Abstract: To address the issue of low coverage rate caused by random deployment method in wireless sensor network（WSN）, this
study compares and analyzes various swarm intelligence optimization algorithms, including particle swarm optimization（ PSO）, artificial bee colony algorithm （ABC）, glowworm swarm optimization （GSO）, grey wolf optimizer （GWO）, ant lion optimization （ALO）, manta ray foraging optimization （MRFO）, sparrow search algorithm （SSA）, artificial rabbits optimization （ARO）, hunter-prey optimization（HPO）, and dung beetle optimizer （DBO） based on WSN network coverage problem. The performance of the algorithms is compared and analyzed through single-objective optimization and multi-objective optimization. The simulation results show that the performance of different algorithms is different under the same conditions and the performance of the algorithms is better as the area of the monitored region increases. Among the above algorithms, GWO, MRFO, ARO and HPO algorithms are more suitable for solving the WSN network coverage problem. Multi-objective optimization based on coverage, movement distance, and connectivity is better than single-objective optimization based on coverage alone in terms of algorithm performance for the WSN network coverage problem.

Key words: wireless sensor network, coverage, swarm intelligence optimization algorithm, algorithm comparison