Fruit Fly Optimization Algorithm Based on Single-Gene Mutation for High-Dimensional Unconstrained Optimization Problems

Abstract

The fruit fly optimization (FFO) algorithm is a new swarm intelligence optimization algorithm. In this study, an adaptive FFO algorithm based on single-gene mutation, named AFFOSM, is designed to aim at inefficiency under all-gene mutation mode when solving the high-dimensional optimization problems. The use of a few adaptive strategies is core to the AFFOSM algorithm, including any given population size, mutation modes chosen by a predefined probability, and variation extents changed with the optimization progress. At first, an offspring individual is reproduced from historical best fruit fly individual, namely, elite reproduction mechanism. And then either uniform mutation or Gauss mutation happens by a predefined probability in a randomly selected gene. Variation extent is dynamically changed with the optimization progress. The simulation results show that AFFOSM algorithm has a better accuracy of convergence and capability of global search than the ESSMER algorithm and several improved versions of the FFO algorithm.