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Multi-sensor and Multi-target Task Allocation Method based on Improved Firefly Algorithm

机译:基于改进萤火虫算法的多传感器和多目标任务分配方法

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Sensor task allocation plays a great role in military, environmental science, medical health, transportation and other fields. In order to make rational use of limited sensor resources, a multi-sensor multi-target task allocation method based on an improved firefly algorithm (FA) is proposed. In the algorithm, the initial position of firefly individual in firefly algorithm is optimized to speed up the search optimization procedure. In the process of constructing efficiency function, position constraints, sensor monitoring ability constraints and target threat degree constraints are considered comprehensively, leading to a more realistic multi-sensor multi-target task allocation algorithm. The analytic hierarchy process (AHP) is used to construct the target threat measure. The simulation results show that the proposed algorithm is more efficient than the standard particle swarm optimization algorithm (PSO) and the standard FA, that is, the sensor task allocation is more reasonable, and the task allocation time cost is also shorter than the other two algorithms.
机译:传感器任务分配在军事,环境科学,医疗健康,运输和其他领域发挥着重要作用。为了理性地利用有限的传感器资源,提出了一种基于改进的Firefly算法(FA)的多传感器多目标任务分配方法。在算法中,优化了萤火虫算法中Firefly个人的初始位置,以加快搜索优化过程。在构建效率函数的过程中,全面地考虑了效率约束,传感器监测能力约束和目标威胁程度约束,导致更现实的多传感器多目标任务分配算法。分析层次处理(AHP)用于构建目标威胁度量。仿真结果表明,该算法比标准粒子群优化算法(PSO)和标准FA更有效,即传感器任务分配更合理,任务分配时间成本也比其他两个更短算法。

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