Decoder based on Parallel Genetic Algorithm and Multi-objective Optimization for Low Density Parity Check Codes

Genetic algorithms are powerful search techniques that are used successfully to solve problems in many different disciplines. This article introduces a new Parallel Genetic Algorithm for decoding LDPC codes (PGAD). The results show that the proposed algorithm gives large gains over the Sum-Product decoder, which proves its efficiency. We also show that the fitness function must be improved by Multi-objective Optimization, for this, we applied the Weighted Sum method to improve PGAD, this new version is called (MOGAD) gives higher performance compared to one. Keywords: Parallel Genetic Algorithms decoder, Sum-Product decoder, Fitness Function, LDPC codes, Error correcting codes, Multi-objective optimization, Weighted sum method