• Journal of Korea Game Society
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• v.16 no.4
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• pp.79-86
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• 2016

Monte-Carlo Tree Search(MCTS) is a best-first tree search algorithm and has been successfully applied to various games, especially to the game of Go. We evaluate the performance of MCTS playing against each other in the game of Tic-Tac-Toe. It reveals that the first player always has an overwhelming advantage to the second player; and we try to find out the reason why the first player is superior to the second player in spite of the fact that the best game result should be a draw. Since MCTS is a statistical algorithm based on the repeated random sampling, it cannot adequately tackle an urgent problem that needs a strategy, especially for the second player. For this, we propose a strategic MCTS(S-MCTS) and show that the S-MCTS player never loses a Tic-Tac-Toe game.

• IE interfaces
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• v.15 no.4
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• pp.338-348
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• 2002

In this paper, we present a restricted tabu search(RTS) algorithm that schedules jobs on identical parallel machines in order to minimize the maximum lateness of jobs. Jobs have release times and due dates. Also, sequence-dependent setup times exist between jobs. The RTS algorithm consists of two main parts. The first part is the MATCS(Modified Apparent Tardiness Cost with Setups) rule that provides an efficient initial schedule for the RTS. The second part is a search heuristic that employs a restricted neighborhood generation scheme with the elimination of non-efficient job moves in finding the best neighborhood schedule. The search heuristic reduces the tabu search effort greatly while obtaining the final schedules of good quality. The experimental results show that the proposed algorithm gives better solutions quickly than the existing heuristic algorithms such as the RHP(Rolling Horizon Procedure) heuristic, the basic tabu search, and simulated annealing.

• Journal of Korea Game Society
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• v.14 no.3
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• pp.47-54
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• 2014

The game of Go is one of the oldest games and originated at least more than 2,500 years ago. In game programming the most successful approach is to use game tree searches using evaluation functions. However it is really difficult to construct feasible evaluation function in computer Go. Monte-Carlo Tree Search(MCTS) has created strong computer Go programs such as MoGo and CrazyStone which defeated human Go professionals played on the $9{\times}9$ board. MCTS is based on the winning rate estimated by Monte-Carlo simulation. Prior to implementing MCTS into computer Go, we tried to measure each winning rate of three positions, center, corner and side, in Tic-Tac-Toe playing as the best first move. The experimental result revealed that the center is the best, a corner the next and a side the last as the best first move.

• The KIPS Transactions:PartB
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• v.10B no.7
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• pp.811-814
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• 2003

In this paper, we propose a fast block-matching motion estimation method using the cross pattern and the hexagonal pattern. For the block-matching motion estimation method, full search finds the best motion estimation, but it requires huge search time because it has to check every search point within the search window. The proposed method makes use of the fact that most of motion vectors lie near the center of block. The proposed method first uses the cross pattern to search near the center of block, and then uses the hexagonal pattern to search larger motion vectors. Experimental results show that our method is better than recently proposed search algorithms in terms of mean-square error performance and required search time.

• International Journal of Computer Science & Network Security
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• v.21 no.11
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• pp.312-320
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• 2021

Increase in computational cost and exhaustive search can lead to more complexity and computational energy. Thus, there is need for effective and efficient scheme to reduce the complexity to achieve optimal energy utilization. This will improve the energy efficiency and enhance the proficiency in terms of the resources needed to achieve convergence. This paper primarily focuses on the development of hybrid swarm intelligence scheme for reducing the computational complexity in binary optimization. In order to reduce the complexity, both artificial bee colony (ABC) and particle swarm optimization (PSO) have been employed to effectively minimize the exhaustive search and increase convergence. First, a new approach using ABC and PSO has been proposed and developed to solve the binary optimization problem. Second, the scout for good quality food sources is accomplished through the deployment of PSO in order to optimally search and explore the best source. Extensive experimental simulations conducted have demonstrate that the proposed scheme outperforms the ABC approaches for reducing complexity and energy consumption in terms of convergence, search and error minimization performance measures.

