• Proceedings of the Korean Information Science Society Conference
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• 2005.07a
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• pp.892-894
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• 2005

Graph partitioning provides an important tool for data clustering, but is an NP-hard combinatorial optimization problem. Spectral clustering where the clustering is performed by the eigen-decomposition of an affinity matrix [1,2]. This is a popular way of solving the graph partitioning problem. On the other hand, semidefinite relaxation, is an alternative way of relaxing combinatorial optimization. issuing to a convex optimization[4]. In this paper we present a semidefinite programming (SDP) approach to graph equi-partitioning for clustering and then we use eigen-decomposition to obtain an optimal partition set. Therefore, the method is referred to as semidefinite spectral clustering (SSC). Numerical experiments with several artificial and real data sets, demonstrate the useful behavior of our SSC. compared to existing spectral clustering methods.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1992.04b
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• pp.73-73
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• 1992

• Information Systems Review
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• v.5 no.2
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• pp.203-217
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• 2003

Constraint Programming and Optimization have developed in different fields to solve common problems in real world. In particular, constraint propagation and linear Programming are their own fundamental and complementary techniques with the potential for integration to benefit each other. This intersection has evoked the efforts to combine both for a solution method to combinatorial optimization problems. Attempts to combine them have mainly focused on incorporating either technique into the framework of the other with traditional models left intact. This paper argues that integrating both techniques into an old modeling fame loses advantages from another and the integration should be molded in a new framework to be able to exploit advantages from both. The paper propose a declarative modeling framework in which the structure of the constraints indicates how constraint programming and optimization solvers can interact to solve problems.

• Journal of Intelligence and Information Systems
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• v.12 no.1
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• pp.139-161
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• 2006

In this paper, we introduce a new combinatorial auction mechanism - N-Bilateral Optimized Combinatorial Auction (N-BOCA). N-BOCA is a flexible iterative combinatorial auction model that offers optimized trading for multi-suppliers and multi-purchasers in the supply chain. We design the N-BOCA system from the perspectives of architecture, protocol, and trading strategy. Under the given N-BOCA architecture and protocol, auctioneers and bidders have diverse decision strategies f3r winner determination. This needs flexible modeling environments. Hence, we propose an optimization modeling agent for bid and auctioneer selection. The agent has the capability to automatic model formulation for Integer Programming modeling. Finally, we show the viability of N-BOCA through prototype and experiments. The results say both higher allocation efficiency and effectiveness compared with 1-to-N general combinatorial auction mechanisms.

• 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.

• Smart Structures and Systems
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• v.31 no.3
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• pp.247-257
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• 2023

In structural health monitoring of large-scale structures, optimal sensor placement plays an important role because of the high cost of sensors and their supporting instruments, as well as the burden of data transmission and storage. In this study, a vibration sensor placement algorithm based on deep reinforcement learning (DRL) is proposed, which can effectively solve non-convex, high-dimensional, and discrete combinatorial sensor placement optimization problems. An objective function is constructed to estimate the quality of a specific vibration sensor placement scheme according to the modal assurance criterion (MAC). Using this objective function, a DRL-based algorithm is presented to determine the optimal vibration sensor placement scheme. Subsequently, we transform the sensor optimal placement process into a Markov decision process and employ a DRL-based optimization algorithm to maximize the objective function for optimal sensor placement. To illustrate the applicability of the proposed method, two examples are presented: a 10-story braced frame and a sea-crossing bridge model. A comparison study is also performed with a genetic algorithm and particle swarm algorithm. The proposed DRL-based algorithm can effectively solve the discrete combinatorial optimization problem for vibration sensor placements and can produce superior performance compared with the other two existing methods.

• Journal of Korean Institute of Industrial Engineers
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• v.35 no.1
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• pp.87-91
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• 2009

In the server disconnection problem, a network with m servers and their users is given and an attacker is to destroy a set of edges to maximize his net gain defined as the total disconnected utilities of the users minus the total edge-destruction cost. The problem is known to be NP-hard. In this paper, we study the server disconnection problem restricted to a ring network. We present an efficient combinatorial algorithm that generates an optimal solution in polynomial time.

• Proceedings of the Polymer Society of Korea Conference
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• 2006.10a
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• pp.110-111
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• 2006

Most applications of polymers involve blends and mixtures of components including solvents, surfactants, copolymers, fillers, organic or inorganic functional additives, and various processing aids. These components provide unique properties of polymeric materials even beyond those tailored into the basic chemical structures. In addition, skillful processing extends the properties for even greater applications. The perennial challenge of polymer science is to understand and exploit the structure-processing-property interplay relationship. We are developing and demonstrating combinatorial methods and high throughput analysis as tools to provide this fundamental understanding.

• Industrial Engineering and Management Systems
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• v.11 no.4
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• pp.310-330
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• 2012

Scheduling is an important tool for a manufacturing system, where it can have a major impact on the productivity of a production process. In manufacturing systems, the purpose of scheduling is to minimize the production time and costs, by assigning a production facility when to make, with which staff, and on which equipment. Production scheduling aims to maximize the efficiency of the operation and reduce the costs. In order to find an optimal solution to manufacturing scheduling problems, it attempts to solve complex combinatorial optimization problems. Unfortunately, most of them fall into the class of NP-hard combinatorial problems. Genetic algorithm (GA) is one of the generic population-based metaheuristic optimization algorithms and the best one for finding a satisfactory solution in an acceptable time for the NP-hard scheduling problems. GA is the most popular type of evolutionary algorithm. In this survey paper, we address firstly multiobjective hybrid GA combined with adaptive fuzzy logic controller which gives fitness assignment mechanism and performance measures for solving multiple objective optimization problems, and four crucial issues in the manufacturing scheduling including a mathematical model, GA-based solution method and case study in flexible job-shop scheduling problem (fJSP), automatic guided vehicle (AGV) dispatching models in flexible manufacturing system (FMS) combined with priority-based GA, recent advanced planning and scheduling (APS) models and integrated systems for manufacturing.

• Proceedings of the IEEK Conference
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• 2002.07a
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• pp.254-257
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• 2002

In previous study about combinatorial optimization problem solver by using neural network, since Hopfield method, to converge into the optimum solution sooner and certainer is regarded as important. Namely, only static states are considered as the information. However, from a biological point of view, the dynamical system has lately attracted attention. Then we propose the "dynamical" combinatorial optimization problem solver using hysteresis neural network. In this article, the proposal system is evaluated by the N-Queen problem.