Solving multi-objective weapon-target assignment considering reliability by improved MOEA/D-AM2M

The weapon-target assignment problem is a challenging optimization issue, but reliability is seldom considered in the majority of existing literature. To address the high-reliability weapon-target assignment problem, this paper integrates weapon reliability and mission reliability into a multi-objec...

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Bibliographic Details
Published in:	Neurocomputing (Amsterdam) Vol. 563; p. 126906
Main Authors:	Yi, Xiaojian, Yu, Huiyang, Xu, Tao
Format:	Journal Article
Language:	English
Published:	Elsevier B.V 01.01.2024
Subjects:	Multi-objective evolutionary algorithm Multi-objective optimization Reliability Weapon-target assignment Multi-objective optimization Weapon-target assignment Reliability Multi-objective evolutionary algorithm
ISSN:	0925-2312
Online Access:	Get full text
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Summary:	The weapon-target assignment problem is a challenging optimization issue, but reliability is seldom considered in the majority of existing literature. To address the high-reliability weapon-target assignment problem, this paper integrates weapon reliability and mission reliability into a multi-objective optimization model (MOD) and presents an improved algorithm termed MOEA/D-iAM2M to the problem. This algorithm effectively combines the strengths of adaptive search space decomposition-based MOEA (MOEA/D-AM2M) and two-stage hybrid learning-based MOEA (HLMEA), resulting in a faster convergence rate and a more extensive distribution of the Pareto solution set. Furthermore, a reference point is incorporated into MOEA/D-iAM2M to facilitate the adaptive weight adjustment. Numerical experiments are carried out to confirm the effectiveness of the proposed MOEA/D-iAM2M. This research is significant in the field of optimization and has practical value in the defense industry.
ISSN:	0925-2312
DOI:	10.1016/j.neucom.2023.126906