A novel self-adaptive multi-strategy artificial bee colony algorithm for coverage optimization in wireless sensor networks

Abstract

Wireless sensor node coverage optimization is a critical issue in wireless sensor networks (WSN), which is a commonly typical NP-hard problem. To enhance the coverage of wireless sensor networks, coverage optimization refers to the prudent placement of resource-constrained wireless sensor nodes. Current coverage optimization techniques frequently result in local optimums and have poor optimization performance. Based on the excellent optimization performance of artificial bee colony (ABC) algorithm, this paper presents a novel self-adaptive multi-strategy artificial bee colony (SaMABC) algorithm, which designs an appropriate strategy pool and a fine-grained adaptive selection mechanism according to the coverage optimization problem. Furthermore, the algorithm is improved through using simulated annealing approach and the dynamic search step to enhance its ability to jump out of the local optimum. Compared with the state-of-the-art optimization algorithms, the evaluation results carried out in several scenarios show that SaMABC obtains the best performance in terms of coverage optimization. Specifically, the coverage of wireless sensor networks in SaMABC achieves around 99.1% and outperforms the initial coverage by up to 14.1%.