National Digital Switching System Engineering and Technological Research Center (NDSC), Zhengzhou 450000, China.
Sensors (Basel). 2018 Jun 29;18(7):2093. doi: 10.3390/s18072093.
With the development of science and technology, modern communication scenarios have put forward higher requirements for passive location technology. However, current location systems still use manual scheduling methods and cannot meet the current mission-intensive and widely-distributed scenarios, resulting in inefficient task completion. To address this issue, this paper proposes a method called multi-objective, multi-constraint and improved genetic algorithm-based scheduling (MMIGAS), contributing a centralized combinatorial optimization model with multiple objectives and multiple constraints and conceiving an improved genetic algorithm. First, we establish a basic mathematical framework based on the structure of a passive location system. Furthermore, to balance performance with respect to multiple measures and avoid low efficiency, we propose a multi-objective optimal function including location accuracy, completion rate and resource utilization. Moreover, to enhance its practicability, we formulate multiple constraints for frequency, resource capability and task cooperation. For model solving, we propose an improved genetic algorithm with better convergence speed and global optimization ability, by introducing constraint-proof initialization, a penalty function and a modified genetic operator. Simulations indicate the good astringency, steady time complexity and satisfactory location accuracy of MMIGAS. Moreover, compared with manual scheduling, MMIGAS can improve the efficiency while maintaining high location precision.
随着科学技术的发展，现代通信场景对被动定位技术提出了更高的要求。然而，当前的定位系统仍然采用手动调度方法，无法满足当前任务密集和分布广泛的场景，导致任务完成效率低下。针对这一问题，本文提出了一种名为基于多目标、多约束和改进遗传算法的调度方法（MMIGAS），提出了一个具有多个目标和多个约束的集中式组合优化模型，并构思了一种改进的遗传算法。首先，我们基于被动定位系统的结构建立了一个基本的数学框架。然后，为了在多个指标之间实现平衡，并避免效率低下的问题，我们提出了一个包括定位精度、完成率和资源利用率在内的多目标最优函数。此外，为了增强其实用性，我们为频率、资源能力和任务协作制定了多个约束条件。对于模型求解，我们提出了一种改进的遗传算法，该算法具有更好的收敛速度和全局优化能力，通过引入约束证明初始化、惩罚函数和改进的遗传算子。仿真表明，MMIGAS 具有良好的收敛性、稳定的时间复杂度和令人满意的定位精度。此外，与手动调度相比，MMIGAS 可以在保持高精度定位的同时提高效率。
