Underestimation-Assisted Global-Local Cooperative Differential Evolution and the Application to Protein Structure Prediction.

ABSTRACT: Various mutation strategies show distinct advantages in differential evolution (DE). The cooperation of multiple strategies in the evolutionary process may be effective. This paper presents an underestimation-assisted global and local cooperative DE to simultaneously enhance the effectiveness and efficiency. In the proposed algorithm, two phases, namely, the global exploration and the local exploitation, are performed in each generation. In the global phase, a set of trial vectors is produced for each target individual by employing multiple strategies with strong exploration capability. Afterward, an adaptive underestimation model with a self-adapted slope control parameter is proposed to evaluate these trial vectors, the best of which is selected as the candidate. In the local phase, the

SUBMITTER: Zhou XG 

PROVIDER: S-EPMC7885903 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature