  Swarm intelligence optimization algorithms can be adopted in swarm robotics
for target searching tasks in a 2-D or 3-D space by treating the target signal
strength as fitness values. Many current works in the literature have achieved
good performance in single-target search problems. However, when there are
multiple targets in an environment to be searched, many swarm
intelligence-based methods may converge to specific locations prematurely,
making it impossible to explore the environment further. The Brain Storm
Optimization (BSO) algorithm imitates a group of humans in solving problems
collectively. A series of guided searches can finally obtain a relatively
optimal solution for particular optimization problems. Furthermore, with a
suitable clustering operation, it has better multi-modal optimization
performance, i.e., it can find multiple optima in the objective space. By
matching the members in a robotic swarm to the individuals in the algorithm
under both environments and robots constraints, this paper proposes a BSO-based
collaborative searching framework for swarm robotics called Robotic BSO. The
simulation results show that the proposed method can simulate the BSO's guided
search characteristics and has an excellent prospect for multi-target searching
problems for swarm robotics.
