Differential Evolution (DE) was a powerful population-based evolutionary algorithm for global optimization, and it achieved great success in both evolutionary computation competitions and engineering applications. Despite the excellent performance of the state-of-the-art DE variants, there are still two main weaknesses existing within them: one is the weakness in a given mutation strategy and the other is the weakness in the corresponding parameter control (of the mutation strategy). By reviewing the existing mutation strategies in the recent state-of-the-art DE variants, it can be seen that all of them have insufficient use of the knowledge obtained during the evolution because the historical information of the population is not taken into consideration, which inevitably leads to a bad perception of the landscapes of the objectives. Moreover, the adaptations of the control parameters including F and CR in these state-of-the-art DE variants are interlaced with one another. A bad F and a good CR may produce a good trial vector candidate, then the bad F is of misuse in the parameter control and vice versa. In this paper, a novel DE variant, called Hip-DE, meaning the latest fashion of DE, with historical population based mutation strategy was proposed to tackle the above mentioned weaknesses. Moreover, novel parameter adaptive mechanisms for control parameters F and CR as well as a platform based step-decrease scheme of population size were proposed to enhance capacity of the mutation strategy. By incorporating these three advancements, the novel Hip-DE algorithm secured an overall better performance on the tested benchmarks in comparison with the recent proposed state-of-the-art DE variants.

差分进化（DE）是用于全局优化的强大的基于种群的进化算法，它在进化计算竞赛和工程应用中均取得了巨大成功。尽管最新的DE变种具有出色的性能，但它们中仍然存在两个主要弱点：一个是给定突变策略的弱点，另一个是相应参数控制（突变的弱点）战略）。通过回顾最新的最新DE变异中的现有变异策略，可以看出，由于未考虑种群的历史信息，所有这些变异都没有充分利用进化过程中获得的知识，这不可避免地导致对目标景观的错误认识。而且，这些最新的DE变体中的F和CR相互交错。不良的F和良好的CR可能会产生好的候选候选矢量，然后不良的F在参数控制中会被滥用，反之亦然。在本文中，提出了一种新颖的DE变体，称为Hip-DE，意为DE的最新形式，它具有基于历史人群的突变策略，以解决上述缺陷。此外，用于控制参数F和CR的新型参数自适应机制提出了基于平台的种群数量递减方案，以提高突变策略的能力。通过结合这三个方面的进步，与最近提出的最新的DE变体相比，新颖的Hip-DE算法在测试的基准上确保了总体上更好的性能。