• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2009.01a
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• pp.460-465
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• 2009

This paper presents a new algorithm that includes a mechanism to avoid local solutions in a motion vector detection method that uses the steepest descent method. Two different implementations of the algorithm are demonstrated using two major search methods for tree structures, depth first search and breadth first search. Furthermore, it is shown that by avoiding local solutions, both of these implementations are able to obtain smaller prediction errors compared to conventional motion vector detection methods using the steepest descent method, and are able to perform motion vector detection within an arbitrary upper limit on the number of computations. The effects that differences in the search order have on the effectiveness of avoiding local solutions are also presented.

• Journal of the Korean Institute of Intelligent Systems
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• v.9 no.6
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• pp.650-656
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• 1999

In this paper, we study on the convergence of the evolution strategies by introducing the Lamarckian evolution and the Baldwin effect, and propose a random local searching and a reinforcement local searching methods. In the random local searching method some neighbors generated randomly from each individual are med without any other information, but in the reinforcement local searching method the previous results of the local search are reflected on the current local search. From the viewpoint of the purpose of the local search it is suitable that we try all the neighbors of the best individual and then search the neighbors of the best one of them repeatedly. Since the reinforcement local searching method based on the Lamarckian evolution and Baldwin effect does not search neighbors randomly, but searches the neighbors in the direction of the better fitness, it has advantages of fast convergence and an improvement on the global searching capability. In other words the performance of the evolution strategies is improved by introducing the learning, reinforcement local search, into the evolution. We study on the learning effect on evolution strategies by applying the proposed method to various function optimization problems.

• Journal of the Korea Society of Computer and Information
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• v.23 no.12
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• pp.1-9
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• 2018

The multiple-choice multidimensional knapsack problem (MMKP) is a variant of the well known 0-1 knapsack problem, which is known as an NP-hard problem. This paper proposes a method for solving the MMKP using the integer programming-based local search (IPbLS). IPbLS is a kind of a local search and uses integer programming to generate a neighbor solution. The most important thing in IPbLS is the way to select items participating in the next integer programming step. In this paper, three ways to select items are introduced and compared on 37 well-known benchmark data instances. Experimental results shows that the method using linear programming is the best for the MMKP. It also shows that the proposed method can find the equal or better solutions than the best known solutions in 23 data instances, and the new better solutions in 13 instances.

• 제어로봇시스템학회:학술대회논문집
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• 1995.10a
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• pp.14-17
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• 1995

Various methods and techniques have been proposed for solving optimization problems; the methods have been applied to various practical problems. However the methods have demerits. The demerits which should be covered are, for example, falling into local minima, or, a slow convergence speed to optimal points. In this paper, Likelihood Search Method (L.S.M.) is proposed for searching for a global optimum systematically and effectively in a single framework, which is not a combination of different methods. The L.S.M. is a sort of a random search method (R.S.M.) and thus can get out of local minima. However exploitation of gradient information makes the L.S.M. superior in convergence speed to the commonly used R.S.M..

• Korean Journal of Cognitive Science
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• v.4 no.2
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• pp.181-200
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• 1994

In the learning phase of multilyer feedforword neural network,there are problems such that local minimum,learning praralysis and slow learning speed when backpropagation algorithm used.To overcome these problems, the genetic algorithm has been used as learing method in the multilayer feedforword neural network instead of backpropagation algorithm.However,because the genetic algorith, does not have any mechanism for fine-tuned local search used in backpropagation method,it takes more time that the genetic algorithm converges to a global optimal solution.In this paper,we suggest a new GA-BP method which provides a fine-tunes local search to the genetic algorithm.GA-BP method uses gradient descent method as one of genetic algorithm's operators such as mutation or crossover.To show the effciency of the developed method,we applied it to the 3-parity bit problem with analysis.

• Structural Engineering and Mechanics
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• v.65 no.6
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• pp.707-719
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• 2018

In this article, a new method is introduced to improve the local search capability of meta-heuristic algorithms using the projection of the path on the border of constraints. In a mathematical point of view, the Gradient Projection Method is applied through a new approach, while the imposed limitations are removed. Accordingly, the gradient vector is replaced with a new meta-heuristic based vector. Besides, the active constraint identification algorithm, and the projection method are changed into less complex approaches. As a result, if a constraint is violated by an agent, a new path will be suggested to correct the direction of the agent's movement. The presented procedure includes three main steps: (1) the identification of the active constraint, (2) the neighboring point determination, and (3) the new direction and step length. Moreover, this method can be applied to some meta-heuristic algorithms. It increases the chance of convergence in the final phase of the search process, especially when the number of the violations of the constraints increases. The method is applied jointly with the authors' newly developed meta-heuristic algorithm, entitled Star Graph. The capability of the resulted hybrid method is examined using the optimal design of truss and frame structures. Eventually, the comparison of the results with other meta-heuristics of the literature shows that the hybrid method is successful in the global as well as local search.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.53 no.1
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• pp.22-31
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• 2004

This paper deals with the dynamic hand gesture recognition based on computer vision using neural networks. This paper proposes a global search method and a local search method to recognize the hand gesture. The global search recognizes a hand among the hand candidates through the entire image search, and the local search recognizes and tracks only the hand through the block search. Dynamic hand gesture recognition method is based on the skin-color and shape analysis with the invariant moment and direction information. Starting point and ending point of the dynamic hand gesture are obtained from hand shape. Experiments have been conducted for hand extraction, hand recognition and dynamic hand gesture recognition. Experimental results show the validity of the proposed method.

• Proceedings of the KIEE Conference
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• 2002.11b
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• pp.76-79
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• 2002

In this paper, we propose a Probabilistic Tabu Search(PTS) method for hydro-thermal scheduling. Hydro scheduling has many constraints and very difficult to solve the optimal schedule because it has many local minima. To solve the problem effectively, the proposed method uses two procedures, one is Tabu search procedure that plays a role in local search, and the other is Restarting procedure that enables to diversify its search region. To adjust parameters such as a reducing rate and initial searching region, search strategy is selected according to its probability after Restarting procedure. In order to show the usefulness of the proposed method, the PTS is applied on two cases which have dependent hydro plants and compared to those of other method. The simulation results show it is very efficient and useful algorithm to solve the hydro-thermal scheduling problem.

• Journal of the Korea Society of Computer and Information
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• v.29 no.4
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• pp.1-8
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• 2024

The traveling salesman problem(TSP) is one of the well known combinatorial optimization problems. Local search has been used as a method to solve TSP. Greedy Random Insertion(GRI) is known as an effective neighbor generation method for local search. GRI selects some cities from the current solution randomly and inserts them one by one into the best position of the current partial solution considering only one city at a time. We first propose another greedy neighbor generation method which is named Full Greedy Insertion(FGI). FGI determines insertion location one by one like GRI, but considers all remaining cities at once. And then we propose a method to combine GRI with FGI, in which GRI or FGI is randomly selected and executed at each iteration in simulated annealing. According to the experimental results, FGI alone does not necessarily perform very well. However, we confirmed that the combined method outperforms the existing local search methods including GRI.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2004.05a
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• pp.416-420
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• 2004

Recently, the importance of vibration reduction at the local structure such as tank, deck which attached machinery and compass deck, has continuously increased by owner and shipbuilder. Because crews are afflicted with them and severe vibration problems affect on the crack of structure. This study conducted optimum design to get a stiffener size of local structure to reducing the vibration level and dec leasing the weight of structure in ship. Random tabu search method (R-Tabu) has fast converging time and can search variables size domains for nonlinear problems. This paper used Nastran external call type independence optimization method which makes using a solver module from Nastran.