried out using GAs, contributed to the solution of the problems experienced in the design of deep beams.

• Journal of the Korean Operations Research and Management Science Society
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• v.24 no.3
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• pp.27-37
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• 1999

In just-in-time production systems, U-shaped production lines rather than traditional straight lines are often adopted since they have some advantages. The advantages of U-lines over straight lines are that the workstations required can be reduced and the necessary number of workers can be easily adjusted when the demand rates are changed. In this paper, we present a new heuristic based on genetic algorithm to improve the workload smoothness in the U-line. In the proposed algorithm, a new genetic representation is developed which is specific to the problem being solved. To enhance the capability of searching good solutions, genetic operators are designed by using the problem-specific information and heuristics. Extensive experiments are carried out on well-known test-bed problems in the literature to verify the performance of our algorithm. The computational results show that our algorithm is a promising alternative to existing heuristics.

• Industrial Engineering and Management Systems
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• v.7 no.3
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• pp.204-213
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• 2008

Companies that produce capital goods need to schedule the production of products that have complex product structures with components that require many operations on different machines. A feasible schedule must satisfy operation and assembly precedence constraints. It is also important to avoid deadlock situations. In this paper a Genetic Algorithm (GA) has been developed that includes a new repair process that rectifies infeasible schedules that are produced during the evolution process. The algorithm was designed to minimise the combination of earliness and tardiness penalties and took into account finite capacity constraints. Three different sized problems were obtained from a collaborating capital goods company. A design of experimental approach was used to systematically identify that the best genetic operators and GA parameters for each size of problem.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2004.05a
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• pp.425-428
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• 2004

Flexible assembly line (FAL) is a production system that assembles various parts in unidirectional flow line with many constraints and manufacturing flexibilities. In this research we deal with a FAL balancing problem with the objective of minimizing the maximum workload allocated to the stations. However, almost all the existing researches do not appropriately consider various constraints due to the problem complexity. Therefore, this thesis addresses a balancing problem of FAL with many constraints and manufacturing flexibilities, unlike the previous researches. To solve this problem we use a genetic algorithm (GA). To apply GA to FAL, we suggest a genetic representation suitable for FAL balancing and devise evaluation method for individual's fitness and genetic operators specific to the problem, including efficient repair method for preserving solution feasibility. The experimental results are reported.