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A Survey of Intelligent Optimization Algorithms for Weapon Target Assignment (WTA) Problem

机译:武器目标分配智能优化算法调查(WTA)问题

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摘要

In the research on firepower strike optimization of intelligent battlefield multi-weapon platform, in order to improve the overall strike effectiveness of weapons, it is necessary to establish a weapon target allocation model to obtain real-time and accurate strike plans, and obtain strike-related strike parameters through data model comparison. To this end, this article first introduces the concept, classification and basic mathematical model of the WTA problem, and sorts out the progress of the weapon-target assignment (WTA) problem at home and abroad. For the analysis of the WTA problem, you can use the genetic algorithm "survival of the fittest, survival of the fittest" mechanism, the particle swarm algorithm iterative process is simple and fast, and the ant colony algorithm has clear goals and strong scalability, etc. to obtain a precise strike plan to solve the problem of low real-time firepower strikes, The problem of low accuracy, finally pointed out the lack of horizontal comparison between the current WTA problem and the intelligent optimization algorithm research, and clarified the next development direction from the three perspectives of the feasibility, effectiveness and scalability of the WTA problem.
机译:在智能战场多武器平台的火力罢工优化研究中,为了提高武器的总体罢工效果,有必要建立武器目标分配模型,以获得实时和准确的罢工计划,并获得罢工 - 相关罢工参数通过数据模型比较。为此,本文首先介绍了WTA问题的概念,分类和基本数学模型,并整理了国内外武器 - 目标分配(WTA)问题的进展。对于对WTA问题的分析,您可以使用遗传算法“Fittest的存活率,适用于最适合的”机制,粒子群算法迭代过程简单快速,蚁群算法具有明显的目标和强大的可扩展性,为获得精确的罢工计划来解决低实时火力罢工的问题,精度低的问题,终于指出了当前WTA问题与智能优化算法研究之间的水平比较,并阐明了下一个从WTA问题的可行性,有效性和可扩展性的三个观点的发展方向。

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