基于种群多样性的可变种群缩减差分进化算法

为了更有效地避免早熟收敛,提高算法的全局搜索能力,提出了基于种群多样性的可变种群缩减差分进化算法(Dapr-DE)。首先,Dapr-DE使用群体多样性指标控制种群规模缩减；然后,使用聚类将种群分为不同类簇,在类簇中根据适应度值删除个体,既维持了种群的多样性,又减少了由于 存在过多相似个体而导致的局部收敛。最后在CEC14测试集的30个函数优化问题上进行了实验比较,验证了所提算法的有效性。

Differential Evolution Algorithm with Adaptive Population Size Reduction Based on Population Diversity

To avoid premature effectively and improve the capability of global search,an algorithm named differential evolution algorithm with adaptive population size reduction based on population diversity(Dapr-DE) was proposed.Dapr-DE firstly uses population diversity to control the population size reduction.Then,Dapr-DE divides the population into some subpopulations by clustering and deletes the individuals according to their fitness,which keeps population diversity effectively and avoids local convergence.At last,the experimental results validate the effectiveness of the proposed algorithm on 30 real optimization problems in the CEC14 function set.