Coverage optimization of wireless sensor networks based on lengthen sparrow algorithm
The paper addresses the problem that the sparrow search algorithm(SSA)converges slowly and easily falls into local optimum when it is applied to wireless sensor network coverage optimization.To solve this problem,a lengthen SSA(LSSA)is developed based on the chaotic mapping factor and positive cosine algorithm strategy,and the global variation method.To be specific,the population is initialized based on Sobol sequence and ICMIC chaotic mapping with infinite folding,which can increase the population diversity.Moreover,the positive cosine algorithm strategy with chaotic mapping factor is introduced to enhance the ability of exploring the unknown region,improving the global search performance.Then the hybrid variation strategy is used in order to accelerate the convergence speed and improve the algorithm's ability to jump out of the local optimum.The simulation results show that the improved algorithm increases the coverage of network nodes by 7%,enhances the overall performance of the network,and has practicality,stability,and robustness.
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维普
