Differential Evolution Enhanced with Composite Population Information Based Mutation Operators
Jingliang Liao, Yiqiao Cai, Yonghong Chen, Tian Wang, Hui Tian
Journal of Digital Information Management, Vol. 13, No. 4, 2015.
DOI: 
Link: http://dline.info/fpaper/jdim/v13i4/v13i4_2.pdf
Abstract:
Differential evolution (DE) is a simple and
powerful evolutionary algorithm, which has been
successfully used in various scientific and engineering
fields. Generally, the base and difference vectors of the
mutation operator in most of DE are randomly selected
from the current population. Additionally, the population
information is not fully exploited in the design of DE. In
order to alleviate these drawbacks and enhance the
performance of DE, this study presents a DE framework
with Composite Population Information based mutation
operator (DE-CPI) for global numerical optimization. In
DE-CPI, the ring topology is employed to define a
neighborhood for each individual and then the direction
information with the neighbors is introduced into the
mutation operator of DE. By this way, the composite
population information, i.e., neighborhood and direction
information, can be fully and simultaneously utilized in
DE-CPI to guide the search of DE. In order to evaluate
the effectiveness of the proposed method, DE-CPI is
incorporated into the original DE algorithms, as well as
several advanced DE variants. Experimental results clearly
show that DE-CPI is able to enhance the performance of
most of the DE algorithms studied.