Swarm robotics has been applied to various applications, including exploration, task allocation, and coverage problems. This application is challenging because decision-makers must calibrate swarming collective motion parameters while maintaining priority for mission-specific objectives. This paper presents a novel simulation-based multi-objective optimization framework that autonomously tunes collective motion parameters for swarming robots solving coverage problems. Our proposed Nondominated Sorting Genetic Algorithm II with Mixed Crossover Mutation (NSGA2-MCM) approach permits decision-makers to balance competing objectives by selecting optimal swarm parameters for specific mission requirements. We evaluate our approach against state-of-the-art multi-objective optimization methods using established performance metrics. Results show that our algorithm boosts Hypervolume by up to 10.82% and cuts Generational Distance by up to 7.62%. As environment complexity increases, it achieves Hypervolume gains of up to 49.98% and Generational Distance reductions of up to 24.28%. Furthermore, C-metric analysis reveals that NSGA2-MCM dominates an average of 88.89% of alternative algorithms’ solutions. |
