Forgetful Swarm Optimization for Astronomical Observation Scheduling

Abstract

In this paper, we propose a novel metaheuristic algorithm called Forgetful Swarm Optimization(FSO) for Astronomical Observation Scheduling (AOS), a type of combinatorial optimization problemdefined by the tasks and constraints assigned to the telescopes and other devices involved in astrophysicalresearch. FSO combines local optimization, Destroy and Repair, and Swarm Intelligence methodologies tocreate a flexible and scalable global optimization algorithm to handle the challenges of AOS. The proposalis 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 greedyalgorithm. The experimental evaluation demonstrates that FSO consistently outperforms other algorithmsin objective completeness, up to 8.4% on average, for all instances of the problem regardless of dimensionsand complexity. Additionally, it has significantly less computational cost than ILS and the base models of aglobal optimization algorithm such as EA.