Hybrid genetic algorithm and application to PID controllers

In order to overcome simple genetic algorithm detects of worse local searching ability and premature convergence, a hybrid genetic algorithm was proposed. The novel algorithm was based on particle swarm optimization and interval algorithm, and applied to parameters optimization of PID controllers by applying particle swarm optimization to the mutation operation, and employing interval algorithm in population initialization of genetic algorithm. The simulation and experimental results show that the novel algorithm is superior to simple genetic algorithm, can overcome premature phenomena, improve the convergence precision and speed, reduce the influence of random initial population, and has advantages such as excellent optimization ability, high rate of convergence and good stability.