Chaos Dynamic Optimization Algorithm via Reinforcement Learning Fusion

Abstract

This paper investigates the integration of chaos dynamic optimization algorithms with reinforcement learning to develop a robust and adaptable system for complex systems, particularly within control and design domains. Traditional optimization methods often rely on handcrafted parameters, limiting flexibility. This research proposes a novel approach that leverages reinforcement learning to dynamically adjust algorithm parameters, fostering a system that autonomously learns and optimizes behavior. The core mechanism centers around employing reinforcement learning to refine the chaos dynamic optimization process, resulting in enhanced accuracy and adaptability. We demonstrate the effectiveness of this fusion through simulations and a preliminary case study involving a dynamic control system. This work establishes a foundation for intelligent system design and offers a promising path towards more flexible and autonomous optimization strategies.