Adaptive strategy in differential evolution via explicit exploitation and exploration controls
This is an incremental improvement for evolutionary algorithm researchers, addressing a specific bottleneck in strategy adaptation.
The paper tackles the problem of over-exploitation or over-exploration in multi-strategy adaptive differential evolution by proposing an explicit adaptation scheme that separates strategies and uses them on-demand, showing competitive performance compared to state-of-the-art algorithms in benchmark tests.
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.