• Journal of KIISE:Databases
• /
• v.33 no.7
• /
• pp.715-726
• /
• 2006

Web service technologies provide a standard means for interoperation and integration of heterogeneous applications distributed over the Internet. For efficient execution of hierarchically interacting composite web services, this paper proposes an approach to distribute web service invocations over peer systems effectively, exploiting intensional XML data embedding external service calls as a result of well services. A cost-based optimization problem on the execution of web services using intensional results was formalized, and a heuristic search method to find an optimal solution and a greedy algorithm to generate an efficient invocation plan quickly were suggested in this paper. Experimental evaluation shows that the proposed greedy algorithm provides near-optimal solutions in an acceptable time even for a large number of Web services.

• Proceedings of the Korea Inteligent Information System Society Conference
• /
• 2002.11a
• /
• pp.502-509
• /
• 2002

Maximal Covering 문제(MCP)란 행렬 상에서 n개의 열(column) 중 p개를 선택하여 m개의 행(row)중 최대한 많은 행을 cover하는 문제로 정의된다. 본 논문에서는 MCP를 유전 알고리즘(Genetic Algorithm)으로 해결하기 위해 문제에 적합하게 설계된 교차 연산자(crossover operator)와 비발현 유전인잔(unexpressed gene)를 가진 새로운 염색체 구조를 제시한다. 해결하고자 하는 대상 MCP의 규모가 매우 큰 경우 전통적인 임의교차(random crossover) 방법으로는 좋은 결과를 얻기가 힘들다. 따라서 본 연구에서는 그리디 교차(greedy crossover) 방법을 제시하여 문제를 해결한다. 그러나 이러한 그리디 교차를 사용하더라도 조기 수렴 등의 문제로 인해 타부 탐색 등의 이웃해 탐색 방법에 비해 그리 좋은 결과를 얻기가 힘들다. 본 논문은 이러한 조기 수렴 문제를 해결하고 다른 이웃에 탐색 방법보다 더 좋은 결과를 얻기 위해 비발현 유전인자(unexpressed gene)를 가진 염색체를 도입하여 해결함을 특징으로 한다. 비발현 유전인자는 교차 과정에서 자식 염색체의 유전인자로 전달되지 않은 정보 중 나중에라도 유용할 가능성이 보이는 정보를 보존하는 역할을 하여 조기 수렴 문제를 해결하는데 도움을 주어 보다 나은 결과를 얻을 수 있게 해준다. 대규모 MCP를 해결하는 실험에서 새로운 비발현 유전인자를 적용한 유전 알고리즘이 기존의 유전 알고리즘뿐만 아니라 다른 탐색 기법에 비해 더욱 좋은 성능을 보여줌을 확인하였다.

• The KIPS Transactions:PartA
• /
• v.16A no.1
• /
• pp.35-42
• /
• 2009

This paper proposes a new coverage algorithm for intelligent robot. Many algorithms for improving the performance of coverage have been focused on minimizing the total coverage completion time. However, if one does not have enough time to finish the whole coverage, the optimal path could be different. To tackle this problem, we propose a new coverage algorithm, which we call MaxCoverage algorithm, for covering maximal area within the deadline. The MaxCoverage algorithm decides the navigation flow by greedy algorithm for Set Covering Problem. The experimental results show that the MaxCoverage algorithm performs better than other algorithms for random deadlines.

• Journal of the Korea Institute of Military Science and Technology
• /
• v.23 no.4
• /
• pp.381-388
• /
• 2020

Weapon target assignment problem is an essential technology for automating the operator's rapid decision-making support in a battlefield situation. Weapon target assignment problem is a kind of the optimization problem that can build up an objective function by maximizing the number of threat target destructed or maximizing the survival rate of the protected assets. Weapon target assignment problem is known as the NP-Complete, and various studies have been conducted on it. Among them, a greedy heuristic algorithm which guarantees (1-1/e) approximation has been considered a very practical method in order to enhance the applicability of the real weapon system. In this paper, we formulated the weapon target assignment problem for supporting decision-making at the level of artillery. The lazy strategy based on hierarchical structure is proposed to accelerate the greedy algorithm. By experimental results, we show that our algorithm is more efficient in processing time and support the same level of the objective function value with the basic greedy algorithm.

