  This paper introduces a novel approach to solve the coverage optimization
problem in multi-agent systems. The proposed technique offers a solution that
not only achieves the global optimality in the agents configuration but also
effectively handles the issue of agents remaining stationary in regions void of
information. The proposed approach leverages a novel cost function for
optimizing the agents coverage and the cost function eventually aligns with the
conventional Voronoi-based cost function. Theoretical analyses are conducted to
assure the asymptotic convergence of agents towards the optimal configuration.
A distinguishing feature of this approach lies in its departure from the
reliance on geometric methods that are characteristic of Voronoi-based
approaches; hence can be implemented more simply. Remarkably, the technique is
adaptive and applicable to various environments with both known and unknown
information distributions. Lastly, the efficacy of the proposed method is
demonstrated through simulations, and the obtained results are compared with
those of Voronoi-based algorithms.
