• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.5
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• pp.2162-2176
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• 2018

Chemical reactions have an intricate relationship with the search for better-quality neighborhood solutions to optimization problems. A catalytic reaction for chemical reactions provides a clue and a framework to solve complicated optimization problems. The application of a catalytic reaction reveals new information hidden in the optimization problem and provides a non-intuitive perspective. This paper proposes a new simulated catalytic reaction method for search in optimization problems. In the experiments using this method, significantly improved results are obtained in almost all graphs tested by applying to a graph bisection problem, which is a representative problem of combinatorial optimization problems.

• The Journal of the Korea institute of electronic communication sciences
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• v.5 no.5
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• pp.444-448
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• 2010

RCR network[1] is a topology for interconnection networks having many desirable properties for building scalable parallel machines. This had been analyzed by Hu and Cao[2] to deal with problems of disconnected graph, bisection width and diameter. We analyze some properties of RCR again and revise the condition for connected graph and network diameter. And we present an efficient algorithm for finding next node on a shortest path.

• Journal of KIISE:Computer Systems and Theory
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• v.35 no.6
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• pp.263-272
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• 2008

We propose and analyze a new interconnection network, called petersen-torus(PT) network based on well-known petersen graph. PT network has a smaller diameter and a smaller network cost than honeycomb torus with same number of nodes. In this paper, we propose optimal routing algorithm and hamiltonian cycle algorithm. We derive diameter, network cost and bisection width.

• Journal of IKEEE
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• v.23 no.4
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• pp.1430-1439
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• 2019

In Functional Mockup Interface(FMI)-based co-simulation, a bisectional algorithm can be used to find the zerocrossing point as a way to improve the accuracy of the simulation results. In this paper, the proposed master algorithm(MA) analyzes the repeated interval graph and predicts the next interval by applying the Markov Chain to the step size. In the simulation, we propose an algorithm to minimize the rollback by storing the step size that changes according to the graph type as an array and applying it to the next prediction interval when the rollback occurs in the simulation. Simulation results show that the proposed algorithm reduces the simulation time by more than 20% compared to the existing algorithm.