Improved sparrow search algorithm optimizes coverage in wireless sensor networks
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.
information processing technologysparrow search algorithmwireless sensor network coveragegood point set initializationdynamic learning mechanismLévy flight strategy
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