  Animals foraging alone are hypothesized to optimize the encounter rates with
resources through L\'evy walks. However, the issue of how the interactions
between multiple foragers influence their search efficiency is still not
completely understood. To address this, we consider a model to study the
optimal strategy for a group of foragers searching for targets distributed
heterogeneously. In our model foragers move on a square lattice containing
immobile but regenerative targets. At any instant a forager is able to detect
only those targets that happen to be in the same site. However, we allow the
foragers to have information about the state of other foragers. A forager who
has not detected any target walks towards the nearest location, where another
forager has detected a target, with probability $\exp{\left(-\alpha d\right)}$,
where $d$ is the distance and $\alpha$ is a parameter. The model reveals that
neither overcrowding ($\alpha\to 0$) nor independent searching
($\alpha\to\infty$) is beneficial for the group. For patchy distribution of
targets the efficiency is maximum for intermediate values of $\alpha$. Also, in
the limit $\alpha\to 0$, the length of the walks can become scale-free.
