Adaptive Particle Swarm Optimization Algorithm With Genetic Mutation Operation

This paper presents a new adaptive particle swarm optimization algorithm with genetic mutation operator. In the algorithm, we give a new adaptive inertia weight to access to local search quickly at the front of the iteration and use the adaptive variance and immune algorithm new affinity definition of the swarm to judge whether the algorithm sink into local minimum or not, then we use a new genetic mutation operator for some particles to escape from the local minimum´s basin of attraction and realize global search. The experiments on six problems show that the modified PSO algorithm can improve the global search ability and greatly enhance the successful rate of search.