This research proposes a new nature-inspired algorithm called the Hydrological Cycle Algorithm (HCA), which simulates the movement of water drops in the hydrological water cycle. In the HCA, a collection of artificial water drops pass through various hydrological water cycle stages, such as flow, evaporation, condensation and precipitation in order to generate solutions. Each stage plays an important role in generating the solution and helps to avoid premature convergence. The HCA differs from other particle-based algorithms by using direct and indirect communication among the water drops, which helps to improve the overall performance and solution quality. The similarities and differences between HCA and other water-based algorithms are identified, and the implications of these differences on overall performance are discussed. In proof-of-concept experiments, the effectiveness and efficiency of HCA are evaluated on well-known discrete, continuous, static, and dynamic benchmarked optimisation problems. The experimental results were found to be competitive and validate the effectiveness of the proposed algorithm and its ability to escape from local optima solutions to converge on the global solution. In conclusion, the HCA provides a new particle-based conceptual framework within which existing and future work in water-based algorithms can be positioned.