关键词: 猎人猎物优化算法, 差分进化, 自适应α变异, 动态反向学习, WSN覆盖

Abstract: Abstract: An improved hunter-prey optimization (IHPO) method is proposed to improve network coverage in order to tackle the issue of broad coverage blind areas and uneven distribution of conventional wireless sensor networks. With the goal to improve population information interchange, cross-variation with dynamic proportional factors was first implemented during the prey position update stage. Second, adaptive α variation is included in the phase of updating the global ideal location to balance the algorithm's performance demands over time. Finally, the population is guided to complete dynamic reverse learning using the global optimal position of adaptive α variation perturbation, increasing the population's variety and capacity for global search. The network nodes optimized by IHPO have a greater coverage rate and are dispersed more uniformly in the WSN coverage challenge. The coverage rate may increase to 92.56% when the sensor perception capacity is insufficient, which is 18.95% greater than the original algorithm and 11.35% and 13.04% higher than the two upgraded algorithms IPSO and IGWO, respectively. The network working duration may be increased to 2500 cycles in the routing test time that the node energy consumption is more evenly distributed at the same.

Key words: hunter prey optimization algorithm, differential evolution, adaptive α variation, dynamic reverse learning, WSN coverage