期刊
SOFT COMPUTING
卷 15, 期 4, 页码 645-665出版社
SPRINGER
DOI: 10.1007/s00500-010-0591-1
关键词
Differential evolution; Biogeography-based optimization; Hybridization; Global numerical optimization; Exploration; Exploitation
资金
- China University of Geosciences
- China Scholarship Council [2008641008]
- National High Technology Research and Development Program of China [2009AA12Z117]
Differential evolution (DE) is a fast and robust evolutionary algorithm for global optimization. It has been widely used in many areas. Biogeography-based optimization (BBO) is a new biogeography inspired algorithm. It mainly uses the biogeography-based migration operator to share the information among solutions. In this paper, we propose a hybrid DE with BBO, namely DE/BBO, for the global numerical optimization problem. DE/BBO combines the exploration of DE with the exploitation of BBO effectively, and hence it can generate the promising candidate solutions. To verify the performance of our proposed DE/BBO, 23 benchmark functions with a wide range of dimensions and diverse complexities are employed. Experimental results indicate that our approach is effective and efficient. Compared with other state-of-the-art DE approaches, DE/BBO performs better, or at least comparably, in terms of the quality of the final solutions and the convergence rate. In addition, the influence of the population size, dimensionality, different mutation schemes, and the self-adaptive control parameters of DE are also studied.
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