scholarly journals Simulation-Based Optimization on the System-of-Systems Model via Model Transformation and Genetic Algorithm: A Case Study of Network-Centric Warfare

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-15 ◽  
Author(s):  
Bong Gu Kang ◽  
Seon Han Choi ◽  
Se Jung Kwon ◽  
Jun Hee Lee ◽  
Tag Gon Kim
Keyword(s):  
Genetic Algorithm ◽  
Execution Time ◽  
Integrated Model ◽  
System Of Systems ◽  
Experimental Result ◽  
Model Parameters ◽  
Sos Model ◽  
Systems Model ◽  
Simulation Based ◽  
Simulation Based Optimization

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.

Simulation-Based Scheduling of Waterway Projects Using a Parallel Genetic Algorithm

2015 ◽  
pp. 334-347 ◽  
Author(s):  
Ning Yang ◽  
Shiaaulir Wang ◽  
Paul Schonfeld
Keyword(s):  
Genetic Algorithm ◽  
Parallel Computing ◽  
Message Passing ◽  
Message Passing Interface ◽  
Computation Time ◽  
Parallel Genetic Algorithm ◽  
Simulation Based ◽  
Multiple Processors ◽  
Simulation Based Optimization ◽  
Speed Up

A Parallel Genetic Algorithm (PGA) is used for a simulation-based optimization of waterway project schedules. This PGA is designed to distribute a Genetic Algorithm application over multiple processors in order to speed up the solution search procedure for a very large combinational problem. The proposed PGA is based on a global parallel model, which is also called a master-slave model. A Message-Passing Interface (MPI) is used in developing the parallel computing program. A case study is presented, whose results show how the adaption of a simulation-based optimization algorithm to parallel computing can greatly reduce computation time. Additional techniques which are found to further improve the PGA performance include: (1) choosing an appropriate task distribution method, (2) distributing simulation replications instead of different solutions, (3) avoiding the simulation of duplicate solutions, (4) avoiding running multiple simulations simultaneously in shared-memory processors, and (5) avoiding using multiple processors which belong to different clusters (physical sub-networks).

scholarly journals Simulation-based Optimization of Multi-Car Elevator Controllers Using a Genetic Algorithm

2004 ◽  
Vol 40 (4) ◽  
pp. 466-473 ◽  
Author(s):  
Hiromichi SUZUKI ◽  
Satoshi TAKAHASHI ◽  
Yasuhito SANO ◽  
Takeshi SUDO ◽  
Sandor MARKON ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Simulation Based ◽  
Simulation Based Optimization

scholarly journals Simulation-Based Scheduling of Waterway Projects Using a Parallel Genetic Algorithm

2016 ◽  
pp. 1071-1084
Author(s):  
Ning Yang ◽  
Shiaaulir Wang ◽  
Paul Schonfeld
Keyword(s):  
Genetic Algorithm ◽  
Parallel Computing ◽  
Message Passing ◽  
Message Passing Interface ◽  
Computation Time ◽  
Parallel Genetic Algorithm ◽  
Simulation Based ◽  
Multiple Processors ◽  
Simulation Based Optimization ◽  
Speed Up

A Parallel Genetic Algorithm (PGA) is used for a simulation-based optimization of waterway project schedules. This PGA is designed to distribute a Genetic Algorithm application over multiple processors in order to speed up the solution search procedure for a very large combinational problem. The proposed PGA is based on a global parallel model, which is also called a master-slave model. A Message-Passing Interface (MPI) is used in developing the parallel computing program. A case study is presented, whose results show how the adaption of a simulation-based optimization algorithm to parallel computing can greatly reduce computation time. Additional techniques which are found to further improve the PGA performance include: (1) choosing an appropriate task distribution method, (2) distributing simulation replications instead of different solutions, (3) avoiding the simulation of duplicate solutions, (4) avoiding running multiple simulations simultaneously in shared-memory processors, and (5) avoiding using multiple processors which belong to different clusters (physical sub-networks).

Simulation-Based Scheduling of Waterway Projects Using a Parallel Genetic Algorithm

2015 ◽  
Vol 6 (1) ◽  
pp. 49-63 ◽  
Author(s):  
Ning Yang ◽  
Shiaaulir Wang ◽  
Paul Schonfeld
Keyword(s):  
Genetic Algorithm ◽  
Parallel Computing ◽  
Message Passing ◽  
Message Passing Interface ◽  
Computation Time ◽  
Parallel Genetic Algorithm ◽  
Distribution Method ◽  
Simulation Based ◽  
Multiple Processors ◽  
Simulation Based Optimization

A Parallel Genetic Algorithm (PGA) is used for a simulation-based optimization of waterway project schedules. This PGA is designed to distribute a Genetic Algorithm application over multiple processors in order to speed up the solution search procedure for a very large combinational problem. The proposed PGA is based on a global parallel model, which is also called a master-slave model. A Message-Passing Interface (MPI) is used in developing the parallel computing program. A case study is presented, whose results show how the adaption of a simulation-based optimization algorithm to parallel computing can greatly reduce computation time. Additional techniques which are found to further improve the PGA performance include: (1) choosing an appropriate task distribution method, (2) distributing simulation replications instead of different solutions, (3) avoiding the simulation of duplicate solutions, (4) avoiding running multiple simulations simultaneously in shared-memory processors, and (5) avoiding using multiple processors which belong to different clusters (physical sub-networks).

scholarly journals Communication Analysis of Network-Centric Warfare via Transformation of System of Systems Model into Integrated System Model Using Neural Network

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-16 ◽  
Author(s):  
Bong Gu Kang ◽  
Kyung-Min Seo ◽  
Tag Gon Kim
Keyword(s):  
Execution Time ◽  
Discrete Event ◽  
System Of Systems ◽  
Integrated System ◽  
Network System ◽  
System Specification ◽  
Discrete Events ◽  
Communication Analysis ◽  
Systems Model ◽  
Devs Formalism

Communication system in the network-centric warfare (NCW) has been analyzed from the perspective of the system of systems (SoS), which consists of a combat system and a network system so that the two reflect each other’s effects. However, this paradoxically causes a prolonged execution time. To solve this problem, this paper proposes an advanced integrated modeling method for the communication analysis in the NCW via the transformation of the SoS, which reduces the simulation execution time while ensuring the accuracy of the communication effects. The proposed models mainly cover interentity traffic and intraentity mobility developed in the form of feed-forward neural networks to guarantee two-way interactions between the combat system and the network system. Because they are characterized as discrete events, the proposed models are designed with the discrete-event system specification (DEVS) formalism. The experimental results show that the proposed transformation reduced an error by 6.40% compared to the existing method and reduced the execution time 3.78-fold compared to the SoS-based NCW simulation.

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