• Journal of the Institute of Electronics Engineers of Korea TC
• /
• v.44 no.11
• /
• pp.19-24
• /
• 2007

This paper presents a performance analysis of dynamic channel allocation (DCA) based on the greedy approach (GA) for orthogonal frequency division multiple access (OFDMA) systems over Rayleigh fading channels. The GA-based DCA achieves its performance improvement using multi-user diversity. We analyze the statistics of the number of allocable users (NAU), which represents the multi-user diversity order at each allocation process. The derived statistics are then used to analyze the performance of GA-based DCA. The analysis results show that the number of subcarriers allocated to each user must be equal to achieve the maximum system performance (i.e., based on outage probability and data throughput).

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.36 no.5B
• /
• pp.464-470
• /
• 2011

V2V (Vehicle-to-Vehicle) is a special kind of VANET (Vehicular Ad-hoc Network), which has high mobility and frequent topology changes and causes the link breakage problem. To resolve this problem, geographic routing protocols such as greedy forwarding are proposed. However, the greedy forwarding approach selects the node closest to the destination node as the transfer node within the transmission range so that it may cause many problems owing to many intersections and many changes in vehicular traffic in urban areas. The paper proposes a greedy perimeter urban routing (GPUR) algorithm considering the presence of 2-hop neighbor nodes and the road characteristics. Simulation results using ns-2 reveal that the proposed GPUR algorithm significantly reduces the routing error problem and the probability of local maximum than the existing routing protocols.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.38B no.6
• /
• pp.485-491
• /
• 2013

Recently, with the proliferation of social network services, users and many companies hope that their information spread more faster. In order to study the information diffusion in the social networks, many algorithms such as greedy algorithm and heuristic algorithm have been proposed. However, the greedy algorithm is too complicated to use in real-life social network, and the heuristic algorithms have been studied under the uniform distribution of diffusion probability, which is different from the real social network property. In this paper, we propose an heuristic information diffusion maximization algorithm based on diffusion probability and node degree. For performance evaluation, we use real social network database, and it is verified that our proposed algorithm activates more active nodes than existing algorithms, which enables faster and wider information diffusion.

• Journal of Korea Game Society
• /
• v.7 no.4
• /
• pp.93-104
• /
• 2007

In the complex real-time multi-agent system such as game environment, dynamic task allocations are repeatedly performed to achieve a goal in terms of system efficiency. In this research, we present a task allocation scheme suitable for the real-time multi-agent environment. The scheme is to optimize the task allocation by complementing existing coordination agent with $A^*$ algorithm. The coordination agent creates a status graph that consists of nodes which represent the combinations of tasks and agents, and refines the graph to remove nodes of non-execution tasks and agents. The coordination agent performs the selective utilization of the $A^*$ algorithm method and the greedy method for real-time re-allocation. Then it finds some paths of the minimum cost as optimized results by using $A^*$ algorithm. Our experiments show that the coordination agent with $A^*$ algorithm improves a task allocation efficiency about 25% highly than the coordination agent only with greedy algorithm.

• KIPS Transactions on Software and Data Engineering
• /
• v.5 no.10
• /
• pp.483-488
• /
• 2016

In this paper, we propose a prototype-based classification learning by using the nearest-neighbor rule. The nearest-neighbor is applied to segment the class area of all the training data with hyperspheres, and a hypersphere must cover the data from the same class. The radius of a hypersphere is computed by the mid point of the two distances to the farthest same class point and the nearest other class point. And we transform the prototype selection problem into a set covering problem in order to determine the smallest set of prototypes that cover all the training data. The proposed prototype selection method is designed by a greedy algorithm and applicable to process a large-scale training set in parallel. The prediction rule is the nearest-neighbor rule and the new training data is the set of prototypes. In experiments, the generalization performance of the proposed method is superior to existing methods.

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