• Proceedings of the KIEE Conference
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• 1998.07c
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• pp.1200-1202
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• 1998

전력 사업자들은 일반 수용가에 대한 공급 신뢰도를 개선하기 위해서 배전 자동화 시스템을 도입, 실시간 고장구간 탐색 및 계통 재구성을 추진하고 있다. 그러나. 고장 감지기 자체의 오동작이나 통신상의 오류, 다중사고의 가능성 등 불확실성을 포함하고 있기 때문에 비상시 사고구간 추정에 많은 노력과 시간비용이 요구될 수 있다. 따라서. 본 연구에서는 확률론과 휴리스틱 탐색법을 이용하여 배전자동화 시스템에 수집된 정보가 불확실성을 포함하는 경우에도 신속하게 사고 예비 후보 지역을 제시함으로써 고장구간 추정시간을 최소화 할 수 있는 전문가 시스템이 개발된다.

• Proceedings of the Korean Information Science Society Conference
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• 1999.10a
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• pp.183-185
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• 1999

주행 경로 안내는 최근 국내에서도 활발히 연구되고 있는 지능형 교통 시스템(ITS)의 주요 기능 중 하나이다. 주행 경로 안내를 위해서는 대규모 도로망에서 신속하게 경로를 찾는 방법과 찾은 경로를 음성 또는 기호로 운전자에게 효율적으로 안내하는 방법등이 필요하다. 본 논문에서는 도로망의 계층성을 휴리스틱 정보로 활용하여 최단시간 경로를 효율적으로 찾는 방법을 제안한다. 제안된 방법은 고속국도, 국도 등의 고속 주행용 도로만으로 소규모 상위 계층 도로망을 만들고 이를 기존 도로망에 덧붙이는 방식으로 통합한다. 이 통합망에 상위 계층 도로망을 우선적으로 찾도록 구성한 A* 알고리즘을 수행하여 최단시간 경로를 찾는다. 또 경로 탐색용 그래프가 디스크에 저장된 경우에, 디스크 접근을 최적화하기 위한 데이터베이스 설계 및 디스크 접근 방법을 기술한다. 제안된 방법의 효율성을 검증하기 위해 서울시 도로망 데이터를 이용하여 실험한 결과, 제안된 방법을 통해 경로 탐색 소요 시간, 디스크 입출력 회수, 메모리 사용량 등을 75%이상 줄일 수 있었다.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.40 no.6
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• pp.87-94
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• 2003

One of the important fields for heuristic algorithm is how to balance between Intensification and Diversification. Ant Colony Optimization(ACO) is a new meta heuristic algorithm to solve hard combinatorial optimization problem. It is a population based approach that uses exploitation of positive feedback as well as Breedy search It was first Proposed for tackling the well known Traveling Salesman Problem(TSP). In this paper, we deal with the performance improvement techniques through balance the Intensification and Diversification in Ant Colony System(ACS). First State Transition considering the number of times that agents visit about each edge makes agents search more variously and widen search area. After setting up criteria which divide elite tour that receive Positive Intensification about each tour, we propose a method to do addition Intensification by the criteria. Implemetation of the algorithm to solve TSP and the performance results under various conditions are conducted, and the comparision between the original An and the proposed method is shown. It turns out that our proposed method can compete with the original ACS in terms of solution quality and computation speed to these problem.

• The Journal of the Korea Contents Association
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• v.20 no.3
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• pp.18-30
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• 2020

Recently, as graph-structured data is widely used in various fields such as social networks and semantic Webs, needs for an effective and efficient search on a large amount of graph data have been increasing. Previous keyword-based search methods often find results by considering only the relevance to a given query. However, they are likely to produce semantically similar results by selecting answers which have high query relevance but share the same content nodes. To improve the diversity of search results, we propose a top-k search method that finds a set of subtrees which are not only relevant but also diverse in terms of the content nodes by controlling their similarity. We define a criterion for a set of diverse answer trees and design two kinds of diversified top-k search algorithms which are based on incremental enumeration and A^⁎ heuristic search, respectively. We also suggest an improvement on the A^⁎ search algorithm to enhance its performance. We show by experiments using real data sets that the proposed heuristic search method can find relevant answers with diverse content nodes efficiently.

