Theoretical and empirical studies in Biology have showed that strategies based on different random walks, such as: Brownian random walk and Lévy random walk are the best option when there is some degree of environmental uncertainty and there is a lack of perceptual capabilities.
When a random walker has no information about where targets are located, different systematic or random searches may provide different chances to find them. However, when time consumption, energy cost and malfunction risks are determinants, an adaptive search strategy becomes necessary in order to improve the performance of the strategy. Thus, we can use a practical methodology to combine a systematic search with a random search through a biological fluctuation.
We demonstrate that, in certain environments it is possible to combine a systematic search with a random search to optimally cover a given area. Besides, this work improves the search performance in comparison with pure random walks such as Brownian walk and Lévy walk. We show these theoretical results using computer simulations.