WSN的多目标优化任务分配建模与改进算法分析

发布时间：2018-03-14 15:32

本文选题：无线传感器网络　切入点：多目标任务优化　出处：《小型微型计算机系统》2017年08期 　论文类型：期刊论文

【摘要】：针对无线传感器网络(Wireless Sensor Network,WSN)监控区域内的入侵者选择节点进行协同攻击的任务分配问题,本文建立以节点移动距离和节点能耗为主的任务分配问题数学模型,利用约束条件将任务分配算法转化为含约束条件的多目标优化(Multi-Objective Optimization,MOO)问题;在已有离散二进制粒子群优化(Binary Particle Swarm Optimization,BPSO)算法的基础上,提出基于惯性权值幂律策略的离散二进制粒子群优化(Power-law Distribution Binary Particle Swarm Optimization,PLD-BPSO)算法,对其惯性权值进行改进,增强了算法的寻优能力;最后使用线性加权法将基于模型的多目标问题转化为单目标寻优问题,通过仿真验证了该任务分配模型和任务分配方法的理论可行性,该算法具有比标准BPSO算法更强的寻优能力.
[Abstract]:In order to solve the task assignment problem of the intruders selected by intruders in the monitoring area of Wireless Sensor Network (WSNs) for wireless sensor networks, this paper establishes a mathematical model of task assignment, which is based on the moving distance and energy consumption of the nodes. The task assignment algorithm is transformed into Multi-Objective Optimization (MOO) problem by using constraint conditions, and based on the existing discrete binary Particle Swarm Optimization (BPSO) algorithm, a new algorithm is proposed to solve the problem, which is called binary binary Particle Optimization (BPSO) algorithm, which is based on the existing discrete binary Particle Swarm Optimization (Dbinary Particle Swarm Optimization) algorithm. A discrete binary particle swarm optimization (PSO) algorithm based on the power law of inertia weight is proposed. The power law of inertia weight is improved to enhance the optimization ability of the algorithm. Finally, the linear weighting method is used to transform the multi-objective problem based on the model into a single-objective optimization problem. The theoretical feasibility of the task assignment model and the task assignment method is verified by simulation. The algorithm has a stronger ability to optimize than the standard BPSO algorithm.
【作者单位】：西安航空学院电子工程学院;
【基金】：国家自然科学基金项目(61401499)资助陕西省教育厅专项科研计划项目(16JK1395)资助
【分类号】：TN929.5;TP18;TP212.9

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