  Existing multi-strategy adaptive differential evolution (DE) commonly
involves trials of multiple strategies and then rewards better-performing ones
with more resources. However, the trials of an exploitative or explorative
strategy may result in over-exploitation or over-exploration. To improve the
performance, this paper proposes a new strategy adaptation method, named
explicit adaptation scheme (Ea scheme), which separates multiple strategies and
employs them on-demand. It is done by dividing the evolution process into
several Selective-candidate with Similarity Selection (SCSS) generations and
adaptive generations. In the SCSS generations, the exploitation and exploration
needs are learnt by utilizing a balanced strategy. To meet these needs, in
adaptive generations, two other strategies, exploitative or explorative is
adaptively used. Experimental studies on benchmark functions demonstrate the
effectiveness of Ea scheme when compared with its variants and other adaptation
methods. Furthermore, performance comparisons with state-of-the-art
evolutionary algorithms and swarm intelligence-based algorithms show that EaDE
is very competitive.
