• Journal of Korea Multimedia Society
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• v.9 no.1
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• pp.119-126
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• 2006

The task scheduling in multiprocessor system is one of the key elements in the effective utilization of multiprocessor systems. The optimal assignment of tasks to multiprocessor is, in almost practical cases, an NP-hard problem. Consequently algorithms based on various modern heuristics have been proposed for practical reason. This paper proposes a new task scheduling algorithm using Genetic Algorithm which combines simulated annealing (GA+SA) in multiprocessor environment. In solution algorithms, the Genetic Algorithm (GA) and the simulated annealing (SA) are cooperatively used. In this method, the convergence of GA is improved by introducing the probability of SA as the criterion for acceptance of new trial solution. The objective of proposed scheduling algorithm is to minimize makespan. The effectiveness of the proposed algorithm is shown through simulation studies. In simulation studies, the result of proposed algorithm is better than that of any other algorithms.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.41 no.2
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• pp.105-116
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• 2018

In this study, we consider the assembly line balancing (ALB) problem which is known as an very important decision dealing with the optimal design of assembly lines. We consider ALB problems with soft constraints which are expected to be fulfilled, however they are not necessarily to be satisfied always and they are difficult to be presented in exact quantitative forms. In previous studies, most researches have dealt with hard constraints which should be satisfied at all time in ALB problems. In this study, we modify the mixed integer programming model of the problem introduced in the existing study where the problem was first considered. Based on the modified model, we propose a new algorithm using the genetic algorithm (GA). In the algorithm, new features like, a mixed initial population selection method composed of the random selection method and the elite solutions of the simple ALB problem, a fitness evaluation method based on achievement ratio are applied. In addition, we select the genetic operators and parameters which are appropriate for the soft assignment constraints through the preliminary tests. From the results of the computational experiments, it is shown that the proposed algorithm generated the solutions with the high achievement ratio of the soft constraints.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2008.05a
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• pp.793-796
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• 2008

본 논문은 생산 물류 시스템최적화의 실현에 가장 대표적인 생산수송계획문제인 수송문제(TP: Transportation Problem)에 고정비용을 고려한 고정비용 수송문제(fcTP: Fixed charge Transportation Problem)를 다룬다. 특히 NP-hard문제로 널리 알려진 TP에서 수송량에 비례하는 가변비용과 함께 추가적으로 모든 경로에서 발생하는 고정비용을 함께 고려한 fcTP를 다룬다. 따라서 이러한 fcTP를 해결하기 위해 메타 휴리스틱기법 중에 가장 널리 이용되고 있는 유전 알고리즘(CA: Genetic Algorithm)을 이용한 해법을 제시하고자 한다. 본 논문에서는 CA를 이용해 고정비용 수송문제의 해를 우선순위기반 유전자 표현법을 이용해 fcTP에 적용해 보고 수치 실험을 통해 그 성능에 대한 연구를 한다.

• Journal of KIISE:Software and Applications
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• v.32 no.4
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• pp.247-257
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• 2005

Generally, the network topology design problem is characterized as a kind of NP-hard combinatorial optimization problem, which is difficult to solve with the classical method because it has exponentially increasing complexity with the augmented network size. In this paper, we propose the efficient approach with two phase that is comprised of evolutionary computation approach based on Prufer number(PN), which can efficiently represent the spanning tree, and a heuristic method considering 2-connectivity, to solve the highly reliable network topology design problem minimizing the construction cost subject to network reliability: firstly, to find the spanning tree, genetic algorithm that is the most widely known type of evolutionary computation approach, is used; secondly, a heuristic method is employed, in order to search the optimal network topology based on the spanning tree obtained in the first Phase, considering 2-connectivity. Lastly, the performance of our approach is provided from the results of numerical examples.

• Journal of Internet Computing and Services
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• v.6 no.6
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• pp.99-106
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• 2005

The task scheduling in multiprocessor system Is one of the key elements in the effective utilization of multiprocessor systems. The optimal assignment of tasks to multiprocessor is, in almost all practical cases, an NP hard problem. Consequently various modern heuristics based algorithms have been proposed for practical reason. Recently, several approaches using Genetic Algorithm (GA) are proposed. However, these algorithms have only one objective such as minimizing cost and makespan. This paper proposes a new task scheduling algorithm using Genetic Algorithm combined simulated annealing (GA+SA) on multiprocessor environment. In solution algorithms, the Genetic Algorithm (GA) and the simulated annealing (SA) are cooperatively used. In this method. the convergence of GA is improved by introducing the probability of SA as the criterion for acceptance of new trial solution. The objective of proposed scheduling algorithm is to minimize makespan and total number of processors used. The effectiveness of the proposed algorithm is shown through simulation studies. In simulation studies, the results of proposed algorithm show better than that of other algorithms.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2005.10a
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• pp.321-333
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• 2005

