Low Voltage Power Line Communication Routing Method based on Improved Genetic Algorithm

Abstract

The low voltage power line communication (LVPLC) routing algorithms are important factors that influence network performance. Genetic Algorithms (GA) has fast random global search routing ability, but has couple of issues: it does not use the feedback information of the network; it is not as efficient in finding optimal solution. This paper proposes an improved genetic algorithm for LVPLC network routes between any two stations. The proposed method, in combination with two well-known techniques (CSMA and TDMA) offers several advantages: fuse several subnets into one network; ensure dynamic network stability; adopt to the fast random global search abilities of GA to find better solutions; feed the GA solution to an ant colony algorithm. The GA/ant colony optimization work together to ensure rapid convergence to the global optimal solution. The simulation results show the effectiveness of the proposed method.