Multi-Objective Weapon Target Assignment Based on D-NSGA-III-A
The multi-objective weapon-target assignment problem, which aims to generate reasonable assignment to meet the objectives, is a typical optimization problem with complex constraints. In order to get close to the actual air combat, the game process between both sides at war is introduced to construct...
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doaj-2c10ec8a950840259f6041b0e3a83fb12021-03-29T22:15:41ZengIEEEIEEE Access2169-35362019-01-017502405025410.1109/ACCESS.2019.29102418685080Multi-Objective Weapon Target Assignment Based on D-NSGA-III-AChunqing Gao0https://orcid.org/0000-0001-8205-4501Yingxin Kou1You Li2Zhanwu Li3An Xu4Aeronautics Engineering College, Air Force Engineering University, Xi’an, ChinaAeronautics Engineering College, Air Force Engineering University, Xi’an, ChinaAeronautics Engineering College, Air Force Engineering University, Xi’an, ChinaAeronautics Engineering College, Air Force Engineering University, Xi’an, ChinaAeronautics Engineering College, Air Force Engineering University, Xi’an, ChinaThe multi-objective weapon-target assignment problem, which aims to generate reasonable assignment to meet the objectives, is a typical optimization problem with complex constraints. In order to get close to the actual air combat, the game process between both sides at war is introduced to construct a three objective mathematical model, which includes the damage of the enemy, the cost of missiles, and the damage value of fighting capacity. Considering the NP-complete nature of multi-objective weapon-target assignment problem, an improved intelligent algorithm (named as D-NSGA-III-A) on the basis of non-dominated sorting genetic algorithm III (NSGA-III) is proposed. In this improved algorithm, first, the non-dominated sorting based on dominance degree matrix is proposed to reduce the unnecessary or repetitive comparisons in ranking schemes, so as to further decrease the time consumption. Second, diversity and convergence are taken into account resorting to the niching information and the dominance ratio when selecting individuals. Third, the adaptive operator selection mechanism, which selects the operators adaptively according to the information of generations from a pool where single point crossover and all bits crossover operators are included, is employed to seek a balance between intensification and diversification within the decision space and to improve the quality of Pareto solutions. From the experiments, the combination of above technologies obtains better Pareto solutions and time performance for solving the static multi-objective target assignment (SMWTA) problem than NSGA-III, MP-ACO, NSGA-II, MOPSO, MOEA/D, and DMOEA-εC.https://ieeexplore.ieee.org/document/8685080/Adaptive operator selection mechanismdominant degree matrixmulti-objective optimizationnon-dominated sorting genetic algorithm IIIweapon target assignment |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Chunqing Gao Yingxin Kou You Li Zhanwu Li An Xu |
spellingShingle |
Chunqing Gao Yingxin Kou You Li Zhanwu Li An Xu Multi-Objective Weapon Target Assignment Based on D-NSGA-III-A IEEE Access Adaptive operator selection mechanism dominant degree matrix multi-objective optimization non-dominated sorting genetic algorithm III weapon target assignment |
author_facet |
Chunqing Gao Yingxin Kou You Li Zhanwu Li An Xu |
author_sort |
Chunqing Gao |
title |
Multi-Objective Weapon Target Assignment Based on D-NSGA-III-A |
title_short |
Multi-Objective Weapon Target Assignment Based on D-NSGA-III-A |
title_full |
Multi-Objective Weapon Target Assignment Based on D-NSGA-III-A |
title_fullStr |
Multi-Objective Weapon Target Assignment Based on D-NSGA-III-A |
title_full_unstemmed |
Multi-Objective Weapon Target Assignment Based on D-NSGA-III-A |
title_sort |
multi-objective weapon target assignment based on d-nsga-iii-a |
publisher |
IEEE |
series |
IEEE Access |
issn |
2169-3536 |
publishDate |
2019-01-01 |
description |
The multi-objective weapon-target assignment problem, which aims to generate reasonable assignment to meet the objectives, is a typical optimization problem with complex constraints. In order to get close to the actual air combat, the game process between both sides at war is introduced to construct a three objective mathematical model, which includes the damage of the enemy, the cost of missiles, and the damage value of fighting capacity. Considering the NP-complete nature of multi-objective weapon-target assignment problem, an improved intelligent algorithm (named as D-NSGA-III-A) on the basis of non-dominated sorting genetic algorithm III (NSGA-III) is proposed. In this improved algorithm, first, the non-dominated sorting based on dominance degree matrix is proposed to reduce the unnecessary or repetitive comparisons in ranking schemes, so as to further decrease the time consumption. Second, diversity and convergence are taken into account resorting to the niching information and the dominance ratio when selecting individuals. Third, the adaptive operator selection mechanism, which selects the operators adaptively according to the information of generations from a pool where single point crossover and all bits crossover operators are included, is employed to seek a balance between intensification and diversification within the decision space and to improve the quality of Pareto solutions. From the experiments, the combination of above technologies obtains better Pareto solutions and time performance for solving the static multi-objective target assignment (SMWTA) problem than NSGA-III, MP-ACO, NSGA-II, MOPSO, MOEA/D, and DMOEA-εC. |
topic |
Adaptive operator selection mechanism dominant degree matrix multi-objective optimization non-dominated sorting genetic algorithm III weapon target assignment |
url |
https://ieeexplore.ieee.org/document/8685080/ |
work_keys_str_mv |
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