Abstract: To solve the problem of higher-dimensional multi-modal function optimization, this work investigates a micro-population fly optimization algorithm. In the algorithm design, a local mutation strategy ensures the elitist sub-population to achieve strong exploitation, whereas the elitist individual identified in the process of evolution guides individuals included in the medium sub-population to transform towards specific directions. More-over, the elitist and worst individuals help the inferior sub-population seek diverse and high-quality indivi-duals along multiple directions. One such algorithm has the merits of structural simplicity, few parameters, strong evolution, and so on. Comparative numerical results show that the algorithm with strong global optimization and high efficiency has great potential for solving higher-dimensional function optimization problems.