Particle swarm optimization based on adaptive many mutation and discrete degree

The mutation probability for the current best particle is determined by two factors: the varrance of the population's fitness and the current optimal solution, discrete degree is used as index to the measure of population diversity, this paper proposes an algorithm of adaptive many mutation and discrete degrees. Discrete degree can associate itself with the parameters relevant to algorithm and can reflect the current state of population distribution in a better way, good performance of the algorithm is ensured in theory. The experimental results show that the new algorithm of global search capability not only has improved significantly, has an optimal convergence rate, but also can avoid the premature convergence problem effectively, and theory analysis show that it is feasible and availability.