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.

中文翻译：

---

高维无约束优化问题的单基因突变果蝇优化算法

果蝇优化（FFO）算法是一种新的群体智能优化算法。在本研究中，设计了一种基于单基因突变的自适应FFO算法AFFOSM，旨在解决高维优化问题时全基因突变模式下的效率低下。AFFOSM算法的核心是使用一些自适应策略，包括任何给定的种群大小，通过预定义概率选择的突变模式以及随着优化进度而变化的变异程度。首先，从历史上最好的果蝇个体繁殖出后代个体，即精英繁殖机制。然后在随机选择的基因中以预定的概率发生均匀突变或高斯突变。变化程度随优化进度而动态变化。