• Journal of Computing Science and Engineering
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• v.8 no.4
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• pp.199-206
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• 2014

The ant colony optimization (ACO) algorithm is a new heuristic algorithm that offers good robustness and searching ability. With in-depth exploration, the ACO algorithm exhibits slow convergence speed, and yields local optimization solutions. Based on analysis of the ACO algorithm and the genetic algorithm, we propose a novel hybrid genetic ant colony optimization (NHGAO) algorithm that integrates multi-population strategy, collaborative strategy, genetic strategy, and ant colony strategy, to avoid the premature phenomenon, dynamically balance the global search ability and local search ability, and accelerate the convergence speed. We select the traveling salesman problem to demonstrate the validity and feasibility of the NHGAO algorithm for solving complex optimization problems. The simulation experiment results show that the proposed NHGAO algorithm can obtain the global optimal solution, achieve self-adaptive control parameters, and avoid the phenomena of stagnation and prematurity.

• Journal of Korean Institute of Industrial Engineers
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• v.31 no.1
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• pp.10-19
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• 2005

The minimum spanning tree (MST) problem is one of the traditional optimization problems. Unlike the MST, the degree constrained minimum spanning tree (DCMST) of a graph cannot, in general, be found using a polynomial time algorithm. So, finding the DCMST of a graph is a well-known NP-hard problem of importance in communications network design, road network design and other network-related problems. So, it seems to be natural to use evolutionary algorithms for solving DCMST. Especially, when applying an evolutionary algorithm to spanning tree problems, a representation and search operators should be considered simultaneously. This paper introduces a new tree representation scheme and a genetic operator for solving combinatorial tree problem using evolutionary algorithms. We performed empirical comparisons with other tree representations on several test instances and could confirm that the proposed method is superior to other tree representations. Even it is superior to edge set representation which is known as the best algorithm.

• Korean Management Science Review
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• v.29 no.3
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• pp.107-120
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• 2012

In this paper we propose an effective genetic algorithm for solving the integrated inventory and routing problem of supply chain composed of multi-warehouses and multi-retailers. Unlike extant studies dealing with integrated inventory and routing problem of supply chain, our model incorporates more realistic aspect such as positive inventory at the multi-warehouses under the assumption of inventory policy of power of two-replenishment-cycle. The objective is to determine replenishment intervals for the retailers and warehouses as well as the vehicles routes so that the total cost of delivery and inventory cost is minimized. A notable feature of our algorithm is that the procedure for evaluating the fitness of objective function has the computational complexity closing to linear function. Computational results show effectiveness of our algorithm.

• Journal of Korean Institute of Industrial Engineers
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• v.17 no.2
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• pp.131-142
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• 1991

The concept of criterion function is proposed as a framework for comparing the geometric and computational characteristics of various nonlinear programming approaches to linear programming such as the method of centers, Karmakar's algorithm and the gravitational method. Also, we discuss various computational issues involved in obtaining an efficient parallel implementation of these methods. Clearly, the most time consuming part in solving a linear programming problem is the direction finding procedure, where we obtain an improving direction. In most cases, finding an improving direction is equivalent to solving a simple optimization problem defined at the current feasible solution. Again, this simple optimization problem can be seen as a least squares problem, and the computational effort in solving the least squares problem is, in fact, same as the effort as in solving a system of linear equations. Hence, getting a solution to a system of linear equations fast is very important in solving a linear programming problem efficiently. For solving system of linear equations on parallel computing machines, an iterative method seems more adequate than direct methods. Therefore, we propose one possible strategy for getting an efficient parallel implementation of an iterative method for solving a system of equations and present the summary of computational experiment performed on transputer based parallel computing board installed on IBM PC.

• Education of Primary School Mathematics
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• v.12 no.1
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• pp.1-20
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• 2009

The purposes of this study are to analyze elementary school student's intuitive thinking in the process of mathematical problem solving and to analyze elementary school student's errors of intuitive thinking in the process of mathematical problem solving. According to these purposes, the research questions can be set up as followings. (1) How is the state of illumination of the elementary school student's intuitive thinking in the process of mathematical problem solving? (2) What are origins of errors by elementary school student's intuitive thinking in the process of mathematical problem solving? In this study, Bogdan & Biklen's qualitative research method were used. The subjects in this study were 4 students who were attending the elementary school. The data in this study were 'Intuitine Thinking Test', records of observation and interview. In the interview, the discourses were recorded by sound and video recording. These were later transcribed and analyzed in detail. The findings of this study were as follows: First, If Elementary school student Knows the algorithm of problem, they rely on solving by algorithm rather than solving by intuitive thinking. Second, their problem solving ability by intuitive model are low. What is more they solve the problem by Intuitive model, their Self- Evidence is low. Third, in the process of solving the problem, intuitive thinking can complement logical thinking. Last, in the concept of probability and problem of probability, they are led into cognitive conflict cause of subjective interpretation.

