Forgetful Swarm Optimization for Astronomical Observation Scheduling

In this paper, we propose a novel metaheuristic algorithm called Forgetful Swarm Optimization (FSO) for Astronomical Observation Scheduling (AOS), a type of combinatorial optimization problem defined by the tasks and constraints assigned to the telescopes and other devices involved in astrophysical research. FSO combines local optimization, Destroy and Repair, and Swarm Intelligence methodologies to create a flexible and scalable global optimization algorithm to handle the challenges of AOS. The proposal is adapted to the well-justified scenarios of the Ariel Space Mission problem, a particular example of AOS, and compared with previous algorithms that are applied to it including an Evolutionary Algorithm (EA), an Iterated Local Search (ILS), a multi-start metaheuristic, a Tabu Search, and a Hill-Climbing greedy algorithm. The experimental evaluation demonstrates that FSO consistently outperforms other algorithms in objective completeness, up to 8.4% on average, for all instances of the problem regardless of dimensions and complexity. Additionally, it has significantly less computational cost than ILS and the base models of a global optimization algorithm such as EA.