Coverage Optimization of WSN Based Upon Improved Salp Swarm Algorithm

In line with the problem of caused by high aggregation in random deployment of wireless sensor networks, a new coverage strategy because of improved salp swarm algorithm is proposed. Firstly, logistic mapping is used to initialize, which makes the population well-distributed in the search space, and improves the diversity of its initial individuals and the convergence speed of its prophase; Secondly, the sine cosine strategy is introduced in the leader stage to boost the global and local exploration capability of this algorithm; finally, Gaussian mutation operators and greedy selection strategies are added to the follower stage, and the optimal individual parameter evolution results are selected, which improves the algorithm development ability. The experimental consequence demonstrates that under the same scenario, the improved algorithm can availably reduce the redundancy of sensor nodes and greatly improve the convergence speed and coverage.