An adaptive ranking teaching learning-based optimization algorithm to solve sensor deployment in harsh environments

Abstract

Sensors are often deployed in harsh environments, in which some threats may endanger the safety of sensors. In this paper, a sensor deployment model is developed in Wireless Sensor Networks (WSNs), in which the coverage rate and the threat risk are considered simultaneously. The model is established as an optimization problem. An adaptive ranking teaching learning-based optimization algorithm (ARTLBO) is proposed to solve the problem. Learners are divided into inferior and superior groups. The teacher phase is boosted by replacing the teacher with the top three learners, and the learner phase is improved by providing some guidance for inferior learners. The experimental results show that the proposed ARTLBO algorithm can effectively optimize the model. The fitness values of the proposed model found by the proposed ARTLBO are 0.4894, 0.4886, which are better than its competitors. The algorithm can provide a higher coverage rate and lower threat risk.