An ensemble algorithm with self-adaptive learning techniques for high-dimensional numerical optimization

• Published in: Applied mathematics and computation Vol. 231; pp. 329 - 346
• Main Authors: Xue, Yu, Zhong, Shuiming, Zhuang, Yi, Xu, Bin
• Format: Journal Article
• Language: English
• Published: Elsevier Inc 15.03.2014
• Subjects: Ensemble; Evolutionary algorithm; High-dimensional; Numerical optimization; Self-adaptation; Self-adaptation; Numerical optimization; High-dimensional; Evolutionary algorithm; Ensemble
• ISSN: 0096-3003; 1873-5649
• DOI: 10.1016/j.amc.2013.12.130

There are many evolutionary algorithms for numerical optimization problems. However, the universality and robustness of existing algorithms are still unsatisfactory, and the performance of these algorithms deteriorates significantly as the dimensionality of the optimization problems increases. In this paper, an ensemble of evolution algorithm based on self-adaptive learning population search techniques (EEA-SLPS) is presented to overcome these defects on the numerical optimization problems. The EEA-SLPS integrates three self-adaptive learning based stochastic search algorithms which are termed as sub-algorithms. In the EEA-SLPS, the population is divided into three sub-populations, and the sub-algorithms are employed to evolve the sub-populations in parallel. Among the three sub-algorithms, one is designed in this paper and the other two are proposed by relevant literature, and eighteen information exchanging manners (IEMs) between sub-populations are investigated in order to make use of the sub-algorithms efficiently. We have found the most suitable IEM for the EEA-SLPS according to experimental investigations. Finally, the EEA-SLPS is tested on a suite of 26 bound-constrained functions with low and high dimensionality. The experimental results indicate that the universality and robustness performance of EEA-SLPS is better. Meanwhile, the results clearly verify the advantages of EEA-SLPS on the numerical optimization problems with low or high dimensionality.