Meta-Component Selection Method of Mission Software Based on NSGA-II

For UAV swarm–based disaster response scenarios involving collaborative area search, identification of high-priority targets, and target information transmission, existing foundational components can partially reuse capabilities from emergency command-and-control (C2) software. However, software capability requirements in dynamic environments are difficult to enumerate exhaustively in advance, while the provisioning and updating of reusable components remain insufficiently efficient. Consequently, current approaches struggle to satisfy the stringent real-time requirements for agile, on-demand software generation at the frontline. On this basis, this paper proposes a multi-objective meta-component selection method of UAV swarms based on Non-dominated Sorting Genetic Algorithm II (NSGA-II). A meta-component description model covering functional coverage, interface constraints, dependency and mutual exclusion relationships, runtime, memory footprint, energy overhead, reliability and security level is constructed. The meta-component selection problem is transformed into a constrained multi-objective combinatorial optimization problem. Fast non-dominated sorting, crowding distance maintenance and constraint repair strategies are adopted to solve the Pareto-optimal component set, and a multi-index comprehensive evaluation method is utilized to select a compromise scheme from the Pareto set. Simulation experiments are carried out with eight types of tactical software functions and 32 candidate meta-components under fixed random seeds and reproducible experimental parameters. The results show that the Monte Carlo estimation yields an HV ratio of 0.998 and an ϵ-coverage of 1.000; the TOPSIS compromise solution requires 306.3 ms of runtime, 601.9 MB of memory, and 6.511 J·min−1 of energy consumption.