Fully Informed Differential Evolution
Mahamed G. H. Omran, Andries Petrus Engelbrecht, Ayed A. Salman, Suha Hamdan · 2006
Differential evolution (DE) is generally considered as a reliable, accurate, robust and fast optimization technique. DE has been successfully applied to solve a wide range of numerical optimization problems. A fully informed DE (FIDE) is proposed in this paper where each member of the individual's neighborhood contributes to the new mutant vector. The performance of FIDE is investigated and compared with other versions of DE. The experiments conducted show that FIDE generally outperformed the other DE versions in all the benchmark functions