An ant lion optimization algorithm with random fractal adaptive search strategy

摘要

For the problems of the unbalanced capability between exploration and exploitation of ant lion optimizer(ALO), an improved ALO algorithm with stochastic fractal and self-adaption searching strategy (SFSALO) is proposed. On the one hand, the stochastic fractal searching function is used for ant so as to improve the capability of exploration. On the other hand, self-adaption searching method is adopted for ant lion at the best position in order to improve the convergence precision and the exploitation ability. The simulation on 3 single-peak and 3 multi-peak benchmark functions shows that the proposed algorithm can fully balances the exploration and exploitation and significantly improves the global optimization ability and the convergence speed.