• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.15 no.4
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• pp.249-257
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• 2015

This paper introduces a heuristic algorithm to NP-hard travelling salesman problem. The proposed algorithm, in its bid to determine initial path, applies SW-DNN, DW-DNN, and DC-DNN, which are modified forms of the prevalent Double-sided Nearest Neighbor Search and searches the minimum value. As a part of its optimization process on the initial solution, it employs 2, 2.5, 3-opt of a local search k-opt on candidate delete edges and 4-opt on undeleted ones among them. When tested on TSP-1 of 26 European cities and TSP-2 of 49 U.S. cities, the proposed algorithm has successfully obtained optimal results in both, disproving the prevalent disbelief in the attainability of the optimal solution and making itself available as a general algorithm for the travelling salesman problem.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.15 no.4
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• pp.223-230
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• 2015

As the Transportation Simplex Method of the general transportation problem are inapplicable to the large-scale unbalanced transportation problem, a commercialized linear programming package remains as the only viable means. There is, however, no method made available to verify the optimality of solutions attained by the package. This paper therefore proposes a simple heuristic algorithm to the large-scale unbalanced transportation problem. From a given problem of $31{\times}15$supply and demand areas, the proposed algorithm determines the number of demands areas for each supply area and executes on the latter in the ascending order of each of their corresponding demand areas. Next, given a single corresponding demand area, it supplies the full demand volume and else, it supplies first to an area of minimum associated costs and subsequently to the rest so as to meet the demand to the fullest extent. This initial optimal value is then optimized through an adjustment process whereby costs are minimized as much as possible. When tested on the $31{\times}15$cost matrix, the proposed algorithm has obtained an optimal result improved from the commercial linear programming package by 8.9%.

• The Journal of Korean Association of Computer Education
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• v.5 no.3
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• pp.37-44
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• 2002

Automatic text classification is a task of assigning predefined categories to free text documents. Its importance is increased to organize and manage a huge amount of text data. There have been some researches on automatic text classification based on machine learning techniques. While most of them was focused on proposal of a new machine learning methods and cross evaluation between other systems, a through evaluation or optimization of a method has been rarely been done. In this paper, we propose an improving method of kNN-based Korean text classification system using heuristic informations about decision function, the number of nearest neighbor, and feature selection method. Experimental results showed that the system with similarity-weighted decision function, global method in considering neighbors, and DF/ICF feature selection was more accurate than simple kNN-based classifier. Also, we found out that the performance of the local method with well chosen k value was as high as that of the global method with much computational costs.

• Industrial Engineering and Management Systems
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• v.7 no.2
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• pp.126-132
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• 2008

The bin packing problem (BPP) is an NP-Complete Problem. The problem can be described as there are $N=\{1,2,{\cdots},n\}$ which is a set of item indices and $L=\{s1,s2,{\cdots},sn\}$ be a set of item sizes sj, where $0<sj{\leq}1$, ${\forall}j{\in}N$. The objective is to minimize the number of bins used for packing items in N into a bin such that the total size of items in a bin does not exceed the bin capacity. Assume that the bins have capacity equal to one. In the past, many researchers put on effort to find the heuristic algorithms instead of solving the problem to optimality. Then, the quality of solution may be measured by the asymptotic worst-case ratio or the average-case ratio. The First Fit Decreasing (FFD) is one of the algorithms that its asymptotic worst-case ratio equals to 11/9. Many researchers prove the asymptotic worst-case ratio by using the weighting function and the proof is in a lengthy format. In this study, we found an easier way to prove that the asymptotic worst-case ratio of the First Fit Decreasing (FFD) is not more than 11/9. The proof comes from two ideas which are the occupied space in a bin is more than the size of the item and the occupied space in the optimal solution is less than occupied space in the FFD solution. The occupied space is later called the weighting function. The objective is to determine the maximum occupied space of the heuristics by using integer programming. The maximum value is the key to the asymptotic worst-case ratio.

• Journal of the Korean Society for Library and Information Science
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• v.44 no.4
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• pp.155-178
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• 2010

Assessment of information credibility is a ubiquitous human activity given that people constantly make decisions and selections based on the value of information in a variety of information seeking and use contexts. Today, people are increasingly engaging in diverse online activities beyond searching for and reading information, including activities such as creating, tagging and rating content, shopping, and listening to and watching multimedia content. The Web 2.0 environment presents new challenges for people because the burden of information evaluation is shifted from professional gatekeepers to individual information consumers. At the same time, however, it also provides unprecedented opportunities for people to use tools and features that help them to make informed credibility judgments by relying on other people's ratings and recommendations. This paper introduces fundamental notions and dimensions of credibility, and contends that credibility assessment can be best understood with respect to human information behavior because it encompasses both the level of effort people exert as well as the heuristics they employ to evaluate information. The paper reports on a survey study investigating people's credibility judgments with respect to online information, focusing on the constructs, heuristics, and interactions involved in people's credibility assessment processes within the context of their everyday life information activities. Using an online activity diary method, empirical data about people's online activities and their associated credibility assessments were collected at multiple points throughout the day for three days. The results indicate that distinct credibility assessment heuristics are emerging as people engage in diverse online activities involving more user-generated and multimedia content. A heuristic approach suggests that people apply mental shortcuts or rules of thumb in order to minimize the amount of cognitive effort and time required to make credibility judgments. The paper discusses why a heuristic approach is key to reaching a more comprehensive understanding of people's credibility assessments within the information-abundant online environment.

