|
Showing 1 - 3 of
3 matches in All Departments
This monograph presents examples of best practices when combining
bioinspired algorithms with parallel architectures. The book
includes recent work by leading researchers in the field and offers
a map with the main paths already explored and new ways towards the
future. Parallel Architectures and Bioinspired Algorithms will be
of value to both specialists in Bioinspired Algorithms, Parallel
and Distributed Computing, as well as computer science students
trying to understand the present and the future of Parallel
Architectures and Bioinspired Algorithms.
This monograph presents examples of best practices when combining
bioinspired algorithms with parallel architectures. The book
includes recent work by leading researchers in the field and offers
a map with the main paths already explored and new ways towards the
future. Parallel Architectures and Bioinspired Algorithms will be
of value to both specialists in Bioinspired Algorithms, Parallel
and Distributed Computing, as well as computer science students
trying to understand the present and the future of Parallel
Architectures and Bioinspired Algorithms.
This work describes a novel approach to the problem of workforce
distribution in dynamic multi-agent systems based on blackboard
architectures, focusing especially on a real-world scenario: the
multi-skill call centre. Traditionally, to address such
highly-dynamic environments, diverse greedy heuristics have been
applied to provide solutions in real-time. Basically, these
heuristics perform a continuous re-planning on the system, taking
into account its current state at all times. As decisions are
greedily taken, the distribution of the workforce may be poor in
the medium and/or long term. The usage of parallel memetic
algorithms, which are more sophisticated than standard ad-hoc
heuristics, can lead us towards much more accurate solutions. In
order to effectively apply parallel memetic algorithms to such a
dynamic environment, we introduce the concept of adaptive time
window. Thus, the size of the time window depends upon the level of
dynamism of the system at a given time. This research proposes a
set of tools to automatically determine the dynamism of the system,
as well as a novel and precise prediction module based on a neural
network and a powerful optimization method.
|
|
Email address subscribed successfully.
A activation email has been sent to you.
Please click the link in that email to activate your subscription.