• Journal of the Korean Operations Research and Management Science Society
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• v.19 no.3
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• pp.219-233
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• 1994

A fixed k-coloring problem is introduced and dealt with by efficient heuristic algorithms. It is shown that the problem can be transformed into the graph partitioning problem. Initial coloring and improving methods are proposed for problems with and with and without the size restriction. Algorithm Move, LEE and OEE are developed by modifying the Kernighan-Lin's two way uniform partitioning procedure. The use of global information in the selection of the node and the color set made the proposed algorithms superior to the existing method. The computational result also shows that the superiority does not sacrifice the time demand of the proposed algorithms.

• Journal of Korean Institute of Industrial Engineers
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• v.12 no.1
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• pp.13-18
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• 1986

This paper is concerned with flow-shop permutation scheduling problem. This paper presents an algorithm for the minimum makespan sequence. The efficiency of proposed algorithm is demonstrated by comparisons with the existing algorithms: Johnson's, branch & bound method, and heuristic algorithms. The proposed algorithm is more effective than the other algorithms. A numerical example is given to illustrate the procedure.

• Proceedings of the KIEE Conference
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• 1997.07c
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• pp.1106-1108
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• 1997

This paper presents a new approach on genetic algorithms to economic dispatch problem for valve point discontinuities. Proposed approach in this paper on genetic algorithms improves the performance to solve economic dispatch problem for valve point discontinuities through combination in penalty function with death penalty, generation-apart elitism, atavism and heuristic crossover. Numerical results on an actual utility system consisted of 13 thermal units show that the proposed approach is faster and robuster than classical genetic algorithm.

• Journal of Information Processing Systems
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• v.16 no.5
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• pp.1196-1213
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• 2020

There is a growing interest in the development of smart environments through predicting the behaviors of inhabitants of smart spaces in the recent past. Various smart services are deployed in modern smart cities to facilitate residents and city administration. Prediction algorithms are broadly used in the smart fields in order to well equip the smart services for the future demands. Hence, an accurate prediction technology plays a vital role in the smart services. In this paper, we take out an extensive survey of smart spaces such as smart homes, smart farms and smart cars and smart applications such as smart health and smart energy. Our extensive survey is based on more than 400 articles and the final list of research studies included in this survey consist of 134 research papers selected using Google Scholar database for period of 2008 to 2018. In this survey, we highlight the role of prediction algorithms in each sub-domain of smart Internet of Things (IoT) environments. We also discuss the main algorithms which play pivotal role in a particular IoT subfield and effectiveness of these algorithms. The conducted survey provides an efficient way to analyze and have a quick understanding of state of the art work in the targeted domain. To the best of our knowledge, this is the very first survey paper on main categories of prediction algorithms covering statistical, heuristic and hybrid approaches for smart environments.

• The KIPS Transactions:PartD
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• v.14D no.2
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• pp.145-156
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• 2007

Recently, the needs for effective real time change detection algorithms for XML/HTML documents and increased in such fields as the detection of defacement attacks to web documents, the version management, and so on. Especially, those applications of real time change detection for large number of XML/HTML documents require fast heuristic algorithms to be used in real time environment, instead of algorithms which compute minimal cost-edit scripts. Existing heuristic algorithms are fast in execution time, but do not provide satisfactory edit script. In this paper, we present existing algorithms XyDiff and X-tree Diff, analyze their problems and propose algorithm X-tree Diff which improve problems in existing ones. X-tree Diff+ has similar performance in execution time with existing algorithms, but it improves matching ratio between nodes from two documents by refining matching process based on the notion of efficiency of matching.

• The KIPS Transactions:PartB
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• v.15B no.4
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• pp.341-346
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• 2008

Minimum Weight Triangulation (MWT) problem is an optimization problem searching for the triangulation of a given graph with minimum weight. Like many other graph problems this problem is also known to be NP-hard for general graphs. Several heuristic algorithms have been proposed for this problem including simulated annealing and genetic algorithm. In this paper, we propose a new genetic algorithm called GA-FF and show that the performance of the proposed genetic algorithm outperforms the previous one.

• Proceedings of the IEEK Conference
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• 2001.06c
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• pp.59-62
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• 2001

This paper discusses a data mining technique for time series analysis of traffic data, which provides useful knowledge for network configuration management. Commonly, a network designer must employ a combination of heuristic algorithms and analysis in an interactive manner until satisfactory solutions are obtained. The problem of heuristic algorithms is that it is difficult to deal with large networks and simplification or assumptions have to be made to make them solvable. Various data mining techniques are studied to gain valuable knowledge in large and complex telecommunication networks. In this paper, we propose a traffic pattern association technique among network nodes, which produces association rules of traffic fluctuation patterns among network nodes. Discovered rules can be utilized for improving network topologies and dynamic routing performance.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2004.05a
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• pp.409-412
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• 2004

Today, meta-heuristic algorithms have been much attention by researcher because they have the power of solving combinational optimization problems efficiently. As the result, many variants of a meta-heuristic algorithm (e.g., simulated annealing) have been proposed for specific application domains. However, there are few efforts to classify them into a unified software framework, which is believed to provide the users with the reusability of the software, thereby significantly reducing the development time of algorithms. In this paper, we present an object-oriented framework to be used as a general tool for efficiently developing variants of simulated annealing algorithm. The interface classes in the framework achieve the modulization of the algorithm, and the users are allowed to specialize some of the classes appropriate for solving their problems. The core of the framework is Algorithm Configuration Pattern (ACP) which facilitates creating user-specific variants flexibly. Finally, we summarize our experiences and discuss future research topics.

• Journal of Korean Institute of Industrial Engineers
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• v.32 no.3
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• pp.163-171
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• 2006

This paper considers a scheduling problem to minimize the sum of the associated scheduling (production/delivery times) cost and the delivery cost for an integrated system of a single production machine and various transportation vehicles with transportation mode selection allowed. Each transportation mode is provided with a fixed number of vehicles at the associated delivery time and cost. The proposed problem is characterized as being NP-hard. Some solution properties are also characterized. Therewith, three heuristic algorithms (called SPT-based, LWF-based and WSPT-based heuristic) and a branch-and-bound algorithm are derived. In order to evaluate the effectiveness and efficiency of the proposed algorithms, computational experiments are made with some numerical instances.

• Structural Engineering and Mechanics
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• v.53 no.5
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• pp.847-865
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• 2015

This paper presents the application of a recently developed meta-heuristic algorithm, called Colliding Bodies Optimization (CBO), for size and topology optimization of steel trusses. This method is based on the one-dimensional collisions between two bodies, where each agent solution is considered as a body. The performance of the proposed algorithm is investigated through four benchmark trusses for minimum weight with static and dynamic constraints. A comparison of the numerical results of the CBO with those of other available algorithms indicates that the proposed technique is capable of locating promising solutions using lesser or identical computational effort, with no need for internal parameter tuning.