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An elastic graph coloring memetic algorithm for lifelong multi-agent task allocation problems
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DOI：10.1016/j.asoc.2026.115400.png)
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Abstract
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• This work addresses the lifelong multi-agent task allocation (LMATA) problem, enabling task allocation in continuous, real-time environments in real-world application. • We propose elastic graph coloring memetic algorithm (EGAMA), a novel algorithm that incorporate backbone-based crossover, Kempe mutation and conflict-resolving local search for efficient agent-task assignments. • Extensive experiments demonstrate that EGCMA achieves a 62.15% reduction in task cycle time, outperforming current state-of-the-art methods across diverse task allocation scenarios.
Keywords:
lifelong multi-agent task allocation
elastic graph coloring
memetic algorithm
task cycle time
conflict-resolving local search
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