Journal of Computer Applications ›› 2019, Vol. 39 ›› Issue (9): 2511-2516.

• Artificial intelligence •

### Biogeography-based optimization algorithms based on improved migration rate models

WANG Yaping, ZHANG Zhengjun, YAN Zihan, JIN Yazhou

1. School of Science, Nanjing University of Science and Technology, Nanjing Jiangsu 210094, China
• Received:2019-02-27 Revised:2019-04-21 Online:2019-05-14 Published:2019-09-10
• Supported by:

This work is partially supported by the National Natural Science Foundation of China (61773014).

### 基于改进的迁移率模型的生物地理学优化算法

1. 南京理工大学 理学院, 南京 210094
• 通讯作者: 张正军
• 作者简介:王雅萍(1995-),女,浙江杭州人,硕士研究生,主要研究方向:数据挖掘;张正军(1965-),男,江苏阜宁人,副教授,博士,主要研究方向:数据挖掘、图形图像;颜子寒(1995-),女,福建永安人,硕士研究生,主要研究方向:数据挖掘;金亚洲(1993-),男,河南西华人,硕士研究生,主要研究方向:数据挖掘。
• 基金资助:

国家自然科学基金资助项目（61773014）。

Abstract:

Biogeography-Based Optimization (BBO) algorithm updates habitats through migration and mutation continuously to find the optimal solution, and the migration model affects the performance of the algorithm significantly. In view of the problem of insufficient adaptability of the linear migration model used in the original BBO algorithm, three nonlinear migration models were proposed. These models are based on Logistic function, cubic polynomial function and hyperbolic tangent function respectively. Optimization experiments were carried out on 17 typical benchmark functions, and results show that the migration model based on hyperbolic tangent function performs better than the linear migration model of original BBO algorithm and cosine migration model with good performance of improved algorithm. Stability test shows that the migration model based on hyperbolic tangent function performs better than the original linear migration model with different mutation rates on most test functions. The model satisfies the diversity of the solutions, and better adapts to the nonlinear migration problem with improved search ability.

CLC Number: