• Journal of the Institute of Electronics Engineers of Korea SD
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• v.49 no.8
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• pp.1-9
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• 2012

Quantum-Inspired Evolutionary Algorithm(QEA) contains sufficient data-level parallelism to be naturally accelerated on GPUs. For an efficient reduction of execution time, however, careful task-mapping should be done to properly reflect the characteristics of CPU and GPU. Furthermore, when deciding which part of the application should run on GPU, we need to consider the data transfer between CPU and GPU memory spaces as well as the data-level parallelism. In addition, the usage of zero-copy host memory, proper choice of the execution configuration, and thread organization considering memory coalescing is important to further reduce the execution time. With all these techniques, we could run QEA 3.69 times faster on average in comparison with the multi-threading CPU for the case of 0-1 knapsack problem with 30,000 items.

• 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.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.39 no.1
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• pp.56-63
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• 2016

We consider a satellite mission scheduling problem, which is a promising problem in recent satellite industry. This problem has various considerations such as customer importance, due date, limited capacity of energy and memory, distance of the location of each mission, etc. Also we consider the objective of each satellite such as general purpose satellite, strategic mission and commercial satellite. And this problem can be modelled as a general knapsack problem, which is famous NP-hard problem, if the objective is defined as to maximize the total mission score performed. To solve this kind of problem, heuristic algorithm such as taboo and genetic algorithm are applied and their performance are acceptable in some extent. To propose more efficient algorithm than previous research, we applied a particle swarm optimization algorithm, which is the most promising method in optimization problem recently in this research. Owing to limitation of current study in obtaining real information and several assumptions, we generated 200 satellite missions with required information for each mission. Based on generated information, we compared the results by our approach algorithm with those of CPLEX. This comparison shows that our proposed approach give us almost accurate results as just less than 3% error rate, and computation time is just a little to be applied to real problem. Also this algorithm has enough scalability by innate characteristic of PSO. We also applied it to mission scheduling problem of various class of satellite. The results are quite reasonable enough to conclude that our proposed algorithm may work in satellite mission scheduling problem.

• Journal of the Korea Society of Computer and Information
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• v.12 no.4
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• pp.43-52
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• 2007

As the number of public Web Services increases, there will be many services with the same functionality. These services. however, will vary in their QoS properties, such as price, response time and availability, and it is very important to choose a best service while satisfying given QoS constraints. This paper brings parallel branching and response time constraint of business processes into focus and investigates several service selection plans based on multidimensional multiple choice Knapsack model. Specifically. proposed in the paper are a plan with response time constraints for each execution flow, a plan with a single constraint over the whole service types and a plan with a constraint on a particular execution path of a composite Web Services. Experiments are conducted to observe the performance of each plan with varying the number of services, the number of branches and the values of response time constraint. Experimental results show that reducing the number of candidate services using Pareto Dominance is very effective and the plan with a constraint over the whole service types is efficient in time and solution quality for small to medium size problems.

• 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.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.10a
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• pp.270-273
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• 2017

Productivity through reduced defects in plastic injection molding and reduced cycle times is a long-standing need in the injection industry. In particular, productivity is very urgent for the domestic injection industry, which is caught between the pursuit of latecomers such as China and technological gap with Germany and Japan which will not be narrowed down. Through 30 years of research and experience in the domestic injection industry, we have found that controlling the surface temperature of injection molds is the key of quality control. There have been various attempts to utilize advanced control techniques such as PID control, but the productivity against leading companies in Germany and Japan is still insufficient. Using Approximation Algorithm - "Knapsack" and "Minimum Makespan Scheduling", We want to show how to efficiently control objects with periodic repetitive data patterns that are difficult to solve with PID control. In addition, We want to propose that the control by Approximation Algorithm is effective enough to improve the productivity of the product by analyzing the data extracted from actual injection site.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.15 no.2
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• pp.102-110
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• 2016

After Sewol ferry disaster occurred in April 2014, Korean government began investing to deploy LTE-based public safety network until the year of 2017. In order to reduce the operating and capital costs, resource sharing scheme among public safety network and commercial LTE networks is considered as one of the viable approaches. This thesis proposes a method of allocating bandwidth of public safety network based on various priorities required for disaster scenarios and stages in a resource sharing environment. In order to obtain the highest efficiency, we formulate the bandwidth allocation problem as a Fractional Knapsack Problem. Greedy algorithm was applied to solve the problem. For performance evaluation, we created several disaster scenarios and set suitable parameters for each scenario based on a disaster manual. The proposed method is compared with two typical methods, which are Class-based bandwidth allocation and Uniform bandwidth allocation. The results showed that the better performance in terms of the sum of the values and the amount of lost bytes.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.17 no.6
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• pp.211-223
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• 2018

Mobile relay systems have been adopted by $4^{th}$ generation mobile systems as an alternative method to extend cell coverage as well as to enhance the system throughput at cell-edges. In order to achieve such performance gains, the mobile relay systems require path selection and resource allocation schemes that are specifically designed for these systems which make use of additional radio resources not needed in single-hop systems. This paper proposes an integrated path selection and resource allocation scheme for LTE-Advanced relay systems using collaborative communication. We first define the problem of maximizing the downlink throughput of LTE-Advanced relay systems using collaborative communication and transform it into a multi-dimensional multi-choice backpacking problem. The proposed Lagrange multiplier-based heuristic algorithm is then applied to derive the approximate solution to the maximization problem. It is shown through simulations that the approximate solution obtained by the proposed scheme can achieve a near-optimal performance.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.17 no.3
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• pp.206-212
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• 1992

In this paper, a public key cryptosystem for security in computer communication networks is proposed. This is based on the security to a difficulty of factorization. For the proposed public key polynomials and the random intergers, then the ciphertext is computed. The security of proposed public key knapsack cryptosystem is verified with digital simulation.

• Journal of the Korean Operations Research and Management Science Society
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• v.34 no.3
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• pp.125-136
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• 2009

The container packing problem Is one of the traditional optimization problems, which is very related to the knapsack problem and the bin packing problem. In this paper, we deal with the quadratic multiple container picking problem (QMCPP) and it Is known as a NP-hard problem. Thus, It seems to be natural to use a heuristic approach such as evolutionary algorithms for solving the QMCPP. Until now, only a few researchers have studied on this problem and some evolutionary algorithms have been proposed. This paper introduces a new efficient evolutionary algorithm for the QMCPP. The proposed algorithm is devised by improving the original network random key method, which is employed as an encoding method in evolutionary algorithms. And we also propose local search algorithms and incorporate them with the proposed evolutionary algorithm. Finally we compare the proposed algorithm with the previous algorithms and show the proposed algorithm finds the new best results in most of the benchmark instances.