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首页> 外文期刊>Automatica Sinica, IEEE/CAA Journal of >A predator-prey particle swarm optimization approach to multiple UCAV air combat modeled by dynamic game theory
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A predator-prey particle swarm optimization approach to multiple UCAV air combat modeled by dynamic game theory

机译：基于动态博弈模型的多种UCAV空战的食肉动物—猎物粒子群优化方法

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摘要

Dynamic game theory has received considerable attention as a promising technique for formulating control actions for agents in an extended complex enterprise that involves an adversary. At each decision making step, each side seeks the best scheme with the purpose of maximizing its own objective function. In this paper, a game theoretic approach based on predatorprey particle swarm optimization (PP-PSO) is presented, and the dynamic task assignment problem for multiple unmanned combat aerial vehicles (UCAVs) in military operation is decomposed and modeled as a two-player game at each decision stage. The optimal assignment scheme of each stage is regarded as a mixed Nash equilibrium, which can be solved by using the PP-PSO. The effectiveness of our proposed methodology is verified by a typical example of an air military operation that involves two opposing forces: the attacking force Red and the defense force Blue.
机译：动态博弈理论作为一种有前途的技术，已经成为备受争议的扩展复杂企业中的代理制定控制行为的一种有前途的技术。在每个决策步骤，双方都寻求最佳方案，以最大化其自身的目标功能。本文提出了一种基于捕食者粒子群优化（PP-PSO）的博弈论方法，将军事作战中的多种无人机作战任务的动态任务分配问题分解并建模为两人博弈。在每个决策阶段。每个阶段的最佳分配方案被视为混合Nash平衡，可以通过使用PP-PSO解决。我们提出的方法的有效性通过一个涉及两个敌对力量的空中军事行动的典型例子来验证：攻击力量Red和防御力量Blue。

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