基于自适应学习群体搜索技术的集成进化算法

为了提高连续数值优化算法的普适性和鲁棒性，提出了基于自适应学习群体搜索技术的集成进化算法. 该算法集成了3种自适应学习群体智能优化算法作为子算法，其中1种子算法是本文设计的，另外两种子算法来自相关文献. 相应地，整个进化种群被分成了3个子种群，在进化过程中，算法以并行的方式采用每种子算法独立地进化各自的子种群，而在进化过程的不同阶段，每种子算法的进化策略及其参数可以自适应地调整. 在实验部分，首先定义了算法性能度量标准，然后在26个较新的测试函数上做了算法性能对比实验，实验结果表明所提出的算法具有较高的普适性和鲁棒性.

Ensemble of evolution algorithm based on self-adaptive learning population search techniques

In order to enhance the performance of universality and robustness of the numerical optimization algorithms, an ensemble of evolution algorithm based on self-adaptive learning population search techniques (EEA-SLPS) is proposed. EEA-SLPS integrates three self-adaptive learning population based algorithms from different fields of stochastic search techniques. One sub-algorithm is designed in this paper, the other two are recently proposed in relevant literature. The whole individual population is divided into three sub-populations, and each sub-algorithm is employed to evolve each sub-population respectively in parallel manner during the whole search process. In each sub-algorithm, both search strategies and parameters are gradually self-adaptive in different stages of the search process. The performance of EEA-SLPS is extensively evaluated on a suite of 26 bound-constrained test functions with different characteristics. By comparing with several state-of-the-art algorithms, the experimental results clearly verify the advantages of EEA-SLPS.