The paper provides an insight into the novel meta-heuristic of the Crow Search Algorithm (CSA) as used for continuous optimization tasks. The presented procedure is inspired by the social behaviour of crows. Based on established CEC 2017 benchmark tasks instances, the paper concentrates on performed experimental parameter studies and on a comparison with the existing Particle Swarm Optimization strategy. Building on the test experiences, sets of internal parameters have been formulated which constitute recommendations for other numerical calculations. Finally, some concluding remarks on possible algorithm extensions are given.