Enhanced Brain Storm Optimization Algorithm for Wireless Sensor Networks Deployment

Abstract

Brain storm optimization is a young and promising swarm intelligence algorithm, which simulates the human brainstorming process. The convergent operation and divergent operation are two basic operators of the brain storm optimization. The $$k$$k means clustering is utilized in the original brain storm optimization, which needs to define the $$k$$k value before the search. To adaptively change the number of clusters during the search, a modified Affinity Propagation AP clustering method and an enhanced creating strategy are proposed on account of the structure information of single or multiple clusters. In addition, the modified brain storm optimization is applied to optimize the dynamic deployments of two different wireless sensor networks WSN. Experimental results show that the proposed algorithm achieves satisfactory results and guarantees a high coverage rate.