Simulation-based multi-objective optimization for mission specific tuning of swarming robots

摘要

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.