• Journal of Intelligence and Information Systems
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• v.3 no.2
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• pp.1-9
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• 1997

These papers propose an evolutionary algorithm for re-using output of waste fuel of light water reactor system in nuclear power plants. Evolutionary algorithm is useful for optimization of the large space problem. The wastes contain several re-useable elements, and they should be carefully selected and blended to satisfy requirements as input material to the heavy water nuclear reactor system. This problem belongs to a NP-hard like the 0/1 Knapsack problem. Two evolutionary strategies are used as a, pp.oximation algorithms in the highly constrained combinatorial optimization problem. One is the traditional strategy, using random operator with evaluation function, and the other is heuristic based search that uses the vector operator reducing between goal and current status. We also show the method, which performs the feasible teat and solution evaluation by using the vectorized data in problem. Finally, We compare the simulation results of using random operator and vector operator for such combinatorial optimization problems.

• Journal of the Korea Society of Computer and Information
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• v.19 no.3
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• pp.45-54
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• 2014

In this paper, an improved two-step P2P traffic classification scheme is proposed to overcome the limitations of the existing methods. The first step is a signature-based classifier at the packet-level. The second step consists of pattern heuristic rules and a statistics-based classifier at the flow-level. With pattern heuristic rules, the accuracy can be improved and the amount of traffic to be classified by statistics-based classifier can be reduced. Based on the analysis of different decision tree algorithms, the statistics-based classifier is implemented with REPTree. In addition, the ensemble algorithm is used to improve the performance of statistics-based classifier Through the verification with the real datasets, it is shown that our hybrid scheme provides higher accuracy and lower overhead compared to other existing schemes.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.5
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• pp.1716-1729
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• 2010

In this paper, we deal with a hierarchical ring-mesh optical network design problem. The objective is to minimize the total cost of optical add-drop multiplexers (OADMs) handling intra-ring traffic, optical cross-connects (OXCs) handling inter-ring traffic, and cabling cost among OADMs and among OXCs, while satisfying intra-ring and inter-ring capacities. We develop an integer programming (IP) formulation for the problem and devise some cutting planes that partially break the symmetry of rings. Dealing with the inherent computational complexity of the problem, we devise an effective heuristic procedure that finds a good quality feasible solution within reasonable computing times. Computational results demonstrate the efficacy of the proposed solution procedure; the developed symmetry breaking inequalities significantly reduce the computing time to find an optimal solution for small size problems, and the heuristic procedure finds a better feasible solution than that CPLEX, a commercial optimization software, finds for large size problems.

• Journal of KIISE:Computer Systems and Theory
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• v.30 no.9
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• pp.477-486
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• 2003

In combinatorial auctions buyers nay bid for arbitrary combinations of goods. But determining the winners of combinatorial auctions who maximize the profit of a seller is known to be NP-complete. A branch-and-bound method can be one of practical algorithm for winner determination. However, bid selection heuristics play a very important role in the efficiency of a branch-and-bound method. In this paper, we designed and implemented an algorithm which used a branch-and-bound method and Linear Programming for winner determination in combinatorial auctions. We propose new bid selection heuristics which consider a branching bid and conflicting bids simultaneously to select a branching bid in the algorithm. In addition, upper bounds are reused to reduce the running time in specific cases. We evaluated the performance of the algorithm by experiments with five data distributions and compared our method with others. The algorithm using heuristics showed a superior efficiency in two data distributions and a similar efficiency in three distributions.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.9
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• pp.6290-6296
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• 2015

More informative ones of heuristics can help to conduct search more efficiently to obtain solution plan. However, in general, to derive highly informative heuristics from problem specifications requires lots of computational effort. To address this problem, we propose an State-Action based Planning Graph(SAPG) and Action-based heuristics for solving planning problems more efficiently. The SAPG is an extended one to be applied to can find interactions between subgoal & goal conditions from the relaxed planning graph which is a common means to get heuristics for solving the planning problems, Action-based heuristics utilizing SAPG graphs can find interactions between subgoal & goal conditions in an effective way, and then consider them to estimate the goal distance. Therefore Action-based heuristics have more information than the existing max and additive heuristics, also requires less computational effort than the existing overlap heuristics. In this pager. we present the algorithm to compute Action-based heuristics, and then explain empirical analysis to investigate the accuracy and the efficiency of the Action-based heuristics.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.41 no.3
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• pp.11-16
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• 2004

