Genetic algorithm convergence study for sensor network optimization

Abstract

Abstract This paper describes optimization of a sensor network by a Genetic Algorithm (GA). The system developed automatically generates the optimization problems depending on the events happening in the environment and constructs a GA with the appropriate internal structure for the problem at hand. GA finds the quasioptimal combination of sensors that can detect and/or locate the targets. An optimal combination is the one that minimizes the power consumption of the entire sensor network and gives the best accuracy of location of desired targets. The paper attempts to determine the percentage of the total search space (PTSS) that should be covered by GA in order to obtain consistent quasioptimal solutions in different runs. The second goal is to determine the relationship between the population size and the GA stopping criteria that for a given PTSS ensures the best performance of GA. The study is performed for the sensor network optimization problem with three targets.