• Journal of KIISE:Software and Applications
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• v.34 no.3
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• pp.246-254
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• 2007

Local search algorithms start from a certain candidate solution and probe its neighborhood to find ones with improved quality. This paper proposes a method of probabilistically filtering out bad-looking neighbors based on a simple low-cost preliminary evaluation heuristics. The probabilistic filtering enables us to save time wasted on fully evaluating those solutions that will eventually be trashed, and thus improves the search efficiency by allowing us to spend more time on examining better looking solutions. Experiments with two large-scaled real-world problems, which are a traffic signal control problem in traffic network and a load balancing problem in production scheduling, have shown that the proposed method finds better quality solutions, given the same amount of CPU time.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.8
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• pp.790-797
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• 2007

This paper proposes a new measure of cost function ruggedness in local optimization with DEAS. DEAS is a computational optimization method developed since 2002 and has been applied to various engineering fields with success. Since DEAS is a recent optimization method which is rarely introduced in Korean, this paper first provides a brief overview and description of DEAS. In minimizing cost function with this non-gradient method, information on function shape measured automatically will enhance search capability. Considering the search strategies of DEAS are well designed with binary matrix structures, analysis of search behaviors will produce beneficial shape information. This paper deals with a simple quadratic function contained with various magnitudes of noise, and DEAS finds local minimum yielding ruggedness measure of given cost function. The proposed shape information will be directly used in improving DEAS performance in future work.

• Proceedings of the KSME Conference
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• 2001.06c
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• pp.554-558
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• 2001

In this study, a hybrid method is used to search the pseudo-optimal solution for the I-dimentional nesting problem. This method is composed of the genetic algorithm for the global search and a simple heuristic one for the local search near the pseudo optimal solution. Several simulation results show that the hybrid method gives very satisfactory results.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.29 no.3
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• pp.62-69
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• 2006

Vehicle routing problem with time windows is determined each vehicle route in order to minimize the transportation costs. All delivery points in geography have various time restriction in camparision with the basic vehicle routing problem. Vechicle routing problem with time windows is known to be NP-hard, and it needs a lot of computing time to get the optimal solution, so that heuristics are more frequently developed than optimal algorithms. This study aims to develop a heuristic method which combines guided local search with a tabu search in order to minimize the transportation costs for the vehicle routing assignment and uses ILOG programming library to solve. The computational tests were performed using the benchmark problems.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.2
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• pp.8-18
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• 2020

An Exponential Bandwidth Harmony Search with Centralized Global Search (EBHS-CGS) was developed to enhance the performance of a Harmony Search (HS). EBHS-CGS added two methods to improve the performance of HS. The first method is an improvement of bandwidth (bw) that enhances the local search. This method replaces the existing bw with an exponential bw and reduces the bw value as the iteration proceeds. This form of bw allows for an accurate local search, which enables the algorithm to obtain more accurate values. The second method is to reduce the search range for an efficient global search. This method reduces the search space by considering the best decision variable in Harmony Memory (HM). This process is carried out separately from the global search of the HS by the new parameter, Centralized Global Search Rate (CGSR). The reduced search space enables an effective global search, which improves the performance of the algorithm. The proposed algorithm was applied to a representative optimization problem (math and engineering), and the results of the application were compared with the HS and better Improved Harmony Search (IHS).

• The KIPS Transactions:PartB
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• v.10B no.3
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• pp.237-242
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• 2003

Ant Colony System (ACS) Algorithm is new meta heuristic for hard combinatorial optimization problem. It is a population based approach that uses exploitation of positive feedback as well as greedy search. It was first proposed for tackling the well known Traveling Salesman Problem (TSP). In this paper, we introduce ACS of new method that adds reinforcement value for each edge that visit to Local/Global updating rule. and the performance results under various conditions are conducted, and the comparision between the original ACS and the proposed method is shown. It turns out that our proposed method can compete with tile original ACS in terms of solution quality and computation speed to these problem.

• Journal of the Chungcheong Mathematical Society
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• v.21 no.1
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• pp.117-127
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• 2008

The performance of a modified Armijo line search rule related to BFGS gradient type method with the results from other well-known line search rules are compared as well as analyzed. Although the modified Armijo rule does require as much computational cost as the other rules, it shows more efficient in finding local minima of unconstrained optimization problems. The sensitivity of the parameters used in the line search rules is also analyzed. The results obtained by implementing algorithms in Matlab for the test problems in [3] are presented.

• The Journal of the Korea Contents Association
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• v.10 no.7
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• pp.81-87
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• 2010

This paper proposes a G-BLAST(BLAST using Grid Computing) system, an integrated software package for BLAST searches operated in heterogeneous distributed environment. G-BLAST employed 'database splicing' method to improve the performance of BLAST searches using exists computing resources. G-BLAST is a basic local alignment search tool of DNA Sequence using grid computing in heterogeneous distributed environment. The G-BLAST improved the existing BLAST search performance in gene sequence analysis. Also G-BLAST implemented the pipeline and data management method for users to easily manage and analyze the BLAST search results. The proposed G-BLAST system has been confirmed the speed and efficiency of BLAST search performance in heterogeneous distributed computing.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.51 no.3
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• pp.155-160
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• 2002

This paper focuses on the problem of local obstacle avoidance of mobile robots. To solve this problem, the safety direction section search algorithm is suggested. This concept is mainly composed with non-collision section and collision section from the detecting area of laser scanning sensor. Then, we will search for the most suitable direction in these sections. The proposed local motion planning method is simple and requires less computation than others. An environment model is developed using the vector space concept to determine robot motion direction taking the target direction, obstacle configuration, and robot trajectory into account. Since the motion command is obtained considering motion dynamics, it results in smooth and fast as well as safe movement. Using the mobile base, the proposed obstacle avoidance method is tested, especially in the environment with pillar, wall and some doors. Also, the proposed autonomous motion planning and control algorithm are tested extensively. The experimental results show the proposed method yields safe and stable robot motion through the motion speed is not so fast.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.11C
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• pp.1091-1097
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• 2005

In this paper, we propose fast motion estimation algorithm. Local statistics of a motion vector is highly correlated to motion vectors of its neighboring blocks. According to the property, block-based motion search range is adaptively determined in order to reduce unnecessary search points. Based on the determined search range, motion vector is obtained by variable step search motion estimation. Experimental results show that comparing to Full search motion estimation, the motion searching points of proposed algorithm is reduced as much as $98\%$. Moreover, PSNR and Bit Rate are almost same to Full search method.