WSN Coverage Optimization Based on Improved Lion Swarm Optimization Algorithm

After the random deployment of wireless sensor nodes, issues such as overlapping coverage and detection blind spots inevitably arise. Employing intelligent optimization algorithms to optimize node deployment and expand network coverage is a common approach. However, the high-dimensional optimization problem of sensor node deployment and the complexity of coverage areas make it difficult for conventional intelligent optimization algorithms to achieve satisfactory solutions. To address these challenges, this paper proposes a wireless sensor network coverage optimization scheme based on a Lion Swarm Optimization (LSO) algorithm with a crisscross strategy. By introducing both horizontal and vertical crisscrosses of individuals during the population evolution process, the diversity of the population is increased, enhancing the algorithm's global search capability and ability to escape local optima. Results demonstrate that the improved LSO algorithm exhibits enhanced solution accuracy and convergence compared to the standard LSO algorithm. In the context of wireless sensor network coverage optimization, this algorithm provides a better design solution.