Affiliations: [a] Shenzhen Institute of Information Technology, Shen Zhen, China
| [b] South China Agriculture University, Guang Zhou, China
Correspondence:
[*]
Corresponding author. Fei Tang, Shenzhen Institute of Information Technology, Shen Zhen, and South China Agriculture University, Guang Zhou, China. E-mail: [email protected].
Abstract: To improve the optimization efficiency of the intelligent bionic optimization algorithm, this paper proposes intelligent bionic optimization algorithm based on the growth characteristics of tree branches. Firstly, the growth organ of the tree is mapped into the coding of the tree growth algorithm (intelligent bionic optimization algorithm). Secondly, the entire tree, that is the growing tree, is formed by selecting the individual that grows fast to generate the next level of shoot population. Lastly, if the growing tree reaches a certain level, the individual coding of the shoots is added to enhance the searching ability of the individuals of current generation in the growth tree growth space, so that the algorithm approaches the optimal solution. The experimental results were compared with the optimization results of the genetic algorithm and the ant colony algorithm using the classic optimization function and showed that this algorithm has fewer iterations, a faster convergence speed, higher precision, and a better optimization ability than the genetic algorithm and the ant colony algorithm.
Keywords: Individual coding, branch population, genetic algorithm, tree growth
algorithm
