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Journal of System Simulation

Abstract

Abstract: To address the challenge that traditional solving methods cannot meet real-time requirements in the intelligent allocation and reconstruction of air defense operation resources, this paper focuses on an intelligent solving method for the dynamic allocation and reconstruction of intercept timing sensor-weapon-target. A multi-constraint model is established with damage effectiveness and operation cost as optimization objectives, and a knowledge-guided genetic optimization method (KGGOM) is proposed. Through knowledge-guided population generation and evolution, constraint-aware operators with correction mechanisms, and multi-path search combined with simulated annealing for gene correction, it improves its optimization capability and speed. Historical superior solution retention and dynamic elite retention mechanisms are introduced to achieve rapid reconstruction of operation resources. Simulation results demonstrate that, compared with traditional genetic algorithm (GA) and particle swarm optimization (PSO), KGGOM improves optimization performance by 4%-5% in resource allocation and by 14% in reconstruction, reduces the average convergence time by about 70%-80% during resource allocation and reconstruction, and has solutions with better stability. This study provides an efficient solving method for the dynamic allocation and reconstruction of air defense resources.

First Page

3028

Last Page

3051

CLC

TP391.9

Recommended Citation

Chen Jiaoxiong, Zhang Chenglong, Zhao Yuqiang, et al. Air Defense Resource Allocation and Reconstruction Method Based on KGGOM[J]. Journal of System Simulation, 2026, 38(10): 3028-3051.

Corresponding Author

Li Xianglin

DOI

10.16182/j.issn1004731x.joss.26-0479

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