Differential evolution is a famous and effective branch of evolutionary computation, which aims at tackling complex optimization problems. There are two aspects significantly affecting the overall performance of DE variants, one is trial vector generation strategy and the other is the control parameter adaptation scheme. Here in this paper, a new hierarchical archive-based trial vector generation strategy with depth information of evolution was proposed to get a better perception of landscapes of objective functions as well as to improve the candidate diversity of the trial vectors. Furthermore, novel adaptation schemes both for crossover rate Cr and for population size ps were also advanced in this paper, and consequently, an overall better optimization performance was obtained after these changes. The novel HARD-DE algorithm was verified under many benchmarks of the Congress on Evolutionary Computation (CEC) Competition test suites on real-parameter single-objective optimization as well as two benchmarks on real-world optimization from CEC2011 test suite, and the experiment results showed that the proposed HARD-DE algorithm was competitive with the other state-of-the-art DE variants.