Abstract: The conventional ant colony algorithm has certain drawbacks and is highly likely to get trapped in local optima. To enhance the efficiency of the ant colony algorithm, individual ants are ranked according to their differences in optimization capabilities. Then, the metabolism mechanism of organisms is introduced to eliminate some ants with poor capabilities. New and more vigorous individuals are generated through mutation to replace the eliminated ones, thus continuously improving the optimization capability of the entire ant colony. To avoid the premature loss of colony diversity and early convergence, the algorithm's requirements for colony diversity and global optimization-seeking ability are effectively balanced by setting a reasonable elimination rate for the replacement of
old individuals with new ones. The improved ant colony algorithm is applied to the optimization of multi-dimensional functions and
compared with the immune algorithm, the standard particle swarm optimization algorithm, and the conventional ant colony algorithm.
The experimental results demonstrate that the hybrid ant colony optimization algorithm enhanced with mutation and metabolism has
superior algorithmic efficiency and global optimization-seeking ability compared with the conventional ant colony algorithm and other
common intelligent algorithms.

Key words: mutation, metabolism, ant colony algorithm, diversity, ranking