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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Veröffentlicht in:Neurocomputing (Amsterdam) Jg. 563; S. 126906
Hauptverfasser: Yi, Xiaojian, Yu, Huiyang, Xu, Tao
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier B.V 01.01.2024
Schlagworte:
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
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Zusammenfassung: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