Solving a multi-objective heterogeneous sensor network location problem with genetic algorithm

In this paper, we consider a multi-purpose two-level location problem introduced by Karatas (2020) to improve the coverage performance of heterogeneous sensor networks. The problem basically seeks to determine the best location scheme of sensors of different types and characteristics in a belt-shaped boundary area with the purpose of providing a sufficient level of field, point and barrier coverage against different types of intruders. To solve the problem, first, the Mixed Integer Linear Programming (MILP) model developed by Karatas (2020) is used together with a commercial solver and a Non-Dominated Sorting Genetic Algorithm-II (NSGA-II), adapted to solve especially large-sized instances of the problem, is applied. Next, we compare the NSGA-II heuristic with the MILP solved via a commercial exact solver on a number of test instances. The experiment results suggest that the heuristic algorithm can produce a large number of diverse and high-quality solutions in very short computation times in comparison to the exact solver. •We consider a multi-objective location problem for heterogeneous sensor networks.•Cooperative gradual covering concept and hub–spoke topology are implemented.•We develop a Non-Dominated Sorting Genetic Algorithm-II meta-heuristic algorithm.•Exact solver and heuristic are benchmarked against different size problem instances.•Heuristic yields better solutions within shorter computation times.