Investigating the Potential of AI-Driven Innovations for Enhancing Differential Evolution in Optimization Tasks

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F70883521%3A28140%2F23%3A63573938" target="_blank" >RIV/70883521:28140/23:63573938 - isvavai.cz</a>

Result on the web

<a href="https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10394233" target="_blank" >https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10394233</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/SMC53992.2023.10394233" target="_blank" >10.1109/SMC53992.2023.10394233</a>

Result language

angličtina

Original language name

Investigating the Potential of AI-Driven Innovations for Enhancing Differential Evolution in Optimization Tasks

Original language description

In recent years, artificial intelligence (AI) and machine learning have demonstrated remarkable potential in various application domains, including optimization. This study investigates the process of leveraging AI, particularly large language models (LLMs), to enhance the performance of metaheuristics, with a focus on the well-established Differential Evolution (DE) algorithm. We employ GPT, a state-of-the-art LLM, to propose an improved mutation strategy based on a dynamic switching mechanism, which is then integrated into the DE algorithm. Throughout the investigation, we also observe and analyze any errors or limitations the LLM might exhibit. We conduct extensive experiments on a comprehensive set of 30 benchmark functions, comparing the performance of the proposed AI-inspired strategy with the standard DE algorithm. The results suggest that the AI-driven dynamic switching mutation strategy provides a competitive edge in terms of solution quality, showcasing the potential of using AI to guide the development of improved optimization algorithms. This work not only highlights the effectiveness of the proposed strategy but also contributes to the understanding of the process of using LLMs for enhancing metaheuristics and the challenges involved therein.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

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Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2023

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

2023 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

ISBN

979-8-3503-3703-7

ISSN

1062-922X

e-ISSN

2577-1655

Number of pages

6

Pages from-to

1070-1075

Publisher name

IEEE

Place of publication

New Jersey, Piscataway

Event location

Honolulu

Event date

Oct 1, 2023

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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