In this paper, a novel meta-heuristic search algorithm inspired by rhinoceros’ natural behaviour is proposed, namely rhinoceros search algorithm (RSA). Similar to our earlier version called elephant search algorithm, RSA simplifies certain habitual characteristics of rhinoceros and stream-lines the search operations, thereby reducing the number of operational parameters required to configure the model. Via computer simulation, it is shown that RSA is able to outperform certain classical meta-heuristic algorithms. Different dimensions of optimization problems are tested, and good results are observed by RSA. The RSA is also implemented on permutation flow-shop scheduling problem (PFSP) with some representation method. Four different problem scales are used. Compared with partible swarm optimization (PSO) on PFSP, the RSA outperforms PSO on different problem scales with a 3% improvement.