Beetle antennae search reimagined: leveraging ChatGPT’s AI to forge new frontiers in optimization algorithms

Abstract

In computational optimization, the Beetle Antennae Search algorithm is renowned for its bio-inspired mechanics and robust performance. However, its efficacy is often challenged by complex, multimodal landscapes in real-world applications. This study introduces an innovative methodology leveraging OpenAI’s ChatGPT to enhance the Beetle Antennae Search algorithm. Using ChatGPT, we developed complex benchmark optimization functions as testing grounds for the algorithm and its iterations. Through AI-guided exploration, two advanced variants were implemented: Adaptive Beetle Antennae Search, with dynamic parameter adjustment, and Adaptive Feedback Beetle Antennae Search, integrating a feedback mechanism for self-tuning. These variants were rigorously tested against the AI-suggested benchmarks, demonstrating superior convergence and precision. For example, the Adaptive Feedback Beetle Antennae Search significantly improved results on the Rastrigin and Ackley functions. This study exemplifies the transformative role of AI in algorithmic design and development, showcasing how AI can assist in creating more efficient optimization methods. By detailing our AI-assisted approach, we contribute to expanding optimization techniques and demonstrate AI’s potential as a co-creator in scientific and engineering advancements.

Keywords

• ChatGPT-guided optimization
• enhanced beetle antennae search
• adaptive parameter adjustment
• feedback mechanism in algorithms
• Algorithms & Complexity
• Artificial Intelligence