A differential evolution algorithm based on individual-sorting and individual-sampling strategies
Yang Lou, Junli Li, Gang Li · 2012
In this paper we propose a novel hybrid version of Differential Evolution (DE). Firstly we modify the traditional structure of population in DE and propose a new strategy for population setting, in which the population is sorted in line with the fitness values of individuals. Another method is saltatory sampling with a nonrandom order, which is utilized to select candidates for the mutation operation. Furthermore, a strategy of survival of the fittest was used for individual selection operation. Combined the two strategies with DE, differential evolution based on individual-sorting and individual-sampling (ISSDE) is proposed, via testing on a series benchmark functions and compared with three variants of DE, the simulation results show that the proposed ISSDE has a better performance both in convergence speed and robustness.