• Proceedings of the KIEE Conference
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• 2000.07a
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• pp.75-79
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• 2000

This paper proposes the optimal placement with minimum set of Phasor Measurement Units (PMU's) using tabu search and makes an alternative plan to secure the robustness of the network with PMU's. The optimal PMU Placement (OPP) problem is generally expressed as a combinatorial optimization problem subjected to the observability constraints. Thus, it is necessary to make a use of an efficient method in solving the OPP problem. In this paper, a tabu search based approach to solve efficiently this OPP problem proposed. The observability of the network with PMU's is fragile at any single PMU contingency. To overcome the fragility, an alternative scheme that makes efficient use of the existing measurement system in power system state estimation proposed. The performance of the proposed approach and the alternative scheme is evaluated with IEEE sample systems.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2000.04a
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• pp.617-620
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• 2000

In this paper, we show an application of constraint logic programming to the operation scheduling on machines in a job shop. Constraint logic programming is a new genre of programming technique combining the declarative aspect of logic programming with the efficiency of constraint manipulation and solving mechanisms. Due to the latter feature, combinatorial search problems like scheduling may be resolved efficiently. In this study, the jobs that consist of a set of related operations are supposed to be constrained by precedence and resource availability. We also explore how the constraint solving mechanisms can be defined over a scheduling domain. Thus the scheduling approach presented here has two benefits: the flexibility that can be expected from an artificial intelligence tool by simplifying greatly the problem; and the efficiency that stems from the capability of constraint logic programming to manipulate constraints to prune the search space in an a priori manner.

• Proceedings of the KIEE Conference
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• 2002.07a
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• pp.124-126
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• 2002

The general capacitor placement problem is a combinatorial optimization problem having an objective function composed of power losses and capacitor installation costs subject to bus voltage constraints. In this paper, the method employing the chaos search algorithm is proposed to solve optimal capacitor placement problem with reducing computational effort and enhancing optimality of the solution. Chaos method in optimization problem searches the global optimal solution on the regularity of chaotic motions and easily escapes from local or near optimal solution than stochastic optimization algorithms. The chaos optimization method is tested on 9 buses and 69 buses system to illustrate the effectiveness of the proposed method.

• Proceedings of the KSME Conference
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• 2001.11a
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• pp.698-702
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• 2001

This paper presents a combinatorial method to compute the solutions of optimization problem. The present hybrid algorithm is the synthesis of an artificial life algorithm and the random tabu search method. The hybrid algorithm is not only faster than the conventional artificial life algorithm, but also gives a more accurate solution. In addition, this algorithm can find all global optimum solutions. And the enhanced artificial life algorithm is applied to optimum design of high-speed, short journal bearings and the usefuless is verified through this example.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.5
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• pp.748-754
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• 2008

The general capacitor placement problem is a combinatorial optimization problem having an objective function composed of power losses and capacitor installation costs subject to bus voltage constraints. In this paper, a global optimization technique, which employing the chaos search algorithm, is applied to solve optimal capacitor placement problem with reducing computational effort and enhancing global optimality of the solution. Chaos method in optimization problem searches the global optimal solution on the regularity of chaotic motions and easily escapes from local or near optimal solution than stochastic optimization algorithms. The chaos optimization method is tested on 9 buses and 69 buses system to illustrate the effectiveness of the proposed method.

• Journal of the military operations research society of Korea
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• v.35 no.3
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• pp.47-59
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• 2009

A hybrid Genetic Algorithm is applied to military resource allocation problem. Since military uses many resources in order to maximize its ability, optimization technique has been widely used for analysing resource allocation problem. However, most of the military resource allocation problems are too complicate to solve through the traditional operations research solution tools. Recent innovation in computer technology from the academy makes it possible to apply heuristic approach such as Genetic Algorithm(GA), Simulated Annealing(SA) and Tabu Search(TS) to combinatorial problems which were not addressed by previous operations research tools. In this study, a hybrid Genetic Algorithm which reinforces GA by applying local search algorithm is introduced in order to address military optimization problem. The computational result of hybrid Genetic Algorithm on Missile Interceptor Allocation problem demonstrates its efficiency by comparing its result with that of a simple Genetic Algorithm.