Recently, collaborative works are increased more and more over the distributed heterogeneous computing environments. The availability of high-speed wide-area networks has also enabled collaborative multimedia applications such as video conferencing, distributed interactive simulation and collaborative visualization. Distributed high performance computing and collaborative multimedia applications, it is extremely important to efficiently perform group communication over a heterogeneous network. Typical group communication patterns are broadcast and Multicast. Heuristic algorithms such as FEF, ECEF, look-ahead make up the message transmission tree for the broadcast and multicast over the distributed heterogeneous systems. But, there are some shortcomings because these select the optimal solution at each step, it may not be reached to the global optimum In this paper, we propose a new heuristic algerian that constructs tree for efficiently collective communication over the previous heterogeneous communication model which has heterogenity in both node and network. The previous heuristic algorithms my result in a locally optimal solution, so we present more reasonable and available criterion for choosing edge. Through the performance evaluation over the various communication cost, improved heuristic algorithm we proposed have less completion time than previous algorithms have, especially less time complexity than look-ahead 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%.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.11
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• pp.2367-2372
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• 2009

Recently, Many studies have been progressing to path-finding adapted dynamic information on the Terminal Based Navigation System(TBNS). The algorithms proposed are based on $A^*$ algorithm. Path-finding algorithms which use heuristic function may occur a problem of the increase of exploring cost. Path-finding with an abstract graph which expresses real road network as a simple graph is proposed for reducing dependency of heuristic and exploring cost. In this paper, two abstract graph that are different method of construction, Homogeneous Node merging($AG^H$) and Connected Node Merging($AG^C$), are implemented. In result of evaluation of performance, $AG^C$ has better performance than $AG^H$ at construction cost and the number of node access but $AG^C$ has worse performance than AGH at exploring cost.

• Journal of the Korea Society for Simulation
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• v.19 no.2
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• pp.91-98
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• 2010

Conventional path-finding has focused on finding short collision-free paths. However, as computer games become more sophisticated, it is required to take tactical information like ambush points or lines of enemy sight into account. One way to make this information have an effect on path-finding is to represent a heuristic function of a search algorithm as a weighted sum of tactics. In this paper we consider the problem of learning heuristic to optimize path-finding based on given tactical information. What is meant by learning is to produce a good weight vector for a heuristic function. Training examples for learning are given by a game level-designer and will be compared with search results in every search level to update weights. This paper proposes a learning algorithm integrated with search for tactical path-finding. The perceptron-like method for updating weights is described and a simulation tool for implementing these is presented. A level-designer can mark desired paths according to characters' properties in the heuristic learning tool and then it uses them as training examples to learn weights and shows traces of paths changing along with weight learning.

• The KIPS Transactions:PartC
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• v.10C no.3
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• pp.367-376
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• 2003

This study deals with the DCMST (Delay constrained Capacitated Minimum Spanning Tree) problem applied in the topological design of local networks or finding several communication paths from root node. While the traditional CMST problem has only the traffic capacity constraint served by a port of root node, the DCMST problem has the additional mean delay constraint of network. The DCMST problem consists of finding a set of spanning trees to link end-nodes to the root node satisfying the traffic requirements at end-nodes and the required mean delay of network. The objective function of problem is to minimize the total link cost. This paper presents two-phased heuristic algorithm, which consists of node exchange, and node shift algorithm based on the trade-off criterions, and mean delay algorithm. Actual computational experience and performance analysis show that the proposed algorithm can produce better solution than the existing algorithm for the CMST problem to consider the mean delay constraint in terms of cost.