An adaptive genetic algorithm based on simulated annealing strategy

Qingsong Yu; Xuejun Yu

doi:10.1117/12.3031179

19 July 2024 An adaptive genetic algorithm based on simulated annealing strategy

Qingsong Yu, Xuejun Yu

Proceedings Volume 13181, Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024); 131814Y (2024) https://doi.org/10.1117/12.3031179
Event: Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024), 2024, Beijing, China

Abstract

Genetic algorithm (GA) is a predominant optimization algorithm with the advantages of a wide application range, high success rate and strong adaptability. However, GAs often suffer from the issue of falling into local optima because the excessive population fitness value generated during the global search process reduces the population diversity of GA. To overcome this issue, this paper proposes an adaptive genetic algorithm based on simulated annealing strategy (SA-AGA). Primarily, a simulated annealing strategy is designed to achieve the local search in SA-AGA. The simulated annealing strategy utilizes the fitness of the population to evaluate the new solution and randomly searches for the preferable solution around the current solution during a stated cooling process. It enables SA-AGA to effectively jump out of the local optimum. Then, two adaptive genetic operators are developed in SA-AGA to achieve the crossover operator and the mutation operator. These adaptive genetic operators enhance the genetic probability adaptively when encountering excessive fitness, which further improves the population diversity of SA-AGA and ensures stable and rapid convergence of SA-AGA. Experimental results of four test functions show that SA-AGA has superior global search capabilities and higher convergence accuracy than other comparative algorithms, and can effectively avoid premature convergence.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Qingsong Yu and Xuejun Yu "An adaptive genetic algorithm based on simulated annealing strategy", Proc. SPIE 13181, Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024), 131814Y (19 July 2024); https://doi.org/10.1117/12.3031179

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available