A novel swarm intelligence optimization algorithm, group area search(GAS), is proposed, which mimics the
searching behavior patterns of gregarious creatures. In the algorithm, the search radius of each member is gradually shrunk
and moderately adjusted in the optimization process. Coupled with a cruising-following mechanism, GAS can achieve a
good balance between global exploration and local exploitation in a natural way. With the characteristics of robustness and
parallelism in nature, GAS is simple to be implemented and can easily be combined with other optimization techniques. The
test results on six benchmark functions show that the proposed algorithm has excellent global optimization capability, high
convergence accuracy and stability, which outperforms the other eight nature-inspired algorithms in general and can cope
with heterogeneous complicated function optimization problems.

swarm intelligence;evolutionary computing;cruising-following mechanism;group area search;global optimization