Modified donor vector of differential evolution

Differential Evolution (DE) is a simple yet efficient stochastic algorithm for solving real world problems. However, the performance of DE is sensitive to the mutation and crossover strategies and their associated parameters. In this paper, a strategy of generating base vectors and scale factors, dynamically, within the process of search, is proposed. Named MDVDE, the method enhances the generalization ability of DE. In this method, the base vector is close to the best individual vector achieved during the course of iteration, and the scale factor is decreased during the run. The proposed strategy has been evaluated on a test-suite of 25 benchmark functions provided by the CEC 2005 special session on real parameter optimization. The results of the experiments indicate that MDVDE is competitive with respect to some other DE strategies. © 2018 Indian Pulp and Paper Technical Association. All rights reserved.