Simulation of a system-of-systems (SoS) model, which consists of a combat model and a network model, has been used to analyze the performance of network-centric warfare in detail. However, finding the combat model parameters satisfying the required combat power using simulation can take a long time for two reasons: (1) the prolonged execution time per simulation run and (2) the enormous number of simulation runs. This paper proposes a simulation-based optimization method for the SoS-based simulation model to overcome these problems. The method consists of two processes: (1) the transformation of the SoS-based model into an integrated model using the neural network to reduce the execution time and (2) the optimization of the integrated model using the genetic algorithm with ranking and selection to decrease the number of simulation runs. The experimental result reveals that the proposed method significantly reduced the time for finding the optimal combat parameters with an acceptable level of accuracy.
Bibliographical noteFunding Information:
This work was supported by the Brain Korea 21 Plus program through the National Research Foundation (NRF) of Korea funded by the Ministry of Education. The authors would like to thank the Editor, Prof. László T. Kóczy, and anonymous reviewers for their valuable comments and constructive suggestions.
Copyright © 2018 Bong Gu Kang et al.