• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2003년도 ICCAS
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• pp.25-29
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• 2003

LQ actuator selection problem for multi-input system discussed in this paper is to determine optimal actuator out of many actuators and input sequences so as to minimize the quadratic control performance. The solution of this problem depends on initial values and has a combinatorial property, so it is extremely difficult to get an optimal solution. For this difficulty, we proposed the concept of comparability of actuators and showed the uniqueness of the solution[1] . Further, to get general optimal solution for LQ problem with actuator selection strategies, we derived the equivalent condition for the comparability of actuator in single-input system . In this paper we extend this result to the case of multi-input system. The derived sufficient condition is applicable in the case of positive semi-definite comparability matrices.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2002년도 ITC-CSCC -1
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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.

• Journal of Computing Science and Engineering
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• 제8권4호
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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.

• 한국정보디스플레이학회:학술대회논문집
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• 한국정보디스플레이학회 2004년도 Asia Display / IMID 04
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• pp.650-653
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• 2004

We have synthesized some phosphors in the system MO-$Al_2O_3-SiO_2$(M=Sr, Ba) by combinatorial polymerized-complex method. Composition and synthetic temperature of phosphors in the liblary was screened from the emission intensities of individual samples under 365nm excitation. As we were screened the higher luminescent candidate composition (or candidate host lattice) at 365nm excitation, investigated whether good radiation was possible at the 405 or 465nm excitation by give the host lattice to be discovered more various change. From libraries about 2 systems, the compound to be expected in long wavelength among the compound to be screened are $Sr_4Al_{14}O_{25}$, $Sr_3Al_2O_6$, $SrAl_2Si_2O_8$, and $BaAl_2Si_2O_8$.

• 대한전기학회논문지:전력기술부문A
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• 제51권5호
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• pp.217-224
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• 2002

This paper presents an algorithm to obtain an approximate optimal solution for the service restoration and load balancing of large scale radial distribution system in a real-time operation environment. Since the problem is formulated as a combinatorial optimization problem, it is difficult to solve a large-scale combinatorial optimization problem accurately within the reasonable computation time. Therefore, in order to find an approximate optimal solution quickly, the authors proposed an algorithm which combines optimization technique called cyclic best-first search with heuristic based feeder loadings balance index for computational efficiency and robust performance. To demonstrate the validity of the proposed algorithm, numerical calculations are carried out the KEPCO's 108 bus distribution system.

• 대한수학회논문집
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• 제13권3호
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• pp.445-459
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• 1998

Let λ be a partition with all distinct parts. In this paper we give a bijection between the set $\Gamma$$_{λ}$(X) of pairs (equation omitted) satisfying a certain condition and the set $\pi_{λ}$(X) of circled permutation tableaux of shape λ on the set X, where P$\frac{1}{2}$ is a tail circled shifted rim hook tableaux of shape λ and (equation omitted) is a barred permutation on X. Specializing to the partition λ with one part, this bijection gives a combinatorial proof of the Schur identity: $\Sigma$2$\ell$(type($\sigma$)) = 2n! summed over all permutation $\sigma$ $\in$ $S_{n}$ with type($\sigma$) $\in$ O $P_{n}$ . .

• Journal of Mechanical Science and Technology
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• 제18권9호
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• pp.1512-1518
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• 2004

Rescaled Simulated Annealing (RSA) has been adapted to solve combinatorial optimization problems in which the available computational resources are limited. Simulated Annealing (SA) is one of the most popular combinatorial optimization algorithms because of its convenience of use and because of the good asymptotic results of convergence to optimal solutions. However, SA is too slow to converge in many problems. RSA was introduced by extending the Metropolis procedure in SA. The extension rescales the state's energy candidate for a transition before applying the Metropolis criterion. The rescaling process accelerates convergence to the optimal solutions by reducing transitions from high energy local minima. In this paper, structural optimization examples using RSA are provided. Truss structures of which design variables are discrete or continuous are optimized with stress and displacement constraints. The optimization results by RSA are compared with the results from classical SA. The comparison shows that the numbers of optimization iterations can be effectively reduced using RSA.

• 산업경영시스템학회지
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• 제33권4호
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• pp.201-208
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• 2010

This research is about developing an efficient solution procedure for the vehicle routing problem under varying vehicle moving speeds for hour-based time interval. Different moving speeds for every hour is too difficult condition to solve for this type of combinatorial optimization problem. A methodology to divide the 12 hour based time interval offered by government into 5 different time intervals and then divide delivery area into 12 small divisions first and then re-organizing them into 5 groups. Then vehicle moving speeds are no longer varying in each of the 5 divisions. Therefore, a typical TSP solution procedure may be applied to find the shortest path for all 5 divisions and then connect the local shortest paths to form a delivery path for whole area. Developed solution procedures are explained in detail with 60 points example.

• 경영과학
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• 제15권2호
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• pp.153-159
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• 1998

Clustering or classification is a very fundamental task that may occur almost everywhere for the purpose of grouping. Optimal clustering is an example of very complicated combinatorial optimization problem and it is hard to develop a generally applicable optimal algorithm. In this paper we propose a general-purpose algorithm for the optimal clustering based on SA(simulated annealing). Among various iterative global optimization techniques imitating natural phenomena that have been proposed and utilized successfully for various combinatorial optimization problem, simulated annealing has its superiority because of its convergence property and simplicity. We first present a version of accelerated simulated annealing(ASA) and then we apply ASA to develop an efficient clustering algorithm. Application examples are also given.

• 한국경영과학회:학술대회논문집
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• 대한산업공학회/한국경영과학회 2004년도 춘계공동학술대회 논문집
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• pp.107-110
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• 2004

We present a unifying framework to establish a lower-bound on the number of semidefinite programming based, lift-and-project iterations (rank) for computing the convex hull of the feasible solutions of various combinatorial optimization problems. This framework is based on the maps which are commutative with the lift-and-project operators. Some special commutative maps were originally observed by $Lov{\acute{a}}sz$ and Schrijver, and have been used usually implicitly in the previous lowerbound analyses. In this paper, we formalize the lift-and-project commutative maps and propose a general framework for lower-bound analysis, in which we can recapture many of the previous lower-bound results on the lift-and-project ranks.