  Population-based search algorithms (PBSAs), including swarm intelligence
algorithms (SIAs) and evolutionary algorithms (EAs), are competitive
alternatives for solving complex optimization problems and they have been
widely applied to real-world optimization problems in different fields. In this
study, a novel population-based across neighbourhood search (ANS) is proposed
for numerical optimization. ANS is motivated by two straightforward assumptions
and three important issues raised in improving and designing efficient PBSAs.
In ANS, a group of individuals collaboratively search the solution space for an
optimal solution of the optimization problem considered. A collection of
superior solutions found by individuals so far is maintained and updated
dynamically. At each generation, an individual directly searches across the
neighbourhoods of multiple superior solutions with the guidance of a Gaussian
distribution. This search manner is referred to as across neighbourhood search.
The characteristics of ANS are discussed and the concept comparisons with other
PBSAs are given. The principle behind ANS is simple. Moreover, ANS is easy for
implementation and application with three parameters being required to tune.
Extensive experiments on 18 benchmark optimization functions of different types
show that ANS has well balanced exploration and exploitation capabilities and
performs competitively compared with many efficient PBSAs (Related Matlab codes
used in the experiments are available from
http://guohuawunudt.gotoip2.com/publications.html).
