• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.7
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• pp.1416-1427
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• 2012

This paper proposes a design technique of a survivable ship backbone network, which describes a near optimal configuration scheme of physical and logical topologies of which the survivable ship backbone network consists. We first analyze and present an efficient architecture of a survivable ship backbone network consisting of redundant links and ship devices with dual communication interfaces. Then, we present an integer linear programming-based configuration scheme of a physical topology with regard to the proposed ship backbone network architecture. Finally, we present a metaheuristic-based configuration scheme of a logical topology, underlying the physical topology.

• Journal of Korean Institute of Industrial Engineers
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• v.33 no.1
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• pp.86-98
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• 2007

This paper presents a heuristic algorithm for the traveling salesman problem with time windows (TSPTW). Aniterative insertion algorithm as a constructive search heuristic and a hybrid meta heuristic combining simulatedannealing and tabu search with the randomized selection of 2-interchange and a simple move operator as animproving search heuristic are proposed, Computational tests performed on 400 benchmark problem instancesshow that the proposed algorithm generates optimal or near-optimal solutions in most cases. New best knownheuristic values for many benchmark problem sets were obtained using the proposed approach.

• Management & Information Systems Review
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• v.34 no.2
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• pp.67-78
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• 2015

In this paper, we consider a scheduling problem of printed circuit board production process with setup cost depending job sequence. Given a set of PCBs, these are produced in single surface mounting device. The problem is to define job sequence with the objective of minimizing the total seutp cost. We propose a mathematical formulation and the problem is proven to be NP-hard. So, a meta heuristic based on genetic algorithm is developed.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2008.10a
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• pp.783-786
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• 2008

외판원문제(Traveling Salesman problem: TSP)는 전형적인 조합최적화 문제로 위치하는 n개의 모든 지점을 오직 한번씩만 방문하는 순회경로를 결정하는 과정에서 순회비용 또는 순회거리를 최소화한다. 따라서 본 논문에서는 종래의 NP-hard문제로 널리 알려진 TSP를 해결하기 위해서 메타 휴리스틱기법 중에서 가장 널리 이용되고 있는 유전 알고리즘(Genetic Algorithm: GA)을 이용한다. 마지막으로, 유전 알고리즘을 이용해 외판원문제에 적합한 성능을 보이는 유전 연산자를 찾아내기 위해 수치 실험을 통해 그 성능에 대한 평가를 한다.

• Journal of Korean Association for Spatial Structures
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• v.21 no.2
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• pp.89-97
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• 2021

This paper describes an adaptive hybrid evolutionary firefly algorithm for a topology optimization of truss structures. The truss topology optimization problems begins with a ground structure which is composed of all possible nodes and members. The optimization process aims to find the optimum layout of the truss members. The hybrid metaheuristics are then used to minimize the objective functions subjected to static or dynamic constraints. Several numerical examples are examined for the validity of the present method. The performance results are compared with those of other metaheuristic algorithms.

• The Journal of the Korea Contents Association
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• v.22 no.9
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• pp.179-193
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• 2022

Amid the explosive growth of various metaverse platforms, there is no unified indicator to measure, analyze, and evaluate based on customer experience. Therefore, the usability evaluation factors in metaverse were identified through a heuristic methodology and literature review, to evaluate the metaverse, a two-to three-dimensional virtual world platform. A measurable system was established by subdividing 20 items in 5 fields, including user control, information structure, design and content, and usage environment, derived through Delphi technique. Based on this, after experiencing the actual contents of major metaverse platforms such as Roblox and Zepeto, usability was evaluated and comparative verification was conducted. As a result, it was estimated that metaverse user experience could be improved as its utility was derived relatively high in terms of user control and content. This study constitutes a theoretical contribution by extending the usability evaluation system, which has been widely used in the field of service design, to the fields of extended reality and mixed reality. At the same time, it has practical key findings of providing basic judgment standards to stakeholders in the metaverse field, as well as policy implications for digital capability enhancement and industry revitalization.

• KIPS Transactions on Software and Data Engineering
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• v.3 no.12
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• pp.523-530
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• 2014

Ontology alignment is the way to share and reuse of ontology knowledge. Because of the ambiguity of concept, most ontology alignment systems combine a set of various measures and complete enumeration to provide the satisfactory result. However, calculating process becomes more complex and required time increases exponentially since the number of concept increases, more errors can appear at the same time. Lately the focus is on meta-matching using the heuristic algorithm. Existing meta-matching system tune extra parameter and it causes complex calculating, as a consequence, the results in the various data of specific domain are not good performed. In this paper, we propose a high performance algorithm by using DCS that can solve ontology alignment through simple process. It provides an efficient search strategy according to distribution of Levy Flight. In order to evaluate the approach, benchmark data from the OAEI 2012 is employed. Through the comparison of the quality of the alignments which uses DCS with state of the art ontology matching systems.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.3
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• pp.185-191
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• 2019

During the modern battlefields of multi-batches flight formation attack situation, it is an essential task for a commander to make a proper fire distribution of air defense missile launch platforms for threat targets with effectively and quickly. Pan et al. try to solve this problem using genetic algorithm, but they are fails. This paper gets the initial feasible solution using high threat target first destroying strategy only use 75% available fire of each missile launch platform. Then, the assigned missile is moving to another target in the case of decreasing total threat. As a result of experiment, while the proposed algorithm is polynomial-time complexity greedy algorithm but this can be improve the solution than genetic algorithm.

• Journal of the Korea Society for Simulation
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• v.28 no.4
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• pp.21-32
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• 2019

Inspired by the social behavior models of a bird flock or fish school, particle swarm optimization (PSO) is a popular metaheuristic optimization algorithm and has been widely used from solving a complex optimization problem to learning a artificial neural network. However, PSO is difficult to apply to many real-life optimization problems involving stochastic noise, since it is originated in a deterministic environment. To resolve this problem, this paper incorporates a resampling method called the uncertainty evaluation (UE) method into PSO. The UE method allows the particles to converge on the accurate optimal solution quickly in a noisy environment by selecting the particles' global best position correctly, one of the significant factors in the performance of PSO. The results of comparative experiments on several benchmark problems demonstrated the improved performance of the propose algorithm compared to the existing studies. In addition, the results of the case study emphasize the necessity of this work. The proposed algorithm is expected to be effectively applied to optimize complex systems through digital twins in the fourth industrial revolution.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.4
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• pp.527-531
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• 2012

In this paper, a novel DPSO-QI (Distributed PSO with quantum-infusion mechanism) algorithm improving one of the fatal defect, the so-called premature convergence, that degrades the performance of the conventional PSO algorithms is proposed. The proposed scheme has the following two distinguished features. First, a concept of neighborhood of each particle is introduced, which divides the whole swarm into several small groups with an appropriate size. Such a strategy restricts the information exchange between particles to be done only in each small group. It thus results in the improvement of particles' diversity and further minimization of a probability of occurring the premature convergence phenomena. Second, a quantum-infusion (QI) mechanism based on the quantum mechanics is introduced to generate a meaningful offspring in each small group. This offspring in our PSO mechanism improves the ability to explore a wider area precisely compared to the conventional one, so that the degree of precision of the algorithm is improved. Finally, some numerical results are compared with those of the conventional researches, which clearly demonstrates the effectiveness and reliability of the proposed DPSO-QI algorithm.