Particle Swarm Optimization Based on Adaptive Multiple Mutation

2014 
Considering the premature convergence problem of particle swarm optimization,a new adaptive particle swarm optimization is presented based on adaptive multiple mutation.The mutation probability for the current best particle is determined by two factors,including the variance of the population’s fitness and the current optimal solution.The ability of particle swarm optimization algorithm to break away from the local optinum is greatly improved by the mutation.A good performance of the algorithm is ensured in theory.The experimental results show that the new algorithm of global search capability not only is 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.
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