吕遐东, 郑爽, 陈杰. 基于改进遗传算法的舰艇编队联合防空资源预规划方法[J]. 中国舰船研究, 2023, 18(3): 266–276. doi: 10.19693/j.issn.1673-3185.02877
引用本文: 吕遐东, 郑爽, 陈杰. 基于改进遗传算法的舰艇编队联合防空资源预规划方法[J]. 中国舰船研究, 2023, 18(3): 266–276. doi: 10.19693/j.issn.1673-3185.02877
LYU X D, ZHENG S, CHEN J. Pre-planning method of joint air defense resources for ship formations based on improved genetic algorithm[J]. Chinese Journal of Ship Research, 2023, 18(3): 266–276. doi: 10.19693/j.issn.1673-3185.02877
Citation: LYU X D, ZHENG S, CHEN J. Pre-planning method of joint air defense resources for ship formations based on improved genetic algorithm[J]. Chinese Journal of Ship Research, 2023, 18(3): 266–276. doi: 10.19693/j.issn.1673-3185.02877

基于改进遗传算法的舰艇编队联合防空资源预规划方法

Pre-planning method of joint air defense resources for ship formations based on improved genetic algorithm

  • 摘要:
      目的  作战资源规划领域逐渐成为智能化决策指导未来作战的核心点,保证更优的作战资源规划方案对实际作战的指导及执行至关重要,为此,提出一种基于改进遗传算法的舰艇编队协同防空资源规划方法。
      方法  首先,将资源服务化,提高资源的通用性;然后,利用遗传算法多种群的衔接来表示多阶段的作战规划,并设计基于多种群遗传算法的多维服务质量(QoS)作战性能指标,从而为联合作战规划方案的评价建立一套优劣评判手段。
      结果  结果显示,所提方法经资源的通用化处理之后,可以很好地与多种群遗传算法相结合,从而得到一个多阶段最优的作战资源规划方案。
      结论  所做研究对作战资源规划的设计与应用具有一定的参考价值。

     

    Abstract:
      Objectives  The field of combat resource planning has gradually become the core point of intelligent decision guidance for future combat, and ensuring a better combat resource planning scheme is crucial to the guidance and implementation of actual combat. To this end, a resource planning method for the joint air defense of warship formations based on an improved genetic algorithm is proposed.
      Methods  First, the resources are serviced to improve their versatility; next, the articulation of multi-population genetic algorithms is used to represent multi-stage combat planning, and multi-dimensional quality of service (QoS) combat performance indicators are designed on the basis of multi-population genetic algorithms, thereby establishing a set of strengths and weaknesses for the evaluation of joint warfare planning programs.
      Results  After the generalization of resources, the proposed method can be effectively combined with multi-population genetic algorithms to obtain a multi-stage optimal combat resource planning scheme.
      Conclusion  This study has certain reference value for the design and application of combat resource planning.

     

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