基于改进正弦余弦算法的无线传感器节点部署优化

为了提高无线传感器网络(WSN)的性能,提出了一种基于改进正弦余弦算法(ESCA)的节点部署优化方法。首先,引入双曲正弦调节因子和动态余弦波权重系数,以平衡算法的全局探索与局部开发能力;然后,提出了一种基于拉普拉斯和高斯分布的变异策略,避免算法陷入局部最优。对于基准函数的优化实验结果表明,ESCA相比引力搜索算法、鲸鱼优化算法、基本正弦余弦算法(SCA)及其改进算法具有更高的收敛精度和收敛速度。最后,将ESCA应用于WSN节点部署优化,结果表明其优化覆盖率相比改进粒子群优化算法、外推人工蜂群算法、改进灰狼优化算法和自适应混沌量子粒子群算法分别提高了1.55个百分点、7.72个百分点、2.99个百分点和7.63个百分点,用更少节点便可达到相同目标精度。

Enhanced sine cosine algorithm based node deployment optimization of wireless sensor network

In order to improve the performance of Wireless Sensor Network (WSN), a node deployment optimization method based on Enhanced Sine Cosine Algorithm (ESCA) was proposed. Firstly, hyperbolic sine regulatory factor and dynamic cosine wave weight coefficient were introduced to balance the global exploration and local exploitation capability of the algorithm. Then, a mutation strategy based on Laplacian and Gaussian distribution was proposed to avoid the algorithm falling into local optimum. The experimental results of benchmark function optimization show that, compared with gravitational search algorithm, whale optimization algorithm, basic Sine Cosine Algorithm (SCA) and improved algorithms, ESCA has better convergence accuracy and convergence speed. Finally, ESCA was applied to WSN node deployment optimization. The results show that, compared with enhanced particle swarm optimization algorithm, extrapolation artificial bee colony algorithm, improved grey wolf optimization algorithm and self-adaptive chaotic quantum particle swarm algorithm, ESCA has improved the coverage rate by 1.55 percentage points, 7.72 percentage points, 2.99 percentage points and 7.63 percentage points respectively, and achieves the same target precision with fewer nodes.