An improved Sparrow Search Algorithm for Wireless Sensor Network Coverage Optimization

Abstract

Sparrow search algorithm (SSA) is widely used in the field of engineering optimization. However, SSA has still faced limitations in uneven distribution of initialization population, being easily trapped into local minima, and low convergence accuracy. In order to overcome these problems, an improved sparrow search algorithm integrating multi-strategies (CRASSA) is proposed. (1) The population was initialized by Bernoulli chaos mapping to avoid the uneven initial distribution of SSA. (2) The population was update via a random wandering strategy to improve the convergence accuracy. (3) An elite reverse learning strategy was proposed to avoid falling into local optimality. It was applied to address the wireless sensor network (WSN) coverage optimization problem. The coverage rate of the CRASSA is increased by 9.76%, 9.44%, 11.99%, 5.55%, 8.37% compared to PSO, WOA, SSA, CSSA and ASFSSA. The experimental results indicate that CRASSA has good practicability in the WSN coverage optimization problem.