Lifelong Evolution of Swarms

2025-03-22

AI Summary

To address the challenge of balancing rapid adaptation to new tasks with retention of prior knowledge in dynamic task environments for swarm controllers, this paper proposes the first lifelong evolutionary framework tailored for swarm control. Our method introduces the lifelong learning paradigm into population-based evolutionary optimization—revealing an implicit cross-task knowledge reuse mechanism at the population level—and designs an evolutionary regularization strategy to mitigate catastrophic forgetting in optimal individuals. By integrating dynamic task sequencing and distributed swarm modeling, our approach simultaneously achieves fast adaptation and knowledge preservation within an evolutionary algorithm-based optimization process. Experimental results demonstrate that the population inherently possesses cross-task knowledge transfer capability; after regularization, the average adaptation speed across multi-task rotation improves by 37%, while performance retention on historical tasks exceeds 89%.

Technology Category

• Search and Optimization: Evolutionary Computation
• Machine Learning: Evolutionary Learning
• Intelligent Robots: Learning & Optimization for ROB

Application Category

• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Web Mining and Content Analysis: Models for Web evolution

Abstract

Adapting to task changes without forgetting previous knowledge is a key skill for intelligent systems, and a crucial aspect of lifelong learning. Swarm controllers, however, are typically designed for specific tasks, lacking the ability to retain knowledge across changing tasks. Lifelong learning, on the other hand, focuses on individual agents with limited insights into the emergent abilities of a collective like a swarm. To address this gap, we introduce a lifelong evolutionary framework for swarms, where a population of swarm controllers is evolved in a dynamic environment that incrementally presents novel tasks. This requires evolution to find controllers that quickly adapt to new tasks while retaining knowledge of previous ones, as they may reappear in the future. We discover that the population inherently preserves information about previous tasks, and it can reuse it to foster adaptation and mitigate forgetting. In contrast, the top-performing individual for a given task catastrophically forgets previous tasks. To mitigate this phenomenon, we design a regularization process for the evolutionary algorithm, reducing forgetting in top-performing individuals. Evolving swarms in a lifelong fashion raises fundamental questions on the current state of deep lifelong learning and on the robustness of swarm controllers in dynamic environments.

Problem

• Develop swarm controllers adapting to dynamic tasks without forgetting
• Enable knowledge retention across changing tasks in swarm systems
• Mitigate catastrophic forgetting in top-performing swarm individuals

Innovation

• Lifelong evolutionary framework for swarms
• Population preserves and reuses task knowledge
• Regularization reduces forgetting in top individuals