• Journal of the Korea Society of Computer and Information
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• v.27 no.9
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• pp.69-76
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• 2022

The traveling salesman problem(TSP) is one of the most famous combinatorial optimization problem. So far, many metaheuristic search algorithms have been proposed to solve the problem, and one of them is local search. One of the very important factors in local search is neighbor generation method, and random-based neighbor generation methods such as inversion have been mainly used. This paper proposes 4 new greedy-based neighbor generation methods. Three of them are based on greedy insertion heuristic which insert selected cities one by one into the current best position. The other one is based on greedy rotation. The proposed methods are applied to first-choice hill-climbing search and simulated annealing which are representative local search algorithms. Through the experiment, we confirmed that the proposed greedy-based methods outperform the existing random-based methods. In addition, we confirmed that some greedy-based methods are superior to the existing local search methods.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.2
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• pp.727-746
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• 2018

The hash function RIPEMD-160 is a worldwide ISO/IEC standard and the hash function HAS-160 is the Korean hash standard and is widely used in Korea. On the basis of differential meet-in-the-middle attack and biclique technique, a preimage attack on 34-step RIPEMD-160 with message padding and a pseudo-preimage attack on 71-step HAS-160 without message padding are proposed. The former is the first preimage attack from the first step, the latter increases the best pseudo-preimage attack from the first step by 5 steps. Furthermore, we locate the linear spaces in another message words and exchange the bicliques construction process and the mask vector search process. A preimage attack on 35-step RIPEMD-160 and a preimage attack on 71-step HAS-160 are presented. Both of the attacks are from the intermediate step and satisfy the message padding. They improve the best preimage attacks from the intermediate step on step-reduced RIPEMD-160 and HAS-160 by 4 and 3 steps respectively. As far as we know, they are the best preimage and pseudo-preimage attacks on step-reduced RIPEMD-160 and HAS-160 respectively in terms of number of steps.

• Communications for Statistical Applications and Methods
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• v.9 no.3
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• pp.809-824
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• 2002

we propose two methods which separate the variable selection step and the split-point selection step. We call these two algorithms as CHITES method and F&CHITES method. They adapted some of the best characteristics of CART, CHAID, and QUEST. In the first step the variable, which is most significant to predict the target class values, is selected. In the second step, the exhaustive search method is applied to find the splitting point based on the selected variable in the first step. We compared the proposed methods, CART, and QUEST in terms of variable selection bias and power, error rates, and training times. The proposed methods are not only unbiased in the null case, but also powerful for selecting correct variables in non-null cases.

• Proceedings of the KIEE Conference
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• 2000.07a
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• pp.519-521
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• 2000

Network reconfiguration is an operation task, and consists in the determination of the switching operations such to reach the minimum loss conditions of the distribution network. In this paper, an effective heuristic based switch scheme for loss minimization is given for the optimization of distribution loss reduction and a solution approach is presented. The solution algorithm for loss minimization has been developed based on the two stage solution methodology. The first stage of this solution algorithm sets up a decision tree which represent the various switching operations available, the second stage applies a proposed technique called cyclic best first search. Therefore, the solution algorithm of proposed method can determine on-off switch statuses for loss reduction, with a minimum computational effort.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.52 no.9
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• pp.513-520
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• 2003

Service restoration is an emergency control in distribution constrol centers to restore out-of-service area as soon as possible when a fault occurs in distribution networks. therefore, it requires fast computation time and high quality solutions for load balancing. In this paper. a load balance index and heuristic guided best-first search are proposed for these problem. The proposed algorithm consists of two parts. One is to set up a decision tree to represent the various switching operations available. Another is to identify the most effective the set of switches using proposed search technique and a load balance index. Test results on the KEPCO's 108 bus distribution system show that the performance is efficient and robust.