• Industrial Engineering and Management Systems
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• v.8 no.1
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• pp.22-28
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• 2009

We consider facility layout problems, where mn facility units are assigned into mn cells. These cells are arranged into a rectangular pattern with m rows and n columns. In order to solve this cell type facility layout problem, many approximation algorithms with improved local search methods were studied because it was quite difficult to find exact optimum of such problem in case of large size problem. In this paper, new algorithms based on Simulated Annealing (SA) method with two neighborhood generation methods are proposed. The new neighborhood generation method adopts the exchanging operation of facility units in accordance with adjacent preference. For evaluating the performance of the neighborhood generation method, three algorithms, previous SA algorithm with random 2-opt neighborhood generation method, the SA-based algorithm with the new neighborhood generation method (SA1) and the SA-based algorithm with probabilistic selection of random 2-opt and the new neighborhood generation method (SA2), are developed and compared by experiment of solving same example problem. In case of numeric examples with problem type 1 (the optimum layout is given), SA1 algorithm could find excellent layout than other algorithms. However, in case of problem type 2 (random-prepared and optimum-unknown problem), SA2 was excellent more than other algorithms.

• Education of Primary School Mathematics
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• v.3 no.1
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• pp.63-77
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• 1999

We introduce what is the meaning of problem and problem solving and also different type of problems and problem-solving strategies were discussed in this paper, with suggestions for teaching both Polya's four-step strategy and specific problem solving strategies. Many real and concrete examples of routine and nonroutine problems in elementary school mathematics are introduced. Especially, we have researched on the actual condition how children in elementary school think about multiplication of fraction for the routine problem. As a result, we have noticed that children have diverse thinking in their own way and also concrete expressions are much better effective than algorithm showing in textbook.

• 대한공업교육학회지
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• v.33 no.2
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• pp.170-191
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• 2008

Many former researchers demonstrated that algorithm learning has a positive outcome on students' problem-solving abilities. One of the methods for algorithm learning, the 'programming learning' method is highly effective. However, there are numerous constraints in schools for programming learning. This study attempts to overcome these issues. Squeak Etoys, one of the educational visual programming languages for easy and interesting learning, has been selected as a learning tool. We developed the algorithm-learning curriculum for middle school students. They were divided into a control group and an experimental group. The students learned on the basis of equal curriculum but, they used other learning tools through over a total 6 sessions. The result showed that Squeak Etoys based Algorithm learning has a positive effect on improving middle school learners' problem solving abilities, self-efficacies and logical thinking abilities. Although the students' logical thinking abilities in the experimental group are improved a lot more than the students' abilities in control group, the students' logical think abilities in the both groups are improved. Therefore, algorithm education in secondary schools are necessary. In conclusion, Squeak Etoys based Algorithm learning has a positive effect on problem solving ability and self efficacy. The developed curriculum can be applicable as a basis for study on algorithm learning and educational programming language.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.4 no.2
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• pp.99-110
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• 2000

This paper is devoted to the parallelism of a numerical matrix eigenvalue problem. The eigenproblem arises in a variety of applications, including engineering, statistics, and economics. Especially we try to approach the industrial techniques from mathematical modeling. This paper has developed a parallel algorithm to find all eigenvalues. It is contributed to solve a specific practical problem, a vibration problem in the industry. Also we compare the runtime between the serial algorithm and the parallel algorithm for the given problems.

• International Journal of Computer Science & Network Security
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• v.23 no.6
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• pp.193-201
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• 2023

In this paper, we consider the maximum scatter traveling salesman problem (MSTSP), a travelling salesman problem (TSP) variant. The problem aims to maximize the minimum length edge in a salesman's tour that travels each city only once in a network. It is a very complicated NP-hard problem, and hence, exact solutions can be found for small sized problems only. For large-sized problems, heuristic algorithms must be applied, and genetic algorithms (GAs) are found to be very successfully to deal with such problems. So, this paper develops a hybrid GA (HGA) for solving the problem. Our proposed HGA uses sequential sampling algorithm along with 2-opt search for initial population generation, sequential constructive crossover, adaptive mutation, randomly selected one of three local search approaches, and the partially mapped crossover along with swap mutation for perturbation procedure to find better quality solution to the MSTSP. Finally, the suggested HGA is compared with a state-of-art algorithm by solving some TSPLIB symmetric instances of many sizes. Our computational experience reveals that the suggested HGA is better. Further, we provide solutions to some asymmetric TSPLIB instances of many sizes.