• Journal for History of Mathematics
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• v.19 no.4
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• pp.63-80
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• 2006

Along with natural and algebraic languages, schema is a fundamental component of mathematical language. The principal purpose of this present study is to focus on this point in detail. Schema was already in use during Pythagoras' lifetime for making geometrical inferences. It was no different in the case of Oriental mathematics, where traces have been found from time to time in ancient Chinese documents. In schma an idea is transformed into something conceptual through the use of perceptive images. It's heuristic value lies in that it facilitates problem solution by appealing directly to intuition. Furthermore, introducing schema is very effective from an educational point of view. However we should keep in mind that proof is not replaceable by it. In this study, various schemata will be presented from a diachronic point of view, We will show with emaples from the theory of categories, Feynman's diagram, and argand's plane, that schema is an indispensable tool for constructing new knowledge.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.13 no.5
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• pp.173-181
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• 2013

This paper proposes a heuristic optimal solution of multicommodity transportation problem. The proposed algorithm has 3 steps. First the proposed algorithm transforms multicommodity transshipment problem to a general transportation problem, but if the problem is a multicommodity transportation problem, it is not transformed. And the multicommodity is disassembled to a single commodity. Second if it is a multicommodity transportation problem, the algorithm selects the minimum cost according to commodity, on the other hand if it is a multicommodity transshipment problem, the algorithm directly selects the minimum cost based on demand area. And the algorithm assigns carloadings to be satisfied the supply and demand quantity. The algorithm repeats these processes until a given demand quantity is satisfied. Last if it has a condition that is able to reduce the transportation expense, the proposed algorithm controls the assignment quantity of the initial value that got from the step 2. The proposed algorithm was applied to two multicommodity transportation problem and three multicommodity transshipment problem and it got more good result than an existing linear programming method.

• Journal of the Korea Society of Computer and Information
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• v.13 no.2
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• pp.51-57
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• 2008

We present a modeling of constraint satisfaction problems and provide heuristic algorithms of backtracking search to optimize the frequency assignment. Our research objective is to find a frequency assignment that satisfies all the constraints using minimum number of frequencies while maximizing the number of community radio stations served for a given area. In order to get a effective solution, some ordering heuristics such as variable orderings and value orderings are provided to minimize the backtracking in finding all solutions within a limited time. To complement the late detection of inconsistency in the backtracking, we provide the consistency enforcing technique or constraint propagation to eliminate the values that are inconsistent with some constraints. By integrating backtracking search algorithms with consistency enforcing techniques, it is possible to obtain more powerful and effective algorithms of constraint satisfaction problems. We also provide the performance evaluation of proposed algorithms by comparing the theoretical lower bound and our computed solution.

• Steel and Composite Structures
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• v.25 no.6
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• pp.663-670
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• 2017

The problem of layup optimization of the composite laminates involves a very complex multidimensional solution space which is usually non-exhaustively explored using different heuristic computational methods such as genetic algorithms (GA). To ensure the convergence to the global optimum of the applied heuristic during the optimization process it is necessary to evaluate a lot of layup configurations. As a consequence the analysis of an individual layup configuration should be fast enough to maintain the convergence time range to an acceptable level. On the other hand the mechanical behavior analysis of composite laminates for any geometry and boundary condition is very convoluted and is performed by computational expensive numerical tools such as finite element analysis (FEA). In this respect some studies propose very fast FEA models used in layup optimization. However, the lower bound of the execution time of FEA models is determined by the global linear system solving which in some complex applications can be unacceptable. Moreover, in some situation it may be highly preferred to decrease the optimization time with the cost of a small reduction in the analysis accuracy. In this paper we explore some machine learning techniques in order to estimate the failure of a layup configuration. The estimated response can be qualitative (the configuration fails or not) or quantitative (the value of the failure factor). The procedure consists of generating a population of random observations (configurations) spread across solution space and evaluating using a FEA model. The machine learning method is then trained using this population and the trained model is then used to estimate failure in the optimization process. The results obtained are very promising as illustrated with an example where the misclassification rate of the qualitative response is smaller than 2%.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.15 no.5
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• pp.299-307
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• 2022

Harmony Search (HS) is a relatively recently developed meta-heuristic optimization algorithm, and various studies have been conducted on it. HS is based on the musician's improvisational performance, and the objective variables play the role of the instrument. However, each instrument is given only a sound range, and there is no concept of a scale that can be said to be the basis of music. In this study, the performance of the algorithm is improved by introducing a scale to the existing HS and quantizing the bandwidth. The introduced scale was applied to HM initialization instead of the existing method that was randomly initialized in the sound band. The quantization step can be set arbitrarily, and through this, a relatively large bandwidth is used at the beginning of the algorithm to improve the exploration of the algorithm, and a small bandwidth is used to improve the exploitation in the second half. Through the introduction of scale and bandwidth quantization, it was possible to reduce the algorithm performance deviation due to the initial value and improve the algorithm convergence speed and success rate compared to the existing HS. The results of this study were confirmed by comparing examples of optimization values for various functions with the conventional method. Specific comparative values were described in the simulation.