• Journal of the Korea Society of Computer and Information
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• v.26 no.10
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• pp.27-35
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• 2021

Local search has been used to solve various combinatorial optimization problems. One of the most important factors in local search is the method of generating a neighbor solution. In this paper, we propose neighbor generation strategies of local search for permutation-based combinatorial optimization, and compare the performance of each strategies targeting the traveling salesman problem. In this paper, we propose a total of 10 neighbor generation strategies. Basically, we propose 4 new strategies such as Rotation in addition to the 4 strategies such as Swap which have been widely used in the past. In addition, there are Combined1 and Combined2, which are made by combining basic neighbor generation strategies. The experiment was performed by applying the basic local search, but changing only the neighbor generation strategy. As a result of the experiment, it was confirmed that the performance difference is large according to the neighbor generation strategy, and also confirmed that the performance of Combined2 is the best. In addition, it was confirmed that Combined2 shows better performance than the existing local search methods.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.55 no.9
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• pp.392-398
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• 2006

Maintenance scheduling of generating units (MSU) has strategic dimension in an oligopolistic market. Strategic MSU of gencos can affect a market power through capacity withdrawal which is related to bidding strategy in an generation wholesale market. This paper presents a combined framework that models the interrelation between competitive bidding and strategic MSU. The combined game model is represented as some sub-optimization problems of a market operator (MO) and gencos, that should be solved through bi-level optimization scheme. The gradient method with dual variables is also adopted to calculate a Nash Equilibrium (NE) by an iterative update technique in this paper. Illustrative numerical example shows that NE of a supply function equilibrium is obtained properly by using proposed solution technique. The MSU made by MO is compared with that by each genco and that under perfect competition market.

• Journal of the Optical Society of Korea
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• v.20 no.3
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• pp.389-395
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• 2016

A Carbon Fiber Composite (CFC) framework was designed for a small lightweight space camera. According to the distribution characteristics of each optical element in the optical system, CFC (M40J) was chosen to accomplish the design of the framework. TC4 embedded parts were used to solve the low accuracy of the CFC framework interface problem. An integrated optimization method and the optimization strategy which combined a genetic global optimization algorithm with a downhill simplex local optimization algorithm were adopted to optimize the structure parameters of the framework. After optimization, the total weight of the CFC framework and the TC4 embedded parts is 15.6 kg, accounting for only 18.4% that of the camera. The first order frequency of the camera reaches 104.8 Hz. Finally, a mechanical environment test was performed, and the result demonstrates that the first order frequency of the camera is 102 Hz, which is consistent with the simulation result. It further verifies the rationality and correctness of the optimization result. The integrated optimization method mentioned in this paper can be applied to the structure design of other space cameras, which can greatly improve the structure design efficiency.

• Proceedings of the KIEE Conference
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• 2007.04c
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• pp.162-164
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• 2007

This paper presents a "window-zoom-out" optimization strategy with relatively fewer sampling data. In this method, an optimal Latin hypercube sampling experiment based on multi-objective Pareto optimization is developed to obtain the sampling data. The response surface method with multiquadric radial basis function combined with (1+$\lambda$) evolution strategy is used to find the global optimal point. The proposed method is verified with numerical experiments.

• Proceedings of the KIEE Conference
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• 1996.11a
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• pp.6-8
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• 1996

The multiobjective optimization is presented for the optimal design of induction motors. The aim of design is to find an optimized induction motor in terms of both the efficiency and the mass. The efficiency and the mass are linearly combined using the weighting factors. Optimization process is performed by using the improved (1+1) evolution strategy (ES). ES is the algorithm that can find the global minimum. To verify the validity of the proposed method. the method is applied to a sample design.

• Journal of the Korean Magnetics Society
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• v.7 no.2
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• pp.103-108
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• 1997

In this paper, the application of fuzzy decision to optimization of induction motor design is proposed. This method can reflect the designer's experience, view, and judgment, but also can be applied to multi-objective optimization design easily. The electromagnetic performance of the induction motor are calculated by means of the equivalent magnetic circuit method. The design method is The $D^2L$ method which is combined with fuzzy decision and optimization algorithm. As the optimization algorithm, the evolution strategy(ES) is applied. The proposed algorithm is applied to a multiobjective optimization of an induction motor design where the motor should have less weight and, at the same time, have higher efficiency and power factor at rated operating points.

• Structural Engineering and Mechanics
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• v.46 no.3
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• pp.403-416
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• 2013

This paper describes new optimization strategy that offers significant improvements in performance over existing methods for geometry design of frame structures. In this study, an imperialist competitive algorithm (ICA) and ant colony optimization (ACO) are combined to reach to an efficient algorithm, called Imperialist Competitive Ant Colony Optimization (ICACO). The ICACO applies the ICA for global optimization and the ACO for local search. The results of optimal geometry for three benchmark examples of frame structures, demonstrate the effectiveness and robustness of the new method presented in this work. The results indicate that the new technique has a powerful search strategies due to the modifications made in search module of ICACO. Higher rate of convergence is the superiority of the presented algorithm in comparison with the conventional mathematical methods and non hybrid heuristic methods such as ICA and particle swarm optimization (PSO).

• Structural Engineering and Mechanics
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• v.7 no.1
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• pp.19-33
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• 1999

The critical buckling loads of unsymmetrically laminated rectangular plates with a given material system and subjected to combined lateral and inplane loads are maximized with respect to fiber orientations by using a sequential linear programming method together with a simple move-limit strategy. Significant influence of plate aspect ratios, central circular cutouts, lateral loads and end conditions on the optimal fiber orientations and the associated optimal buckling loads of unsymmetrically laminated plates has been shown through this investigation.

• Journal of the Korean Mathematical Society
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• v.47 no.1
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• pp.63-70
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• 2010

In this paper, we develop a nonmonotone spectral memory gradient method for unconstrained optimization, where the spectral stepsize and a class of memory gradient direction are combined efficiently. The global convergence is obtained by using a nonmonotone line search strategy and the numerical tests are also given to show the efficiency of the proposed algorithm.

• Proceedings of the KIEE Conference
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• 1997.07b
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• pp.699-701
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• 1997

The simulated annealing(SA) algorithm is a stochastic strategy for search of the ground state and a powerful tool for optimization, based on the annealing process used for the crystallization in physical systems. It's main disadvantage is the long convergence time. Therefore, this paper proposes a stochastic algorithm combined with conventional deterministic optimization method to reduce the computation time, which is called SDS(Stochastic-Deterministic-Stochastic) method.