中文摘要：

为提高差分演化算法的性能，提出一种精英反向学习策略的差分演化算法．该算法以一定的概率通过反向学习生成种群中精英个体的反向解，引入一般化系数k，构造动态搜索边界下的反向群体形成反向搜索空间，之后同时评估当前种群与反向种群的解来指导算法的搜索空间向包含全局最优解的空间逼近，有利于均衡算法的勘探与开采能力．对13个典型的测试函数进行实验，将本文算法与5种代表性的差分演化算法进行对比，结果表明本文算法不仅在求解精度上更优，在收敛速度上也有非常大的优势．

英文摘要：

A differential evolution （DE） algorithm using elite opposition-based learning strategy is proposed for enhancing its perform- ance. The opposite solution of the elite individual in the population is generated at a certain probability by opposition-based learning in the proposed algorithm, and a generalized coefficient k is introduced to form an opposite search space by constructing the opposite population that locates inside the dynamic search boundaries, then the search space of algorithm is guided to approximate the space in which the global optimum is included by simultaneously evaluate the current population and the opposite one. This approach is helpful to obtain a tradeoff between exploration and exploitation ability of DE. The experiments are conducted on 13 classic benchmark func- tions, and the experimental results compared with other 5 representative DE variants show that the proposed algorithm is much better than compared ones at not only the accuracy of solutions but also for the convergence speed.