Can multi-agent AI optimize complex processes better?
A multi-agent system for hybrid optimization
This paper presents a multi-agent system for optimizing complex engineering problems, particularly those involving "black box" scenarios where internal workings are opaque. It uses multiple optimization algorithms (solvers) concurrently, coordinated by a scheduler agent, and evaluates solutions using separate model evaluation agents. An analysis agent tracks the best solutions found and shares them with the solvers, enabling cooperation.
Key points for LLM-based multi-agent systems:
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Black Box Optimization: The system's design is relevant to LLMs, which can also be considered black boxes due to their complex inner workings. The agent-based architecture can be adapted for optimizing LLM prompts and parameters.
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Agent Collaboration: The scheduler and analysis agents facilitate collaboration among solvers (optimization algorithms). This structure can be used in LLM-based systems where multiple agents, each utilizing different prompting strategies or LLMs, collaborate to achieve a goal.
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Decoupled Evaluation: Separating the model evaluation from the solvers allows for flexible and potentially parallel evaluation, suitable for situations where interacting with an LLM is computationally expensive.
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Dynamic Optimization: The system adapts its search based on ongoing results. This is relevant for dynamic LLM tasks where the optimal strategy might change based on user interactions or other evolving factors.
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Multi-Objective Optimization: The system's support for multiple objectives is applicable to optimizing LLMs for multiple criteria (e.g., accuracy, fluency, safety).