The Particle Swarm Differential Evolution Algorithm for Ecological Sensor Network Coverage Optimization

Abstract

The problem of coverage optimization is the challengingly important and key part in the research and application of ecology sensor network related with the ecological monitoring of Poyang Lake. A modified differential evolution algorithm (PSI-DE) combined with particle swarm intelligence is proposed to solve the coverage optimization problem. First, an improved version of the mutation rule combined with self-cognitive and social-cognitive items is introduced. Then, the influence on the coverage optimization performance of the PSI-DE algorithm brought by the five factors – namely, population size, number of iterations, sensing radius size, raster size, and number of nodes – is discussed and analyzed. The statistical results about the best coverage rate, average coverage rate, worst coverage rate, and variance are respectively obtained through a lot of simulation experiments. A series of the coverage rate curves, the line chart, and the node layout are drawn in this paper, and finally, the figures and the statistical results are proven to confirm each other.

Keywords

• coverage optimization
• differential evolution
• ecological sensor network
• hybrid algorithm
• particle swarm optimization