Journal
COMPUTATIONAL & APPLIED MATHEMATICS
Volume 36, Issue 1, Pages 433-458Publisher
SPRINGER HEIDELBERG
DOI: 10.1007/s40314-015-0237-0
Keywords
Hybrid optimization; Estimation of distribution algorithm; Chaotic differential evolution algorithm; Convergence; Global optimization
Categories
Funding
- National Natural Science Foundation of China [51365030]
- scientific research funds from Gansu University
- General and Special Program of the Postdoctoral Science Foundation of China
- Science Foundation for Excellent Youth Scholars of Lanzhou University of Technolog [1114ZTC139, 2012M521802, 2013T60889, 1014ZCX017]
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Estimation of distribution algorithms (EDAs) and differential evolution (DE) are two types of evolutionary algorithms. The former has fast convergence rate and strong global search capability, but is easily trapped in local optimum. The latter has good local search capability with slower convergence speed. Therefore, a new hybrid optimization algorithm which combines the merits of both algorithms, a hybrid optimization algorithm based on chaotic differential evolution and estimation of distribution (cDE/EDA) was proposed. Due to its effective nature of harmonizing the global search of EDA with the local search of DE, the proposed algorithm can discover the optimal solution in a fast and reliable manner. Chaotic policy was used to strengthen the search ability of DE. Meantime the global convergence of algorithm was analyzed with the aid of limit theorem of monotone bounded sequence. The proposed algorithm was tested through a set of typical benchmark problems. The results demonstrate the effectiveness and efficiency of the proposed cDE/EDA algorithm.
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