• The Journal of the Korea Contents Association
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• v.11 no.3
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• pp.100-107
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• 2011

One of the important fields for heuristic algorithm is how to balance between Intensification and Diversification. In this paper, we deal with the performance improvement techniques through balance the intensification and diversification in Ant Colony System(ACS) which is one of Ant Colony Optimization(ACO). In this paper, we propose the hybrid searching method between intensification strategy and diversification strategy. First, the length of the global optimal path does not improved within the limited iterations, we evaluates this state that fall into the local optimum and selects the next node using changed parameters in the state transition rule. And then we consider the overlapping edge of the global best path of the previous and the current, and, to enhance the pheromone for the overlapping edges increases the probability that the optimal path is configured. Finally, the performance of Best and Average-Best of proposed algorithm outperforms ACS-3-opt, ACS-Subpath, ACS-Iter and ACS-Global-Ovelap algorithms.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.7
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• pp.943-948
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• 2018

In this paper, we propose a hybrid approach of combining $A^*$ and Genetic algorithm in the path search problem. In $A^*$, the cost from a start node to the intermediate node is optimized in principle but the path from that intermediate node to the goal node is generated and tested based on the cumulated cost and the next node in a priority queue is chosen to be tested. In that process, we adopt the genetic algorithm principle in that the group of nodes to generate the next node from an intermediate node is tested by its fitness function. Top two nodes are selected to use crossover or mutation operation to generate the next generation. If generated nodes are qualified, those nodes are inserted to the priority queue. The proposed method is compared with the original sequential selection and the random selection of the next searching path in $A^*$ algorithm and the result verifies the superiority of the proposed method.

• Journal of the Korea Society of Computer and Information
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• v.16 no.3
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• pp.203-210
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• 2011

Ant Colony System is a new meta heuristics algorithms to solve hard combinatorial optimization problems. 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. In this paper, we propose the searching method to consider the overlapping edge of the global best path of the previous and the current. This method is that we first determine the overlapping edge of the global best path of the previous and the current will be configured likely the optimal path. And, to enhance the pheromone for the overlapping edges increases the probability that the optimal path is configured. Finally, the performance of Best and Average-Best of proposed algorithm outperforms ACS-3-opt, ACS-Subpath and ACS-Iter algorithms.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.10 no.5
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• pp.215-221
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• 2010

Hopfield networks have been proposed as a new computational tool for finding the shortest path of networks. Zhang and Ali studied the method of finding shortest path by expended neurons of Hopfield networks. Ali Algorithm is well known as the tool with the neurons of branch numbers. Where a network grows bigger, it needs much more time to solve the problem by Ali algorithm. This paper modifies the method to find the synapse matrix and the input bias vector. And it includes the eSPN algorithm after proper iterations of the Hopfield network. The proposed method is a tow-stage method and it is more efficient to find the shortest path.The proposed method is verified by three sample networks. And it could be more applicable then Ali algorithm because it's fast and easy. When the cost of brach is changed, the proposed method works properly. Therefore dynamic cost-varing networks could be used by the proposed method.

• Journal of the Korean Institute of Intelligent Systems
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• v.8 no.4
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• pp.27-35
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• 1998

Navigation is a method to direct a mobile robot without collision when traversing the environment. This is to reach a destination without getting lost. In this paper, global and local path planning in fixed obstacle and moving obstacle using genetic algorithm are presented. First, mobile robot searches optimal global path using genetic algorithm without falling into local minima. Then if it finds a unknown obstacle, it searches new path without crashing obstacle. Also if there is a moving obstacle, mobile robot searches new optimal path without colliding with the obstacles. Various simulation results show the proposed algorithm can search a shortest path effectively.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.10a
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• pp.71-73
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• 2017

