Title: A hybrid genetic algorithm based method for smart beef farming

Authors: Kangshun Li; Junhao Chen; Ziheng Chen; Wenyan Lin

Addresses: College of Big Data and Computer Science, Guangdong Baiyun University, Guangzhou, 510450, China; College of Mathematics and Informatics, South China Agricultural University, Guangzhou, 510642, China ' College of Software Engineering, South China Agricultural University, Guangzhou, 510642, China ' College of Software Engineering, South China Agricultural University, Guangzhou, 510642, China ' College of Mathematics and Informatics, South China Agricultural University, Guangzhou, 510642, China

Abstract: To enhance cost efficiency in the cattle industry, particularly through feed formulation optimisation, we propose a novel feed encoding method that accurately and simply expresses the proportions between different feeds. Building upon this encoding method, we introduce adaptive simulated annealing genetic algorithm (ASAGA), a hybrid genetic algorithm designed to optimise feed costs. ASAGA cleverly combines the powerful global search capability of genetic algorithms with the effective local optimisation ability of simulated annealing. It incorporates an elite pool strategy to retain high-potential individuals during population evolution and utilises adaptive crossover and mutation strategies to improve adaptability and resolution efficiency. Furthermore, we introduce three different neighbourhood structure strategies to enhance exploration of the solution space. Experimental results have demonstrated the effectiveness of ASAGA in optimising feed costs for smart cattle farming.

Keywords: adaptive genetic algorithm; AGA; simulated annealing algorithm; smart cattle farming.

DOI: 10.1504/IJBIC.2026.151785

International Journal of Bio-Inspired Computation, 2026 Vol.27 No.1, pp.1 - 16

Received: 06 Sep 2024
Accepted: 05 Jul 2025
Published online: 19 Feb 2026 *

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