With the limited frequency spectrum and an increasing demand for cellular communication services, the problem of channel assignment becomes increasingly important. However, finding a conflict free channel assignment with the minimum channel span is NP hard. As demand for services has expanded in the cellular segment, sever innovations have been made in order to increase the utilization of bandwidth. The innovations are cellular concept, dynamic channel assignment and hierarchical network design. Hierarchical network design holds the public eye because of increasing demand and quality of service to mobile users. We consider the frequency assignment problem and the base station placement simultaneously. Our model takes the candidate locations emanating from this process and the cost of assigning a frequency, operating and maintaining equipment as an input. In addition, we know the avenue and demand as an assumption. We propose the network about the profit maximization. This study can apply to GSM(Global System for Mobile Communication) which has 70% portion in the world. Hierarchical network design using GA(Genetic Algorithm) is the first three-tier (Macro, Micro, Pico) model, We increase the reality through applying to EMC (Electromagnetic Compatibility Constraints). Computational experiments on 72 problem instances which have 15${\sim}$40 candidate locations demonstrate the computational viability of our procedure. The result of experiments increases the reality and covers more than 90% of the demand.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.20 no.42
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• pp.171-179
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• 1997

Genetic Algorithms face an inherent conflict between exploitation and exploration. Exploitation refers to taking advantage of information already obtained in the search. Exploration show that a pattern in bits coupled with another pattern elsewhere in the string is more effective. In this paper shows that the selection method has a major impact on the balance between exploitation and exploration. A more heavy-handed approach seeks to exploit the available information. If decisions must be made quickly, especially those in real-time trading environments, then quicker convergence through exploitation may be more desirable. Also this paper we present some theoretical and empirical the selection method in genetic algorithms for a GA-hard problem.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.03a
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• pp.81-84
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• 1998

Genetic Algorithms (GAs) are stochastic algorithms whose search methods model some natural phenomena. The procedure of GAs may be divided into two sub-procedures : Operation and Selection. Chromosomes can produce new offspring by means of operation, and the fitter chromosomes can produce more offspring than the less fit ones by means of selection. However, operation which is executed randomly and has some limits to its execution can not guarantee to produce fitter chromosomes. Thus, we propose a method which gives a directional information to the genetic operator by reinforcement learning. It can be achived by using neural networks to apply reinforcement learning to the genetic operator. We use the amount of fitness change which can be considered as reinforcement signal to calcualte the error terms for the output units. Then the weights are updated using backpropagtion algorithm. The performance improvement of GAs using reinforcement learning can be measured by applying the pr posed method to GA-hard problem.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.3
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• pp.185-191
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• 2022

This paper proposed a division algorithm of performance complexity $O{\frac{n(n+1)}{2}}$ for a change-making problem(CMP) in which polynomial time algorithms are not known as NP-hard problem. CMP seeks to minimize the sum of the x_j number of coins exchanged when a given amount of money C is exchanged for c_j,j=1,2,⋯,n coins. Known polynomial algorithms for CMPs are greedy algorithms(GA), divide-and-conquer (DC), and dynamic programming(DP). The optimal solution can be obtained by DP of O(nC), and in general, when given C>2ⁿ, the performance complexity tends to increase exponentially, so it cannot be called a polynomial algorithm. This paper proposes a simple algorithm that calculates quotient by dividing upper triangular matrices and main diagonal for k×n matrices in which only j columns are placed in descending order of c_j of n for c_j ≤ C and i rows are placed k excluding all the dividers in c_j. The application of the proposed algorithm to 39 benchmarking experimental data of various types showed that the optimal solution could be obtained quickly and accurately with only a calculator.

• Journal of the Korean Institute of Landscape Architecture
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• v.15 no.3
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• pp.11-19
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• 1988

The purpose of this study is to investigate ways to control the wind dispersal of contton-wrapped seeds of such poplars and willows as Poplus alba, P. tomentiglandulosa, P. euramericana, P. deltoides, and Salix pseudo-lasiogyne. These trees are hated by many people because of their seeds blowing all over the place during May. These cottony seeds can be nuisance to various types of outdoor activities, pose safely threat to drivers, become fire hazards during prolonged spring dry spells, and cause many types of health hazards of allergy such as sneezing. rhinitis, asthma, and skin rashes. Four control methods can be used to resolve the problem. First, pruning can be a solution, but it is unsatisfactory in terms of costs and outcome. Second, planting of male trees only can be a solution, but it is hard to identify sexes of saplings. Third, female trees can he replaced with other species. But it requires high costs and takes at least ten years to functionally replace the removed ones. As an alternative to such unsatisfactory control methods, the possibility of applying plant growth regulators has been investigated since 1983. During the pre-test, various concentrations and mixtures of them were either sprayed or injected, but failed to achieve any promising results. But the injection of a mixture made up of 0.75g of 2-chloroethane phosphonic acid with 0.2 mg of GA in 300cc water in the end of March produced premature falling of almost all aments and capsules of treated poplars and willows. It was found that the effect of the injection lasts two years. The results of the main experiment of 1987 can be summarized as follows ; First, the injection of the mixture of 2-chloroethane phosphonic acid and GA increases the premature abscisin of aments and capsules, thus reducing the wind dispersal of the cottony seeds of S. pseudolasiogyne, P. tomentiglandulosa, and P. euramericana 1644.09, 1200.61, and 1485.11 times, respectively, than that of the naural abscisin, It is estimated that the average number of wind-blown seeds reduced are approximatively 6,185,100, 4062,900, and 2,830,670 per tree, respectively. Second, the treatment causes no observable side effects on the growth of the samples tested.