• Journal of the Korean Institute of Telematics and Electronics C
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• v.34C no.5
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• pp.66-70
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• 1997

This paper presents a parallel combinatorial variant of evolution strategy (PCES) to solve well-known combinatorial optimization problems, Quadratic assignment problems (QAPs). The PCES reduces the possibility of getting stuck in local minima due to maintenance of subpopulation and thus it is more effective than the CES. Experiment results on two benchmark problems show that the PCES is better than the cES and the genetic algorithm(GA).

• Journal of Korean Society of Industrial and Systems Engineering
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• v.20 no.44
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• pp.93-101
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• 1997

This paper describes the application of Genetic Algorithms(GAs) to nonlinear constrained mixed optimization problems. Genetic Algorithms are combinatorial in nature, and therefore are computationally suitable for treating discrete and integer design variables. But, several problems that conventional GAs are ill defined are application of penalty function that can be adapted to transform a constrained optimization problem into an unconstrained one and premature convergence of solution. Thus, we developed an improved GAs to solve this problems, and two examples are given to demonstrate the effectiveness of the methodology developed in this paper.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2008.10a
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• pp.308-311
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• 2008

The ant colony optimization (ACO) is a metaheuristic inspired by the behavior of real ants. Recently, ACO has been widely used to solve the difficult combinatorial optimization problems. In this paper, we propose an ACO algorithm to solve the two disjoint paths problem with dual link cost structure (TDPDCP). We propose a dual pheromone structure and a procedure for solution construction which is appropriate for the TDPDCP. Computational comparisons with the state-of-the-arts algorithms are also provided.

• Journal of Korea Society of Industrial Information Systems
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• v.27 no.4
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• pp.37-45
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• 2022

In this paper, a multi agent-multi task assignment problem, which is a representative problem of combinatorial optimization, is presented. The objective of the problem is to determine the coordinated agent-task assignment that maximizes the sum of the achievement rates of each task. The achievement rate is represented as a concave down increasing function according to the number of agents assigned to the task. The problem is expressed as an NP-hard problem with a non-linear objective function. In this paper, to solve the assignment problem, we propose a hybrid cross-entropy algorithm as an effective and efficient solution methodology. In fact, the general cross-entropy algorithm might have drawbacks (e.g., slow update of parameters and premature convergence) according to problem situations. Compared to the general cross-entropy algorithm, the proposed method is designed to be less likely to have the two drawbacks. We show that the performances of the proposed methods are better than those of the general cross-entropy algorithm through numerical experiments.

• Journal of Korea Society of Industrial Information Systems
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• v.28 no.1
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• pp.27-34
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• 2023

The optimal assignment problem between agents and tasks is known as one of the representative problems of combinatorial optimization and an NP-hard problem. This paper covers multi agent-multi task assignment problems with uncertain completion probability. The completion probabilities are generally uncertain due to endogenous (agent or task) or exogenous factors in the system. Assignment decisions without considering uncertainty can be ineffective in a real situation that has volatility. To consider uncertain completion probability mathematically, a mathematical formulation with stochastic programming is illustrated. We also present an algorithm by using the sample average approximation method to solve the problem efficiently. The algorithm can obtain an assignment decision and the upper and lower bounds of the assignment problem. Through numerical experiments, we present the optimality gap and the variance of the gap to confirm the performances of the results. This shows the excellence and robustness of the assignment decisions obtained by the algorithm in the problem with uncertainty.

• Magazine of the Korean Society of Agricultural Engineers
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• v.45 no.6
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• pp.83-95
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• 2003

In this study, a new concept in a paddy consolidation project is introduced in that curved parallel terracing with contour-lined layout is adopted in sloping areas instead of conventional rectangular terracing. The contoured layout reduces earth-moving considerably compared to rectangular methods in consolidation projects. The objective of the paper is to develop a combinatorial optimization model using the network theory for the design of contour-lined plots which minimizes the volume of earth moving. The results showed that as the length of short side of plot is longer or the land slope is steeper, the volume of earth moving for land leveling increases. The developed optimization model is applied for three consolidated districts and the resulting optimal earth moving is compared with the volume of earth from the conventional method. The shorter is the minimum length of short side of a polt with increases the number of plots, the less is the volume of earth. As the minimum length of short side is 20 m for efficient field works by farm machinery, the volume of earth moving of optimal plot is less by 21.0∼27.1 % than that of the conventional consolidated plots.

• Journal of Information Processing Systems
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• v.14 no.4
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• pp.961-979
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• 2018

For many years, matching in a bipartite graph has been widely used in various assignment problems, such as stable marriage problem (SMP). As an application of bipartite matching, the problem of stable marriage is defined over equally sized sets of men and women to identify a stable matching in which each person is assigned a partner of opposite gender according to their preferences. The classical SMP proposed by Gale and Shapley uses preference lists for each individual (men and women) which are infeasible in real world applications for a large populace of men and women such as matrimonial websites. In this paper, we have proposed an enhancement to the SMP by computing a weighted score for the users registered at matrimonial websites. The proposed enhancement has been formulated into profit maximization of matrimonial websites in terms of their ability to provide a suitable match for the users. The proposed formulation to maximize the profits of matrimonial websites leads to a combinatorial optimization problem. We have proposed greedy and genetic algorithm based approaches to solve the proposed optimization problem. We have shown that the proposed genetic algorithm based approaches outperform the existing Gale-Shapley algorithm on the dataset crawled from matrimonial websites.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.29 no.2 s.233
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• pp.262-269
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• 2005

Optimization of a satellite structure under severe space launching environments is performed considering various design constraints. Simulate annealing, one of combinatorial optimization techniques, is used to optimize the satellite. The optimization results by the simulated annealing are compared to those by the method of modified feasible direction and genetic algorithm. Ten bar truss structure is optimized for feasibility study of the simulated annealing. Finally, the satellite structure is optimized by the simulated annealing algorithm under space environment. Weights of the satellite upper platform and propulsion module are minimized with consideration of several static and dynamic constraints. MSC/NASTRAN is used to find the static and dynamic responses. Simulated annealing has been programmed and integrated with the finite element analysis program for optimization. It is shown that the simulated annealing algorithm can be extended to the optimization of space structures.

• 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.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.5
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• pp.2162-2176
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• 2018

Chemical reactions have an intricate relationship with the search for better-quality neighborhood solutions to optimization problems. A catalytic reaction for chemical reactions provides a clue and a framework to solve complicated optimization problems. The application of a catalytic reaction reveals new information hidden in the optimization problem and provides a non-intuitive perspective. This paper proposes a new simulated catalytic reaction method for search in optimization problems. In the experiments using this method, significantly improved results are obtained in almost all graphs tested by applying to a graph bisection problem, which is a representative problem of combinatorial optimization problems.