基于当前最优解的反向差分进化算法求解函数优化问题

当最优解偏离目标函数定义域的几何中心时，反向个体容易远离全局最优解，基于反向差分进化算法的性能会大幅降低. 该文引入基于当前最优解的反向学习策略，并与差分进化算法相结合，求解函数优化问题. 当前代的最优解作为候选解和相应反向个体之间的对称点，能保证反向种群的利用率始终维持在较高水平. 实验结果表明，该算法可行而高效，且算法性能的提升完全是反向个体的贡献. 此外，提出一种增强的基于反向差分进化算法，展示出此类优化方法的最优效果.

Opposition-Based Differential Evolution Using the Current Optimum for Function Optimization

When the global optimum is not located at the geometric center of the domain,the opposite numbers may lapse from the global optimum,leading to poor performance of opposition-based differential evolution.A novel opposition-based learning strategy using the current optimum is introduced,and it is combined with differential evolution for function optimization.The optimum in the current generation is served as a symmetry point between an estimate and the corresponding opposite estimate,resulting in a high rate ...