Hybridizing Adaptive Biogeography-Based Optimization with Differential Evolution for Global Numerical Optimization

Abstract:

Biogeography-based optimization (BBO) is a new biogeography inspired algorithm. It mainly uses the biogeography-based migration operator to share the information among solution. Differential evolution (DE) is a fast and robust evolutionary algorithm for global optimization. In this paper, we applied a hybridization of adaptive BBO with DE approach, namely ABBO/DE/GEN, for the global numerical optimization problems. ABBO/DE/GEN adaptively changes migration probability and mutation probability based on the relation between the cost of fitness function and average cost every generation, and the mutation operators of BBO were modified based on DE algorithm and the migration operators of BBO were modified based on number of iteration to improve performance. And hence it can generate the promising candidate solutions. To verify the performance of our proposed ABBO/DE/GEN, 9 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 BBO/DE/GEN approaches, ABBO/DE/GEN performs better, or at least comparably, in terms of the quality of the final solutions and the convergence rate.

• Periodical: Applied Mechanics and Materials (Volumes 457-458)
• Pages: 1283-1287
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.457-458.1283
• Online since: October 2013
• Authors: Si Ling Feng, Qing Xin Zhu, Sheng Zhong, Xiu Jun Gong
• Keywords: Adaptive Biogeography-Based Optimization, Differential Evolution, Global Numerical Optimization

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