Abstract:

The fruit fly optimization algorithm (FOA) is easy to fall into local extremum and has slow convergence speed. To overcome these problems, combining the advantages of Cauchy mutation and Gaussian mutation, we propose the concepts of mutation effectiveness coefficient and CauchyGaussian dynamic reduction mutation factor and a fruit fly optimization algorithm with CauchyGaussian dynamic reduction mutation (FOACGDRM) as well, which takes into account of both the global exploration character and the local exploitation character, and which is  applied to improve the FOA. The FOACGDRM can enrich population diversity, effectively avoid local extremum and enhance the convergence speed. Simulation experiments on classic function instances and practical engineering instances verify the FOACGDRM, which show that the FOACGDRM is better in precision speed and stability.

Key words: fruit fly optimization;Cauchy mutation;Gaussian mutation;dynamic reduction mutation;mutation effectiveness coefficient;dynamic reduction mutation factor