Multi‐strategy enhanced coot algorithm for coverage optimization in wireless sensor networks

Abstract An improved coot optimization algorithm (COOT) is proposed for the coverage optimization in wireless sensor networks (WSNs). To monitor areas of interest and obtain valid data, a WSNs coverage model is developed. The population is initialized with a Cubic map and an opposition‐based learning strategy. Carrying out dimension‐by‐dimensional opposition‐based learning on the leader population to strengthen the global search ability of the algorithm, the simplex method is introduced to optimize the local exploration of the population. The experimental results demonstrate that the enhanced COOT can effectively reduce energy consumption and improve network coverage. A COOT based on simplex method and dimension‐by‐dimension opposition‐based learning is proposed to optimize the coverage of WSNs. The experimental results as Figure 2a and 2c show that the improved algorithm has faster convergence speed and higher precision, which reduces the node redundancy in the coverage optimization process of wireless sensor network and improves the coverage.