Optimal Coverage of Heterogeneous Sensors with Integer Representation

Optimal coverage of sensors in an environment is key to tracking and realizing serviceability in areas of interest ubiquitously. In this paper, we tackle the optimal sensor coverage problem by identifying a subset of$m$elements from a set of$n$sensors such that the aggregated coverage (or performance) value of the selected subset precisely satisfies a user-defined target criterion. To efficiently encode and sample the combinatorial search space, we employ an integer representation scheme through eleven gradient-free optimization algorithms that incorporate diverse selection mechanisms, multimodality strategies, exploration and exploitation trade-offs, selection pressure, similarity-based mutation, archive mechanisms, and adaptation strategies. Our computational experiments involving$\mathbf{1, 2 0 0}$sensor coverage problem instances demonstrate the merits of Particle Swarm Optimization with a Niching Strategy based on Fitness Ratio (NPSO) in consistently achieving global optima. These results highlight the potential of niching strategies in particle-based schemes to address sensor coverage problems where precise coverage and optimal resource allocation are critical.