Considering the complexity of wireless sensor network (WSN) coverage problems, which include many variables and a large continuous search space, a WSN coverage optimization method based on an improved artificial bee colony (ABC) algorithm with teaching strategy is proposed in this paper. ABC, which is good at exploration but poor at exploitation, is improved by introducing a teaching strategy in teaching-learning-based optimization (TLBO) that has a rapid convergence but is easily trapped in a local optima. Thus, the proposed algorithm combines the advantages of ABC strong global search ability and TLBO rapid convergence. In addition, to retain the diversity and eliminate the parameter limit in ABC, a dynamic search update strategy is introduced instead of the scout bee phase of ABC. In addition to preliminary examinations with a number of benchmark functions, the performance of the algorithm is verified by solving a complicated wireless sensor network coverage problem. The simulation results verify that the proposed algorithm achieves better balance between global and local search compared with other state-of-the-art algorithms.

针对无线传感器网络（WSN）覆盖问题复杂，变量多，连续搜索空间大的问题，提出了一种基于改进的人工蜂群（ABC）算法和教学策略的无线传感器网络覆盖优化方法。ABC是一种善于探索而又不善于开发的ABC，它通过在基于教学学习的优化（TLBO）中引入一种教学策略进行了改进，该策略具有快速收敛但很容易陷入局部最优的局面。因此，提出的算法结合了ABC强大的全局搜索能力和TLBO快速收敛的优点。另外，保留多样性并消除参数限制在ABC中，引入了动态搜索更新策略，而不是ABC的侦察蜂阶段。除了使用许多基准功能进行初步检查外，还通过解决复杂的无线传感器网络覆盖问题来验证算法的性能。仿真结果证明，与其他最新算法相比，该算法在全局搜索和局部搜索之间达到了更好的平衡。