ANTS 2016 special issue: Editorial
Swarm Intell
Marco Dorigo 0 1 2 3
Mauro Birattari 0 1 2 3
Xiaodong Li 0 1 2 3
Manuel López-Ibáñez 0 1 2 3
Kazuhiro Ohkura 0 1 2 3
Carlo Pinciroli 0 1 2 3
Thomas Stützle 0 1 2 3
B Marco Dorigo 0 1 2 3
Xiaodong Li 0 1 2 3
Kazuhiro Ohkura 0 1 2 3
RMIT University, Melbourne, Australia
0 Hiroshima University , Hiroshima , Japan
1 University of Manchester , Manchester , UK
2 IRIDIA, Université Libre de Bruxelles , Brussels , Belgium
3 Worcester Polytechnic Institute , Worcester, MA , USA
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Six of the article submitted to the 2016 edition of the conference were accepted for
publication after at least two rounds of reviews with comments by at least three referees.
The special issue opens with “Cooperative object transport with a swarm of e-puck
robots: robustness and scalability of evolved collective strategies.” In this paper, Muhanad
H. Mohammed Alkilabi, Aparajit Narayan, and Elio Tuci show how coordinated collective
transport can be obtained with robots with minimalistic capabilities. The robots involved
in the study are assumed capable of pushing (not pulling) an object, detecting the object
(using a camera and a ring of proximity sensors), and estimating their displacement (using
a custom-made optical flow sensor). The robot controller is a neural network configured
through artificial evolution in simulated experiments and later validated with e-puck robots.
The authors evaluate the scalability and generality of the behavior and also provide insight
on its internal logic. The minimalist assumptions in this work could be used as a basis to
evaluate future implementations of collective transport systems and to assess the costs and
benefits of using increasingly capable robots.
In “Learning cluster-based classification systems with ant colony optimization
algorithms,” Khalid M. Salama and Ashraf M. Abdelbar introduce a classification algorithm
that first determines a clustering of the whole data set into data subsets. Subsequently, the
algorithm uses local classification models, each learned separately for a data subset, to
perform the classification task of new data instances. The data subsets are determined using
an ACO-based clustering approach, while for each data subset a classification algorithm
such as decision trees, rule-based systems, or support vector machines is used. An
ensemble approach is introduced that can give a weighted combination of the local classifications
to further improve performance. The experimental results obtained on a large number of
data sets indicate that the highest predictive accuracy is obtained by a system configuration
that allows different classifiers to be used for different data subsets and that employs the
ensemble-based classification approach.
In “Automatic synthesis of rulesets for programmable stochastic self-assembly of
rotationally symmetric robotic modules,” Bahar Haghighat and Alcherio Martinoli tackle the problem
of generating rulesets for programmable self-assembling robots. Extending previous work
on graph grammars, the authors propose two novel algorithms to synthesize sequential and
concurrent rulesets. The novelty of these algorithms is twofold: first, the number of rules
increases linearly with the number of latches available on the robots, rather than following a
square law; second, the generated ruleset can be used on real robots without modification, in
contrast to rulesets generated with previous approaches. The algorithms are evaluated using
both real Lily robots and two different simulation frameworks, which highlight different
aspects of the dynamics of the system. This work is a step forward in the principled design
of modular systems for medical and space applications, where stochastic self-assembly will
be an important part of their dynamics.
In “Continuous time gathering of agents with limited visibility and bearing-only sensing,”
Levi Itzhak Bellaiche and Alfred Bruckstein study the aggregation of a swarm of simple
point agents in a two-dimensional space. In particular, the authors focus on agents whose
capabilities are severely limited: each agent can only sense the direction to the neighboring
peers that are located within a finite sensing range. The contribution made in the article is to
prove that, by following a simple behavioral rule, these agents gather to a common meeting
point in finite time. Indeed, the distance between each pair of agents that initially see each
other decreases monotonically in time.
In “Stochastic stability of particle swarm optimization,” Adam Erskine, Thomas Joyce,
and J. Michael Herrmann examine the dynamics of particle swarm optimization (PSO) in
the framework of random dynamical systems. With such an approach, the swarm dynamics
are quasi-linear, which allows an analytical treatment of their stability. The simplification
assumption on the stochasticity of the PSO algorithm is no longer required. Instead, a
stochastically exact formulation can be used to show that the range of stable parameters is ac (...truncated)