改进的麻雀搜索算法优化无线传感器网络覆盖

Improved sparrow search algorithm optimizes coverage in wireless sensor networks

针对麻雀搜索算法在高维复杂问题上由于随机性大而容易陷入局部最优的问题,提出了一种融合多策略改进的麻雀搜索算法。在初始化阶段,引入佳点集策略以确保种群具备多样性和遍历性。在发现者位置更新中,采用动态学习机制平衡全局寻优和局部探索;在跟随者位置更新中,引入莱维飞行扰动机制以增强局部逃逸能力。最后,将本文算法应用于解决无线传感器网络覆盖问题,从最大化覆盖率、最小化冗余和最大化能耗均衡3个角度对多目标覆盖优化问题进行抽象。仿真结果表明:3项改进措施显著提高了算法性能,增强了网络节点覆盖质量,使网络整体性能得到了有效提升,证明本文算法具备实际应用的良好性能。

In response to the challenges of significant randomness and susceptibility to local optima in the sparrow search algorithm,an enhanced approach was proposed integrating multiple strategies.During the initialization phase,a good point set strategy was introduced to ensure population diversity and thorough exploration.The discoverer's position update incorporates a dynamic learning mechanism,effectively balancing global optimization and local exploration capabilities.Simultaneously,the follower's position update integrates a Lévy flight disturbance mechanism,reinforcing local escape capabilities.Finally,the proposed method was applied to solve the coverage problem of wireless sensor networks.Through a multi-objective coverage optimization function,considering coverage rate maximizing,redundancy minimization,and energy consumption equilibrium maximizing.The simulation results show that the three improvement measures significantly improve the algorithm performance,enhance the coverage quality of network nodes,and effectively improve the overall performance of the network,proving that the proposed method has good performance in practical applications.