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Open Access

ARTICLE

Cat Swarm Algorithm Generated Based on Genetic Programming Framework Applied in Digital Watermarking

Shu-Chuan Chu1,2, Libin Fu2, Jeng-Shyang Pan2,3, Xingsi Xue4, Min Liu5,*
1 School of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing, 210000, China
2 College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, 266000, China
3 Department of Information Management, Chaoyang University of Technology, Taichung, 40601, Taiwan
4 School of Information Science and Engineering, Fujian University of Technology, Fuzhou, 350000, China
5 Key Laboratory of Hydrometeorological Disaster Mechanism and Warning of Ministry of Water Resource, Nanjing University of Information Science and Technology, Nanjing, 210000, China
* Corresponding Author: Min Liu. Email: email
(This article belongs to the Special Issue: Metaheuristic-Driven Optimization Algorithms: Methods and Applications)

Computers, Materials & Continua https://doi.org/10.32604/cmc.2025.062469

Received 18 December 2024; Accepted 10 February 2025; Published online 17 March 2025

Abstract

Evolutionary algorithms have been extensively utilized in practical applications. However, manually designed population updating formulas are inherently prone to the subjective influence of the designer. Genetic programming (GP), characterized by its tree-based solution structure, is a widely adopted technique for optimizing the structure of mathematical models tailored to real-world problems. This paper introduces a GP-based framework (GP-EAs) for the autonomous generation of update formulas, aiming to reduce human intervention. Partial modifications to tree-based GP have been instigated, encompassing adjustments to its initialization process and fundamental update operations such as crossover and mutation within the algorithm. By designing suitable function sets and terminal sets tailored to the selected evolutionary algorithm, and ultimately derive an improved update formula. The Cat Swarm Optimization Algorithm (CSO) is chosen as a case study, and the GP-EAs is employed to regenerate the speed update formulas of the CSO. To validate the feasibility of the GP-EAs, the comprehensive performance of the enhanced algorithm (GP-CSO) was evaluated on the CEC2017 benchmark suite. Furthermore, GP-CSO is applied to deduce suitable embedding factors, thereby improving the robustness of the digital watermarking process. The experimental results indicate that the update formulas generated through training with GP-EAs possess excellent performance scalability and practical application proficiency.

Keywords

Cat swarm algorithm; genetic programming; digital watermarking; update mode; mode generation framework

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