• Journal of the Korean Ceramic Society
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• v.40 no.12
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• pp.1170-1176
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• 2003

We developed an evolutionary optimization process involving a genetic algorithm and combinatorial chemistry (combi-chem), which was tailored exclusively for tile development of LED phosphors with a high luminescent efficiency, when excited by soft ultra violet irradiation. The ultimate goal of our study was to develop oxide red phosphors, which are suitable for three-band white Light Emitting Diodes (LED). To accomplish this, a computational evolutionary optimization process was adopted to screen a Eu$^{3+}$-doped alkali earth borosilicate system. The genetic algorithm is a well-known, very efficient heuristic optimization method and combi-chem is also a powerful tool for use in an actual experimental optimization process. Therefore the combination of a genetic algorithm and combi-chem would enhance the searching efficiency when applied to phosphor screening. Vertical simulations and an actual synthesis were carried out and promising red phosphors for three-band white LED applications, such as Eu$_{0.14}$Mg$_{0.18}$Ca$_{0.07}$Ba$_{0.12}$B$_{0.17}$Si$_{0.32}$O$_{\delta}$, were obtained.

• Journal of the Korea Society of Computer and Information
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• v.27 no.9
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• pp.69-76
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• 2022

The traveling salesman problem(TSP) is one of the most famous combinatorial optimization problem. So far, many metaheuristic search algorithms have been proposed to solve the problem, and one of them is local search. One of the very important factors in local search is neighbor generation method, and random-based neighbor generation methods such as inversion have been mainly used. This paper proposes 4 new greedy-based neighbor generation methods. Three of them are based on greedy insertion heuristic which insert selected cities one by one into the current best position. The other one is based on greedy rotation. The proposed methods are applied to first-choice hill-climbing search and simulated annealing which are representative local search algorithms. Through the experiment, we confirmed that the proposed greedy-based methods outperform the existing random-based methods. In addition, we confirmed that some greedy-based methods are superior to the existing local search methods.

• Journal of Electrical Engineering and Technology
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• v.6 no.2
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• pp.174-181
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• 2011

This paper proposes a novel binary ant colony optimization (NBACO) method. The proposed NBACO is based on the concept and principles of ant colony optimization (ACO), and developed to solve the binary and combinatorial optimization problems. The concept of conventional ACO is similar to Heuristic Dynamic Programming. Thereby ACO has the merit that it can consider all possible solution sets, but also has the demerit that it may need a big memory space and a long execution time to solve a large problem. To reduce this demerit, the NBACO adopts the state probability matrix and the pheromone intensity matrix. And the NBACO presents new updating rule for local and global search. The proposed NBACO is applied to test power systems of up to 100-unit along with 24-hour load demands.

• IE interfaces
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• v.10 no.2
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• pp.1-13
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• 1997

The problem of the formation of machine-part cells in FMS is a very important issue at the planning and operating stages of FMS. This problem is inherently a combinatorial optimization problem, proven to be NP-complete(or, NP-hard). Among the several kinds of approaches which have been applied to solve the combinatorial optimization problems, the Simulated Annealing(SA) algorithm, a technique of random search type with a flexibility in generating alternatives, is a powerful problem solving tool. In this paper, the SA algorithm is used to solve machine cell-part family formation problems. The primary purpose of the study is to find the near-optimal solution of machine cell-part family formation problem, whare the product volume and number of operations are prespecified, that can minimize the total material handling cost caused by exceptional elements and intercell moves as much as possible. The results show that the SA algorithm is able to find a near-optimal solution for practical problems of the machine cell-part family formation.