An Improved Inertia Weight Firefly Optimization Algorithm and Application

Firefly Optimization Algorithm (FA) is a novel heuristic stochastic algorithm based on swarm intelligence, which is inspired by the fireflies´ biochemical and collective behavior. But for the increasing of attractiveness and the light intensity, it may excessively increase the convergence rates of the algorithm, thus the optimizing results are easily repeated oscillation on the position of local or global extreme value point, and the optimizing accuracy is reduced. Therefore, an improved inertia weight firefly optimization algorithm (IWFA) is proposed in this paper, through the introduction of the inertia weight, the algorithm has a better ability to go on a global search in the early, and can avoid premature convergence into a local optimum, the algorithm has a small inertia weight to carry through a local search at a later stage, and can increase the optimization accuracy. The test results of five benchmark functions´ optimization and PID parameters tuning show that the algorithm optimization ability is better than FA and the particle swarm optimization (PSO) algorithm.