Swarm Intelligence
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Imprecise evidence in social learning
Swarm Intelligence -
AbstractSocial learning is a collective approach to decentralised
decision-making and is comprised of two processes; evidence updating and belief
fusion. In this paper we propose a social learning model in which agents’
beliefs are represented by a set of possible states, and where the evidence
collected can vary in its level of imprecision. We investigate this model using
multi-agent and multi-ro...
Distributed deformable configuration control for multi-robot systems with low-cost platforms
Swarm Intelligence - - 2022
This work presents a deformable configuration controller—a fully distributed
algorithm that enables a swarm of robots to avoid an obstacle while maintaining
network connectivity. We assume a group of robots flocking in an unknown
environment, each of which has only incomplete knowledge of the geometry without
a map, a shared coordinate, or the use of a centralized control scheme. Instead,
the cont...
Evolving coordinated group behaviours through maximisation of mean mutual information
Swarm Intelligence - - 2008
Interactive ant colony optimization (iACO) for early lifecycle software design
Swarm Intelligence - Tập 8 - Trang 139-157 - 2014
Finding good designs in the early stages of the software development lifecycle
is a demanding multi-objective problem that is crucial to success. Previously,
both interactive and non-interactive techniques based on evolutionary algorithms
(EAs) have been successfully applied to assist the designer. However, recently
ant colony optimization was shown to outperform EAs at optimising quantitative
mea...
Energy-efficient indoor search by swarms of simulated flying robots without global information
Swarm Intelligence - Tập 4 - Trang 117-143 - 2010
Swarms of flying robots are a promising alternative to ground-based robots for
search in indoor environments with advantages such as increased speed and the
ability to fly above obstacles. However, there are numerous problems that must
be surmounted including limitations in available sensory and on-board processing
capabilities, and low flight endurance. This paper introduces a novel strategy
to c...
Swarm Cognition: an interdisciplinary approach to the study of self-organising biological collectives
Swarm Intelligence - Tập 5 Số 1 - Trang 3-18 - 2011
Wildfire detection in large-scale environments using force-based control for swarms of UAVs
Swarm Intelligence - Tập 17 - Trang 89-115 - 2022
Wildfires affect countries worldwide as global warming increases the probability
of their appearance. Monitoring vast areas of forests can be challenging due to
the lack of resources and information. Additionally, early detection of
wildfires can be beneficial for their mitigation. To this end, we explore in
simulation the use of swarms of uncrewed aerial vehicles (UAVs) with long
autonomy that ca...
Coherent collective behaviour emerging from decentralised balancing of social feedback and noise
Swarm Intelligence - Tập 13 - Trang 321-345 - 2019
Decentralised systems composed of a large number of locally interacting agents
often rely on coherent behaviour to execute coordinated tasks. Agents cooperate
to reach a coherent collective behaviour by aligning their individual behaviour
to the one of their neighbours. However, system noise, determined by factors
such as individual exploration or errors, hampers and reduces collective
coherence. ...
Multi-guide particle swarm optimisation archive management strategies for dynamic optimisation problems
Swarm Intelligence - Tập 16 - Trang 143-168 - 2022
This study presents archive management approaches for dynamic multi-objective
optimisation problems (DMOPs) using the multi-guide particle swarm optimisation
(MGPSO) algorithm by Scheepers et al. (Swarm Intell, 13(3–4):245–276, 2019,
https://doi.org/10.1007/s11721-019-00171-0 ). The MGPSO is a multi-swarm
approach developed for static multi-objective optimisation problems, where each
subswarm opt...
Inertia weight control strategies for particle swarm optimization
Swarm Intelligence - Tập 10 - Trang 267-305 - 2016
Particle swarm optimization (PSO) is a population-based, stochastic optimization
technique inspired by the social dynamics of birds. The PSO algorithm is rather
sensitive to the control parameters, and thus, there has been a significant
amount of research effort devoted to the dynamic adaptation of these parameters.
The focus of the adaptive approaches has largely revolved around adapting the
iner...
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