Improved artificial bee colony algorithm for global optimization |
| |
Authors: | Weifeng Gao Sanyang Liu |
| |
Affiliation: | Department of Applied Mathematics, Xidian University, Xi?an 710071, China |
| |
Abstract: | The artificial bee colony algorithm is a relatively new optimization technique. This paper presents an improved artificial bee colony (IABC) algorithm for global optimization. Inspired by differential evolution (DE) and introducing a parameter M, we propose two improved solution search equations, namely “ABC/best/1” and “ABC/rand/1”. Then, in order to take advantage of them and avoid the shortages of them, we use a selective probability p to control the frequency of introducing “ABC/rand/1” and “ABC/best/1” and get a new search mechanism. In addition, to enhance the global convergence speed, when producing the initial population, both the chaotic systems and the opposition-based learning method are employed. Experiments are conducted on a suite of unimodal/multimodal benchmark functions. The results demonstrate the good performance of the IABC algorithm in solving complex numerical optimization problems when compared with thirteen recent algorithms. |
| |
Keywords: | Randomized algorithms Artificial bee colony algorithm Initial population Solution search equation Search mechanism |
本文献已被 ScienceDirect 等数据库收录! |
|