Improvements of Particle Swarm in Binary CSPs with Maximal Degree Variables Ordering

Constraint satisfaction problems is an important research area in artificial intelligence. People now pay more attention to particle swarm intelligence to solve CSPs. But the calculation of evaluation in particle swarm of CSPs is to determine whether the conflict is zero in one variable with its related variables. This way treats each variable equally. Adding max-degree static variable ordering of variables to fitness function is proposed, and now each variable is treated differently. Thus certain variables' instantiation satisfies some constraints firstly with high probability and affects the direction of the whole swarm by selecting the global best particle and local best particles. Random generated constraints satisfaction problems show that this improvement is efficient, which has better capacity in searching and could converge to global solution faster.