Abstract:

The Firefly Algorithm (FA) has a few disadvantages in solving the constrained global optimization problem, including that it is difficult to produce initial population, the size of relative attractiveness has nothing to do with the absolute brightness of fireflies, the inertia weight does not take full advantage of the information of objective function, and it cannot better control and constrain the mobile distance of firefly. Therefore an improved FA was proposed. Firstly, Genetic Algorithm (GA) was used to produce an initial population, which improved the production speed of initial population. Secondly, on the basis of the objective function, a dynamic self-adaptive inertia weight was added to FA to improve the convergence speed. Furthermore, a calculation method of relative attractiveness was given, and the size of relative attractiveness had something to do with the absolute brightness of fireflies. Finally, the compression factor was introduced into the location update formula of FA to control and constrain the movement distance of firefly, and thus improved the convergence speed of FA. The experimental results of four test functions show that, compared with standard FA and FA with inertia weight, the improved FA is more effective, which significantly improves computing speed and reduces iteration number.