A Novel Evolutionary Algorithm Ensemble for Global numerical Optimization
A Novel Evolutionary Algorithm Ensemble for Global numerical Optimization
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DOI：
10.1142/s021821301350022x
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发表时间：
2013-08
期刊：
影响因子：
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通讯作者：
Yongyong Niu;Zixing Cai;Min Jin
中科院分区：
文献类型：
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作者：
Yongyong Niu;Zixing Cai;Min Jin
近年来，进化算法集成在进化计算界逐渐引起了越来越多的关注。提出了一种新的全局数值优化进化算法集成NEALE。为了在探索和利用之间做出很好的权衡，NEALE由两个组成算法组成，即，复合差分进化（CoDE）和协方差矩阵自适应进化策略（CMA-ES）。在进化过程中，CoDE的目的是探索更有前途的区域和改善人口的整体质量，而CMA-ES的目的是加快收敛速度，提高解决方案的精度。此外，NEALE鼓励组成算法之间的交互。在NEALE中，交互由预定义的代数控制，并根据组成算法的特点设计不同的交互策略。NEALE的性能进行了测试的25个基准测试功能开发的2005年IEEE进化计算大会（IEEE CEC 2005）的实时参数优化的特别会议。与其他最先进的进化算法和个体成分算法相比，NEALE的性能明显优于它们。
In the past few years, evolutionary algorithm ensembles have gradually attracted more and more attention in the community of evolutionary computation. This paper proposes a novel evolutionary algorithm ensemble for global numerical optimization, named NEALE. In order to make a good tradeoff between the exploration and exploitation, NEALE is composed of two constituent algorithms, i.e., the composite differential evolution (CoDE) and the covariance matrix adaptation evolution strategy (CMA-ES). During the evolution, CoDE aims at probing more promising regions and refining the overall quality of the population, while the purposes of CMA-ES are to accelerate the convergence speed and to enhance the accuracy of the solutions. In addition, NEALE encourages the interaction between the constituent algorithms. In NEALE, the interaction is controlled by a predefined generation number and different interaction strategies are designed according to the features of the constituent algorithms. The performance of NEALE has been tested on 25 benchmark test functions developed for the special session on real-parameter optimization of the 2005 IEEE Congress on Evolutionary Computation (IEEE CEC2005). Compared with other state-of-the-art evolutionary algorithms and the individual constituent algorithms, NEALE performs significantly better than them.