Trajectory optimization for exposure to minimal electromagnetic pollution using genetic algorithms approach: A case study

Abstract

Low-frequency electromagnetic pollution associated with electricity supplies and electrical appliances creates broad and specific challenges. Among them, knowing the values of this pollution in urban areas to prevent long exposure in the daily life human beings is rising in today's information society.

This paper presents a comprehensive approach for, first, mapping electromagnetic pollution of complete urban areas and, second, based on the former data, the trajectories planning of commuting with minimal electromagnetic exposure.

In the first stage, the proposed approach reduces the number of necessary measurements for the pollution mapping, estimating their value by optimizing functional criteria using genetic algorithms (GAs) and considering the superposition effect of different sources.

In the second stage, a combination of a specifically designed search space and GA as optimization algorithm makes it possible to determine an optimized trajectory that represents a balanced solution between distance and exposure to magnetic fields.

The results verify the obtaining of a complete mapping with less error, between 1% and 2.5%, in power lines and medium/low voltage (MV/LV) substations, respectively. The proposed approach obtains optimized trajectories for different types of commuting (pedestrians, bikers, and vehicles), and it can be integrated into mobile applications.

Finally, the method was tested on an actual urban area in Malaga (Spain).