• Journal of KIISE:Computer Systems and Theory
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• v.31 no.1_2
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• pp.95-101
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• 2004

The scheduling problem that maps independent tasks to heterogeneous resources in distributed computing systems is known as NP-complete［1］. GRID［2］ is an example of distributed systems that consisted of heterogeneous resources. Many algorithms to solve this problem have been presented［1,3,4,5］. The scheduling algorithm can be classified into static scheduling algorithms and dynmic scheduling algorithms. A dynamic scheduling algorithm can be used when we can not predict the priority of tasks. Moreover, a dynamic scheduling algorithm can be divided into on-line mode algorithm and batch mode algorithm according to the scheduling time［1,6］. In this paper, we propose a new on-line mode scheduling algorithm. By extensive simulation, we can see that our scheduling algorithm outperforms previous scheduling algorithms.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.4
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• pp.138-144
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• 2020

The Army's engineers are carrying out a range of operations using various equipment, of which, artillery unit support is the representative engineering operation field. The main task of the artillery unit is to attack the enemy's center with firepower from the rear of a friendly force. The artillery must move its original position after firing several times to prevent exposure of the shooting position. This paper proposed a mathematical model and heuristic algorithm that can be used to determine the optimal allocation among engineer equipment, the team (work), and position while reflecting the constraints of the construction of an artillery position. The model proposed in this paper derived the optimal solution for the small size problems, but it takes a long time to derive the optimal solution for the problem of equipment placement of the engineer battalion and brigade scale. Although the heuristic suggested in this study does not guarantee the optimal solution, the solution could be obtained in a reasonable amount of time.

• The Transactions of the Korea Information Processing Society
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• v.4 no.6
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• pp.1550-1556
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• 1997

In this paper, we apply the evolutionary algorithm to the combinatorial optimization problem. Evolutionary algorithm useful for the optimization of the large space problem. This paper propose a method for the reuse of wastes of light water in atomic reactor system. These wastes contain several reusable elements, and they should be carefully selected and blended to satisfy requirements as an input material to the heavy water atomic reactor system. This problem belongs to an NP-hard like the 0/1 knapsack problem. Two evolutionary strategies are used as approximation 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 perform the feasible test and solution evaluation by using the vectored knowledge in problem domain. Finally, We compare the simulation results of using random operator and vector operator for such combinatorial optimization problems.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.16 no.1
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• pp.245-251
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• 2016

This paper suggests a heuristic algorithm to obtain optimal solution of minimum number of aircraft delay in multi-airport arrivals/departures problem. This single airport arrivals/departures problem can be solved by mathematical optimization method only. The linear programming or genetic algorithm that is a kind of metaheuristic method is used for a multi-airport arrivals/departures problem. Firstly, the proposed algorithm selects the median minimum delays capacity in various arrivals/departures capacities at an airport for the number of aircraft in $i^{th}$ time interval (15 minutes) at each airport. Next, we suggest reallocate method for arrival aircraft between airports. This algorithm better result of the number of delayed aircraft then genetic algorithm.

• 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.

• The KIPS Transactions:PartB
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• v.18B no.1
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• pp.39-44
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• 2011

In this paper, we propose a method to improve the Firefly Algorithm(FA) introduced by Xin-She Yang, recently. We design and analyze the improved firefly algorithm based on the heuristic. We compare the FA with the Particle Swarm Optimization (PSO) which the problem domain is similar with the FA in terms of accuracy, algorithm convergence time, the motion of each particle. The compare experiments show that the accuracy of FA is not worse than PSO's, but the convergence time of FA is slower than PSO's. In this paper, we consider intuitive reasons of slow convergence time problem of FA, and propose the improved version of FA using a partial mutation heuristic based on the consideration. The experiments using benchmark functions show the accuracy and convergence time of the improved FA are better than them of PSO and original FA.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.6
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• pp.33-41
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• 2021

This paper proposes heuristic greedy algorithm that can be find the solution within polynomial time with solution finding rule for the capacitated minimum spanning tree(CMST) problem, known as NP-hard. The CMST problem can be solved by computer-aided meta-heuristic because of the Esau-Williams heuristic polynomial time algorithm has a poor performance. Nevertheless the meta-heuristic methods has a limit performance that can't find optimal solution. This paper suggests visual by handed solution-finding rule for CMST. The proposed algorithm firstly construct MST, and initial feasible solution of CMST from MST, then optimizes the CMST with the subtree gates more adjacent to root node. As a result of total 30 cases of OR-LIB 10 data, Q=3,5,10, the proposed algorithm gets the best performance.

• KIPS Transactions on Software and Data Engineering
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• v.7 no.1
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• pp.1-8
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• 2018

Software testing is important to determine the reliability of the system, a task that requires a lot of effort and cost. Model-based testing has been proposed as a way to reduce these costs by automating test designs from models that regularly represent system requirements. For each path of model to generate an input value to perform a test, meta-heuristic technique is used to find the test data. In this paper, we propose an automatic test data generation method using a slicing method and a priority policy, and suppress unnecessary computation by excluding variables not related to target path. And then, experimental results show that the proposed method generates test data more effectively than conventional method.

• Journal of Korea Game Society
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• v.11 no.5
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• pp.23-30
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• 2011

Flocking behaviors are frequently used in games and computer graphics for realistic simulation of massive crowds. Since simulation of massive crowds in real time is a computationally intensive task, there were many researches on efficient algorithm. In this paper, we find experimentally the fact that there are unnecessary computations in the previous efficient flocking algorithm, and propose a noble algorithm that overcomes the weakness of the previous algorithm with a simple heuristic. A number of experiments were conducted to evaluate the performance of the proposed algorithm. The experimental results showed that the proposed algorithm outperformed the previous efficient algorithm by about 21% on average.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.11
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• pp.2072-2077
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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 fester 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%.