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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Main Authors: Chunqing Gao, Yingxin Kou, You Li, Zhanwu Li, An Xu
Format: Article
Language:English
Published: IEEE 2019-01-01
Series:IEEE Access
Subjects:
Online Access:https://ieeexplore.ieee.org/document/8685080/
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spelling 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/
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