基于多智能体协作的分布式算法设计方法

Abstract

This paper presents a novel approach to distributed algorithm design leveraging the power of multi-agent collaboration. The core idea is to automate the design and optimization process by training a population of intelligent agents through techniques such as evolutionary algorithms or reinforcement learning. These agents learn to coordinate and communicate, ultimately solving distributed computing problems. The system utilizes a simulated environment for both evaluation and iterative improvement. This approach contrasts with traditional manual algorithm design, offering the potential for discovering more efficient and robust solutions, especially in complex scenarios where human intuition may be limited. The research explores the potential of multi-agent systems to fundamentally change the landscape of algorithm design, moving towards adaptive and self-optimizing solutions. The key contribution lies in the automated discovery process, driven by the collective intelligence of the agents.