• Journal of Korea Society of Industrial Information Systems
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• v.27 no.1
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• pp.37-48
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• 2022

In this study, a hybrid meta-heuristic approach is proposed for solving engineering design optimization problems. Various approaches in many literatures have been proposed to solve engineering optimization problems with various types of decision variables and complex constraints. Unfortunately, however, their efficiencies for locating optimal solution do not be highly improved. Therefore, we propose a hybrid meta-heuristic approach for improving their weaknesses. the proposed GA-VNS-HC approach is combining genetic algorithm (GA) for global search with variable neighborhood search (VNS) and hill climbing (HC) for local search. In case study, various types of engineering design optimization problems are used for proving the efficiency of the proposed GA-VNS-HC approach

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2007.10a
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• pp.561-564
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• 2007

In driving a car, u-turn or left turn makes the speed of car decrease considerably or require more waiting time at the cross for the traffic signal to turn green. A more straight path, therefore, is probably faster to arrive at the destination than zig-zaged path with same distance. Previous works related to the path navigation do not consider the straightness of the path. In this paper, we have proposed the path navigation algorithm with turn heuristic for enhancing the straightness of a path. We have implement the proposed algorithm and compared it with a traditional $A^*$ algorithm. The experimental result shows that the degree of the straightness of a path is enhanced by 30% and the navigation distance of a path is deceased by 3.3%.

• Proceedings of the Materials Research Society of Korea Conference
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• 2008.11a
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• pp.12.1-12.1
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• 2008

• Journal of Intelligence and Information Systems
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• v.15 no.3
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• pp.137-150
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• 2009

This paper proposes the hybrid heuristic method to apply the opportunity time to solve the vehicle routing and scheduling problem with time constraints(VRSPTW). The opportunity time indicates the idle time which remains after the vehicle performs the unloading service required by each customer's node. In this proposed heuristic, we add the constraints to VRSPTW model for the opportunity time. We also obtain the initial solution by applying the cost evaluation function to the insertion strategy considering the opportunity time. In addition, we improve the former result by applying the opportunity time to the tabu search strategy by swapping the customer's node. Finally, we suggest the construction strategies of initial routing which can efficiently acquire the nearest optimal solution from various types of data in terms of geographical condition, scheduling horizon and vehicle capacity. Our experiment show that our heuristic can get the nearest optimal solution more efficiently than the Solomon's I1 heuristic.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.3
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• pp.117-126
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• 2016

In this study, a new meta-heuristic optimization algorithm, Vision Correction Algorithm (VCA), designed according to the optical properties of glasses was developed. The VCA is a technique applying optometry and vision correction procedure to optimization algorithm through the process of myopic/hyperopic correction-brightness adjustment-compression enforcement-astigmatism adjustment. The proposed VCA unlike the conventional meta-heuristic algorithm is an automatically adjusting global/local search rate and global search direction based on accumulated optimization results. The proposed algorithm was applied to the representative optimization problem (mathematical and engineering problem) and results of the application are compared with that of the present algorithms.

• Journal of Intelligence and Information Systems
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• v.6 no.2
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• pp.1-14
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• 2000

The berth and crane scheduling problem in a container terminal encompasses the whole process of assigning berth to each ship, determining the duration of berthing, assigning container cranes to each ship, and determining the specific start and end time of each crane service, for all the ships scheduled to be arriving at the terminal during a certain scheduling horizon. This problem is basically a constraint satisfaction problem in which cranes and berths should be assigned in such a way that all the spatial and temporal constraints are satisfied without any interference. However, it is also an optimization problem because the requested arrival and departure time should be met for as many of the scheduled ships as possible, while the operation cost of the terminal should be minimized. In this paper, we present an effective and efficient approach to solving this type of problem, which combines constrain satisfaction search and heuristic repair. We first employ a constraint satisfaction search to find a feasib1e solution. Then, the feasible solution is modified to a more optimal one by iteratively applying our heuristic repair operations within the framework of constraint satisfaction search. Experimental results with a real data from Pusan East Container Terminal showed that our approach can derive a schedule of satisfactory quality in a very short time.