15,618 research outputs found
A Multi-Agent Based Approach for Particle Swarm Optimization
We propose a new approach towards Particle Swarm Optimization named Agent-based PSO. The swarm is elevated to the status of a multi-agent system by giving the particles more autonomy, an asynchronous execution, and superior learning capabilities. The problem space is modeled as an environment which forms clusters of points that are known to be non-optimal and this transforms the environment into a more dynamic and informative resource
Random Finite Set Theory and Optimal Control of Large Collaborative Swarms
Controlling large swarms of robotic agents has many challenges including, but
not limited to, computational complexity due to the number of agents,
uncertainty in the functionality of each agent in the swarm, and uncertainty in
the swarm's configuration. This work generalizes the swarm state using Random
Finite Set (RFS) theory and solves the control problem using Model Predictive
Control (MPC) to overcome the aforementioned challenges. Computationally
efficient solutions are obtained via the Iterative Linear Quadratic Regulator
(ILQR). Information divergence is used to define the distance between the swarm
RFS and the desired swarm configuration. Then, a stochastic optimal control
problem is formulated using a modified L2^2 distance. Simulation results using
MPC and ILQR show that swarm intensities converge to a target destination, and
the RFS control formulation can vary in the number of target destinations. ILQR
also provides a more computationally efficient solution to the RFS swarm
problem when compared to the MPC solution. Lastly, the RFS control solution is
applied to a spacecraft relative motion problem showing the viability for this
real-world scenario.Comment: arXiv admin note: text overlap with arXiv:1801.0731
Chaotic Quantum Double Delta Swarm Algorithm using Chebyshev Maps: Theoretical Foundations, Performance Analyses and Convergence Issues
Quantum Double Delta Swarm (QDDS) Algorithm is a new metaheuristic algorithm
inspired by the convergence mechanism to the center of potential generated
within a single well of a spatially co-located double-delta well setup. It
mimics the wave nature of candidate positions in solution spaces and draws upon
quantum mechanical interpretations much like other quantum-inspired
computational intelligence paradigms. In this work, we introduce a Chebyshev
map driven chaotic perturbation in the optimization phase of the algorithm to
diversify weights placed on contemporary and historical, socially-optimal
agents' solutions. We follow this up with a characterization of solution
quality on a suite of 23 single-objective functions and carry out a comparative
analysis with eight other related nature-inspired approaches. By comparing
solution quality and successful runs over dynamic solution ranges, insights
about the nature of convergence are obtained. A two-tailed t-test establishes
the statistical significance of the solution data whereas Cohen's d and Hedge's
g values provide a measure of effect sizes. We trace the trajectory of the
fittest pseudo-agent over all function evaluations to comment on the dynamics
of the system and prove that the proposed algorithm is theoretically globally
convergent under the assumptions adopted for proofs of other closely-related
random search algorithms.Comment: 27 pages, 4 figures, 19 table
Route Swarm: Wireless Network Optimization through Mobility
In this paper, we demonstrate a novel hybrid architecture for coordinating
networked robots in sensing and information routing applications. The proposed
INformation and Sensing driven PhysIcally REconfigurable robotic network
(INSPIRE), consists of a Physical Control Plane (PCP) which commands agent
position, and an Information Control Plane (ICP) which regulates information
flow towards communication/sensing objectives. We describe an instantiation
where a mobile robotic network is dynamically reconfigured to ensure high
quality routes between static wireless nodes, which act as source/destination
pairs for information flow. The ICP commands the robots towards evenly
distributed inter-flow allocations, with intra-flow configurations that
maximize route quality. The PCP then guides the robots via potential-based
control to reconfigure according to ICP commands. This formulation, deemed
Route Swarm, decouples information flow and physical control, generating a
feedback between routing and sensing needs and robotic configuration. We
demonstrate our propositions through simulation under a realistic wireless
network regime.Comment: 9 pages, 4 figures, submitted to the IEEE International Conference on
Intelligent Robots and Systems (IROS) 201
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