Mission-oriented Combat Effectiveness Evaluation Model for Intelligent Equipment System-of-systems

doi:10.16182/j.issn1004731x.joss.26-0128

Abstract

Abstract:

A hierarchical evolutionary adaptive learning (HEAL) framework is proposed to address the problems in the operational simulation of intelligent equipment systems under complex adversarial environments, including fragmented hierarchical decision-making, unclear mapping between algorithmic indicators and operational effectiveness, and the lack of adaptive evolution capability of simulation models. A multi-layer learning architecture that integrates robust autonomous decision-making for single units, emergent collaboration of system networks, and game-theoretic evolution of system-of-systems strategies is constructed by following the “micro-meso-macro” system-oriented hierarchical modeling paradigm. A cross-layer closed-loop coupling among learned policies, cooperative structures, and operational effectiveness is achieved by combining bottom-up behavioral abstraction with top-down rule shaping, thereby establishing an interpretable mapping relationship from algorithmic indicators to operational effectiveness indicators. Simulation results demonstrate that compared with single-layer or weakly coupled learning methods, the HEAL framework significantly improves the mission success rate and collaboration consistency, while effectively mitigating effectiveness degradation under complex disturbances. A unified and verifiable methodological framework is provided for hierarchical modeling, collaborative learning, and simulation-based evaluation of operational effectiveness for intelligent equipment systems.

Key words: intelligent equipment system, hierarchical evolutionary adaptive learning, system simulation, stochastic game, operational effectiveness evaluation, emergent collaboration

CLC Number:

TP18

Zhang Long, Huang Wenbo, Feng Xuanming, Lei Zhen, Wang Ying. Mission-oriented Combat Effectiveness Evaluation Model for Intelligent Equipment System-of-systems[J]. Journal of System Simulation, 2026, 38(10): 2819-2839.

Figures/Tables 12

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