TY - GEN
T1 - Multi-target firepower allocation method based on improved genetic algorithm
AU - Yuan, Yuan
AU - Zhang, An
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/11/20
Y1 - 2015/11/20
N2 - Aiming at the firepower allocation problem of strategic bomber in its multi-target attack, the optimal firepower allocation model is established based on the principle to obtain the maximum damage value of targets. Then the genetic algorithm (GA) is improved. A new genetic encoding method is proposed, by which the constraints of the firepower allocation problem can be included in each individual. Meanwhile the corresponding genetic operators of selection, crossover and mutation are designed and individuals will still meet the constraints after these genetic operations. Finally the inversion operator is introduced to enhance the local search ability of GA. Simulation results indicate that the proposed algorithm is a simple, accurate, effective and fast algorithm. Compared with simple GA (SGA) based on binary code and GA based on general decimal code (DGA), the solution quality and solving efficiency of the improved GA (IGA) are greatly increased.
AB - Aiming at the firepower allocation problem of strategic bomber in its multi-target attack, the optimal firepower allocation model is established based on the principle to obtain the maximum damage value of targets. Then the genetic algorithm (GA) is improved. A new genetic encoding method is proposed, by which the constraints of the firepower allocation problem can be included in each individual. Meanwhile the corresponding genetic operators of selection, crossover and mutation are designed and individuals will still meet the constraints after these genetic operations. Finally the inversion operator is introduced to enhance the local search ability of GA. Simulation results indicate that the proposed algorithm is a simple, accurate, effective and fast algorithm. Compared with simple GA (SGA) based on binary code and GA based on general decimal code (DGA), the solution quality and solving efficiency of the improved GA (IGA) are greatly increased.
KW - Encoding method
KW - Improved genetic algorithm
KW - Inversion operator
KW - Multi-target firepower allocation
UR - https://www.scopus.com/pages/publications/84954491253
U2 - 10.1109/IHMSC.2015.89
DO - 10.1109/IHMSC.2015.89
M3 - 会议稿件
AN - SCOPUS:84954491253
T3 - Proceedings - 2015 7th International Conference on Intelligent Human-Machine Systems and Cybernetics, IHMSC 2015
SP - 531
EP - 534
BT - Proceedings - 2015 7th International Conference on Intelligent Human-Machine Systems and Cybernetics, IHMSC 2015
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 7th International Conference on Intelligent Human-Machine Systems and Cybernetics, IHMSC 2015
Y2 - 26 August 2015 through 27 August 2015
ER -