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<p>In the face of the complex battlefield environment and various attack modes, it is particularly important to make the warship system efficiently recover in the shortest time after attacks. This paper simulated the combat situation of naval warfare and allocated different importance to equipment under different attack. Based on the warship defense system models orient to different attacks, the ADRS (attack defense recovery strategy) was firstly proposed. ADRS can greatly reduce the damage in the whole process of system under multiple attack waves. Secondly, we proposed the IACO (improved ant colony optimization) by using non-uniform initial pheromone and classification of starting points within the group to significantly improve the solution and convergence speed. Then we proposed an IGA (improved genetic algorithm) by using two-way matching rule which can optimize the quality of the maintenance path. Finally, we hybrid the two algorithms into HIACOGA (hybrid IACO and IGA) based on ADRS. Simulation results show that, across torpedo, aircraft/missile, and UAV scenarios, ADRS-HIACOGA reduces convergence time by 15.0–58.3% versus ADRS-IGA and improves solution quality by 0.64–4.21% (vs. ADRS-IGA) and 2.05–8.78% (vs. ADRS-IACO). It further improves solution quality over GRS-HIACOGA by 9.16–26.18%. Simulations across attack types show ADRS-HIACOGA optimizes faster and better, markedly boosting system resilience. Gains persist under fewer iterations and tighter time limits, with the largest improvements under torpedo attacks, the most destructive case. Thus, ADRS-HIACOGA excels at recovery and meets real-time demands in multi-wave maritime combat under severe conditions.</p>

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Research on warship system modeling and recovery based on hybrid improved ant colony optimization and genetic algorithm

  • 期刊论文

  • 发表日期：

    2026年1月8日

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  • Zhen Li,
  • LuHong Wang

摘要

In the face of the complex battlefield environment and various attack modes, it is particularly important to make the warship system efficiently recover in the shortest time after attacks. This paper simulated the combat situation of naval warfare and allocated different importance to equipment under different attack. Based on the warship defense system models orient to different attacks, the ADRS (attack defense recovery strategy) was firstly proposed. ADRS can greatly reduce the damage in the whole process of system under multiple attack waves. Secondly, we proposed the IACO (improved ant colony optimization) by using non-uniform initial pheromone and classification of starting points within the group to significantly improve the solution and convergence speed. Then we proposed an IGA (improved genetic algorithm) by using two-way matching rule which can optimize the quality of the maintenance path. Finally, we hybrid the two algorithms into HIACOGA (hybrid IACO and IGA) based on ADRS. Simulation results show that, across torpedo, aircraft/missile, and UAV scenarios, ADRS-HIACOGA reduces convergence time by 15.0–58.3% versus ADRS-IGA and improves solution quality by 0.64–4.21% (vs. ADRS-IGA) and 2.05–8.78% (vs. ADRS-IACO). It further improves solution quality over GRS-HIACOGA by 9.16–26.18%. Simulations across attack types show ADRS-HIACOGA optimizes faster and better, markedly boosting system resilience. Gains persist under fewer iterations and tighter time limits, with the largest improvements under torpedo attacks, the most destructive case. Thus, ADRS-HIACOGA excels at recovery and meets real-time demands in multi-wave maritime combat under severe conditions.