A Modified MOEA/D Algorithm for Solving Bi-Objective Multi-Stage Weapon-Target Assignment Problem

In command of modern intelligent operations, in addition to solving the problem of multi-unit coordinated task assignment, it is also necessary to obtain a suitable plan according to the needs of decision makers. Based on these requirements, we established a multi-stage bi-objective weapon-target as...

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Vydané v:IEEE access Ročník 9; s. 71832 - 71848
Hlavní autori: Wu, Xiaochen, Chen, Chen, Ding, Shuxin
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Piscataway IEEE 2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Agglomeration
Algorithms
Clustering
decomposition-based multi-objective evolutionary algorithm (MOEA/D)
Discrete wavelet transforms
Evolutionary algorithms
Heuristic algorithms
ideal-nadir Tchebycheff approach
Multi-stage weapon target assignment (MWTA)
Multiple objective analysis
niche
Operations research
Optimization
Resource management
Statistics
Task analysis
Weapons
ISSN:2169-3536, 2169-3536
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Shrnutí:In command of modern intelligent operations, in addition to solving the problem of multi-unit coordinated task assignment, it is also necessary to obtain a suitable plan according to the needs of decision makers. Based on these requirements, we established a multi-stage bi-objective weapon-target assignment model, and designed a new algorithm with niche and region self-adaptive aggregation (named MOEA/ D-NRSA) based on the decomposition-based multi-objective evolutionary algorithm (MOEA/D) to obtain richer solutions that meet the preferences of different decision makers. Compared with MOEA/D, MOEA/ D-NRSA has advantages in improving the convergence and maintaining the distribution of the solution. On the one hand, it contains a population evolution method based on niche technology to obtain better offspring; on the other hand, it has a new neighborhood selection and update strategy. This strategy first clusters the individuals in the objective space to divide into different regions, in which the subproblems can independently select the appropriate aggregation mode according to the clustering density of the region and update its neighborhood. This strategy can improve the uneven distribution of individuals and maintain the diversity and distribution of the population. Numerical experiments selected state-of-the-art algorithms for comparison, which proved the superiority of MOEA/D-NRSA.
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ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2021.3079152