New parallel Genetic Algorithms on GPU for solving Max-CSPs

Constraint Satisfaction Problems (CSPs) are among the easiest and more used formalisms to model real-world-constrained problems (transport, planning, scheduling, Indeed, the Genetic Algorithm (GA) is one of the optimization methods used to solve CSPs. This meta-heuristic finds a good solution in a reasonable time. However, it could be inefficient when dealing with very large-scale problems, in particular CSPs. Therefore, the High Performance Computing (HPC) is recommended, as an additional way, to accelerate the research. This paper introduces two parallel genetic algorithm-based approaches using GPU for solving Maximal Constraint Satisfaction Problems (Max-CSPs). The first approach is based on one parallelism level, while the second approach is based on two parallelism levels. The experimental results presented in this work, prove how efficient our proposed approaches are.