• Transactions of the Korean Society of Mechanical Engineers
• /
• v.17 no.5
• /
• pp.1183-1192
• /
• 1993

Since the punching time is strongly related to the productivity in sheet metal stamping, there have been a lot of efforts to obtain the optimal tool path. However, most of the conventional efforts have the basic limitations to provide the global optimal solution because of the inherent difficulties of the NP hard combinatorial optimization problem. The existing methods search the optimal tool path with limiting tool changes to the minimal number, which proves not to be a global optimal solution. In this work, the turret rotation time is also considered in addition to the bed translation time of the NCT machine, and the total punching time is minimized by the simulated annealing algorithm. Some manufacturing constraints in punching sequences such as punching priority constraint and punching accuracy constraint are incorporated automatically in optimization, while several user-interactions to edit the final tool path are usually required in commercial systems.

• Journal of Advanced Marine Engineering and Technology
• /
• v.40 no.2
• /
• pp.138-145
• /
• 2016

This paper proposes an improved tabu search method for subset selection in multiple linear regression models. Variable selection is a vital combinatorial optimization problem in multivariate statistics. The selection of the optimal subset of variables is necessary in order to reliably construct a multiple linear regression model. Its applications widely range from machine learning, timeseries prediction, and multi-class classification to noise detection. Since this problem has NP-complete nature, it becomes more difficult to find the optimal solution as the number of variables increases. Two typical metaheuristic methods have been developed to tackle the problem: the tabu search algorithm and hybrid genetic and simulated annealing algorithm. However, these two methods have shortcomings. The tabu search method requires a large amount of computing time, and the hybrid algorithm produces a less accurate solution. To overcome the shortcomings of these methods, we propose an improved tabu search algorithm to reduce moves of the neighborhood and to adopt an effective move search strategy. To evaluate the performance of the proposed method, comparative studies are performed on small literature data sets and on large simulation data sets. Computational results show that the proposed method outperforms two metaheuristic methods in terms of the computing time and solution quality.

• The KIPS Transactions:PartB
• /
• v.10B no.3
• /
• pp.237-242
• /
• 2003

Ant Colony System (ACS) Algorithm is new meta heuristic for hard combinatorial optimization problem. It is a population based approach that uses exploitation of positive feedback as well as greedy search. It was first proposed for tackling the well known Traveling Salesman Problem (TSP). In this paper, we introduce ACS of new method that adds reinforcement value for each edge that visit to Local/Global updating rule. and the performance results under various conditions are conducted, and the comparision between the original ACS and the proposed method is shown. It turns out that our proposed method can compete with tile original ACS in terms of solution quality and computation speed to these problem.

• Proceedings of the Korean Operations and Management Science Society Conference
• /
• 2005.05a
• /
• pp.768-772
• /
• 2005

TSP(Traveling Salesman Problem) has been a nagging NP-complete problem to test almost every algorithmic idea in combinatorial optimization in vain. The main bottleneck is how to get the integer results {0,1} and to avoid sub-tours. We suggest simple and practical method in two steps. Firstly for every node, an initial Hamiltonian cycle us produced on the nearest neighbour concept. The node with nearest distance is to be inserted to form a increased feasible cycle. Secondly we improve the initial solution by exchanging 2 cuts of the grand tours. We got practical results within 1 from the optimum in 30 minutes for up to 200 nodes problems. TSP of real world type might be tackled practically in our formulation.

• The KIPS Transactions:PartB
• /
• v.16B no.3
• /
• pp.195-202
• /
• 2009

In this paper, we propose the regions segmentation method of the white matter and the gray matter for brain MR image by using the ant colony optimization algorithm. Ant Colony Optimization (ACO) is a new meta heuristics algorithm to solve hard combinatorial optimization problem. This algorithm finds the expected pixel for image as the real ant finds the food from nest to food source. Then ants deposit pheromone on the pixels, and the pheromone will affect the motion of next ants. At each iteration step, ants will change their positions in the image according to the transition rule. Finally, we can obtain the segmentation results through analyzing the pheromone distribution in the image. We compared the proposed method with other threshold methods, viz. the Otsu' method, the genetic algorithm, the fuzzy method, and the original ant colony optimization algorithm. From comparison results, the proposed method is more exact than other threshold methods for the segmentation of specific region structures in MR brain image.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.35 no.5
• /
• pp.415-422
• /
• 2017

Finding influential factors from given clustering result is a typical data science problem. Genetic Algorithm based method is proposed to derive influential factors and its performance is compared with two conventional methods, Classification and Regression Tree (CART) and Chi-Squared Automatic Interaction Detection (CHAID), by using Dunn's index measure. To extract the influential factors of preference towards political parties in South Korea, the vote result of $18^{th}$ presidential election and 'Demographic', 'Health and Welfare', 'Economic' and 'Business' related data were used. Based on the analysis, reverse engineering was implemented. Implementation of reverse engineering based approach for influential factor analysis can provide new set of influential variables which can present new insight towards the data mining field.

• Journal of the Korean Operations Research and Management Science Society
• /
• v.31 no.3
• /
• pp.1-10
• /
• 2006

In this paper we deal with a cell planning issue arisen in a CDMA based multicast network. In a CDMA based wireless network, a terminal can significantly reduce the bit error rate via the cohesion of data streams from multiple base stations. In this case, multiple base stations have to be operated according to a common time line. The cells whose base stations are operated as such are called soft-combined cells. Therefore, a terminal can take advantage of error rate reduction, if the terminal is in a soft-combined cell and at least one neighboring cell is also soft-combined. However, as soft-combining operation gives heavy burden to the network controller, the limited number of cells can be soft-combined. Our problem us to find a limited number of soft-combined cells such that the benefit of the soft-combining operation is maximized.

• Proceedings of the KIEE Conference
• /
• 2006.07a
• /
• pp.216-217
• /
• 2006

This paper presents development of program for generator maintenance scheduling. The maintenance scheduling of generating units is a dynamic discrete combinatorial optimization problem with constraints to determine the optimal maintenance periods of each generating units for a given planning periods. The developed program is designed so as to provide the maintenance schedule satisfying the operating reserve margin levelization and the procurement of proper reliability. In order to verify the effectiveness of the developed program, the numerical study has been performed with the practical data in 2005.

• Proceedings of the Korea Society for Simulation Conference
• /
• 2001.10a
• /
• pp.365-370
• /
• 2001

The genetic algorithm that simulates the virus evolutionary theory has been developed applying to combinatorial optimization problems. The algorithm in this study uses only one individual and a population of viruses. The individual is attacked, inflected and improved by the viruses. The viruses are composed of flour genes (a pair of top gene and a pair of tail gene). If the individual is improved by the attacking, the inflection occurs. After the infection, the tail genes are mutated. If the same virus attacks several times and fails to inflect, the top genes of the virus are mutated. By this mutation, the individual can be improved effectively. In addition, the influence of the immunologic mechanism on evolution is simulated.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.35 no.1
• /
• pp.140-147
• /
• 2012

Vehicle routing problem is one of the traveling salesman problems with various conditions such as vehicle capacity limits, delivery time windows, as well as time dependent speeds in metropolitan area. In this research hourly vehicle moving speeds information in a typical metropolitan area are analyzed to use the results in the design procedure of VRP heuristic. Quality initial vehicle routing solutions can be obtained with adaption of the analysed results of the time periods with no vehicle speed changes. This strategy makes complicated time dependent vehicle speed simple to solve. Time dependent vehicle speeds are too important to ignore to obtain optimum vehicle routing search for real life logistics systems.