논문에서는 최적화 문제를 해결하는 기법의 하나인 $A^*$와 유전자 알고리즘을 이용하여 모든 노드를 탐색하여 최적의 경로를 도출하는 최적화 경로 탐색 방법을 제안한다. 경로를 도출하기 위해 $A^*$ 알고리즘을 적용하여 출발지 노드로부터 중간 경로 노드까지의 거리를 측정하여 개체를 생성한다. 출력 노드들을 도출하기 위해 생성된 개체를 적합도 함수에 적용하여 적합도를 계산한다. 계산된 적합도 값에 따라 교배를 할 노드 및 교배 지점을 선택한다. 선택된 노드와 교배 지점을 이용하여 개체들을 교배한다. 교배를 통해 새로운 개체를 생성한다. 새로운 개체가 적합도 조건에 만족하면 출력 노드로 도출하고, 다음 출력 노드를 도출하기 위한 출발지 노드로 선택한다. 이러한 과정을 반복하여 모든 출력 노드를 도출한다. 제안된 방법을 경로 탐색 문제를 대상으로 실험한 결과, $A^*$ 알고리즘만을 이용한 경우보다 제안된 방법이 경로 탐색 문제에 있어서 최적화된 거리를 기반으로 경로를 탐색하는 것을 확인하였다.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2007.12a
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• pp.226-227
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• 2007

Significant increase of container flows in marine terminals requires more efficient automatic port systems. This paper presents a novel routing and collision avoidance algorithm of linear motor based shuttle cars using random access sequence dynamic programming (RAS DP). The proposed RAS DP is accomplished online for determining optimal paths for each shuttle car.

• Journal of Korea Game Society
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• v.9 no.4
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• pp.81-88
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• 2009

In this paper, I suggested a way for searching a path of the intelligent character in an action game by using a genetic algorithm. This realized the algorithm which enables not only to chose the nearest path but also to search the optimum path by using genetic algorithm. In this case, if the codes of chromosomes are applied as they are, a lot of lethal genes could occur. In order to solve such a problem, I used a splicing method, one of the DNA's behavior characteristics. The intelligent character searched out a optimum pass as well as a shortcut path with one treatment by using the characteristic of a genetic algorithm which generates multiple candidate solutions in the search process.

• Proceedings of the Korean Information Science Society Conference
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• 1998.10a
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• pp.636-638
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• 1998

WDM망을 구성하는데 있어서 광학적 파장은 가장 중요한 자원의 하나이다. 그러나 주어진 통신 요구를 모두 수용하면서 동시에 최소한의 파장만을 사용하는 WDM망의 설계 문제는 이미 NP-complete 계열의 문제인 것으로 밝혀졌으며 많은 휴리스틱 알고리즘들이 제안되었다. 본 논문에서는 임의의 물리적 망 위상(topology)과 완전 연결(full connection)형태의 통신 요구가 주어질 경우, 요구되는 파장 경로(Wavelength Path, lightpath)를 확립하기 위한 방법으로써 각 노드 사이의 최단 거리 경로를 기반으로 하여 탐색 공간을 만들고 구성된 탐색 공간 내에서 Branch-and-bound 탐색방식을 수행하는 파장 경로 설정 알고리즘을 제안한다. Branch-and-bound탐색방식은 초기에 좋은 bound조건을 가질 경우 주어진 시간 안에 보다 넓은 탐색 공간을 검색할 수 있으므로 최초의 탐색에서 가능한 좋은 성능의 파장 경로 설정을 발견할 수 있어야한다. 시뮬레이션 실험을 통하여 최초의 탐색에서 발견한 파장 경로 설정과 구성된 탐색 공간내의 최적해를 얻고, cut-set를이용하여 요구 파장 개수의 하위 한계값을 계산한후, 이를 상호 비교하여 제안된 알고리즘의 성능을 평가한다.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.10
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• pp.109-117
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• 2014

In navigation route planning systems using A* algorithms, the cardinality of an Open list, which is a list of candidate nodes through which a terminal node can be accessed, increases as the path length increases. In this paper, a method of alternately utilizing the Dijkstra's algorithm and the A* algorithm to reduce the cardinality of the Open list is investigated. In particular, by employing a depth parameter, named Level, the two algorithms are alternately performed depending on the Level's value. Using the hybrid searching approach, the Open list constructed in the Dijkstra's algorithm is transferred into the Open list of the A* algorithm, and consequently, the unconstricted increase of the cardinality of the Open list of the former algorithm can be avoided and controlled appropriately. In addition, an optimal or nearly optimal path similar to the Dijkstra's route, but not available in the A* algorithm, can be found. The experimental results, obtained with synthetic and real-life benchmark data, demonstrate that the computational cost, measured with the number of nodes to be compared, was remarkably reduced compared to the traditional searching algorithms, while maintaining the similar distance to those of the latter algorithms. Here, the values of Level were empirically selected. Thus, a study on finding the optimal Level values, while taking into consideration the actual road conditions remains open.