On Local vs. Population-Based Heuristics for Ground Station Scheduling

Finding an optimal solution is computationally hard for most combinatorial optimization problems. Therefore the use of heuristics methods aims at finding, if not optimal, near optimal solutions in reasonable amount of computation time. Due to lack of knowledge about the landscape of fitness function, searching the solution space by heuristic methods becomes very challenging. One can search the solution space through local search methods that build a path of feasible solutions. Here, the search method selects one solution at each iteration towards reaching the near optimal solution. To this group belong Hill Climbing, Simulated Annealing, Tabu Search and other algorithms. Alternatively, one can use heuristic methods that use many feasible solutions at the same time at any iteration, known as population-based heuristics. In this group there are Genetic Algorithms (GAs and its variants), Memetic Algorithms (MAs), and more generally, Evolutionary Algorithms (EAs). The research issue is, for a given combinatorial optimization problem, which of the two search methods is more effective. This is even more challenging for the case of highly constraint problems. In this paper we present a study on the effectiveness of using local search vs. Population-based search for the problem of Ground Station Scheduling problem, which is known for its high complexity and over-constraint nature.