Publications related to Particle Swarm Optimization of Memory usage in Embedded Systems

Noise-Resistant Particle Swarm Optimization for the Learning of Robust Obstacle Avoidance Controllers using a Depth Camera

Alcherio Martinoli, Inaki Navarro Oiza, Ezequiel Leonardo Di Mario

The Ranger robot was designed to interact with children in order to motivate them to tidy up their room. Its mechanical configuration, together with the limited field of view of its depth camera, make the learning of obstacle avoidance behaviors a hard pro ...

Ieee2016

Exact Algorithms for $ L ^{ 1 } $ -TV Regularization of Real-Valued or Circle-Valued Signals

Michaël Unser, Martin Kurt Storath

We consider

L ^{ 1 }

-TV regularization of univariate signals with values on the real line or on the unit circle. While the real data space leads to a convex optimization problem, the problem is nonconvex for circle-valued data. In this paper, we deriv ...

SIAM2016

Optimal Load Sharing of Parallel Compressors via Modifier Adaptation

Dominique Bonvin, Timm Faulwasser, Alejandro Gabriel Marchetti, Predrag Milosavljevic

We discuss optimal load sharing of parallel gas compressors in the presence of plant-model mismatch. We formulate this problem as a static real-time optimization task and propose to tackle it by means of modifier adaptation. Under mild assumptions, the cho ...

Ieee2016

Particle Swarm Optimization and Kalman Filtering for Demand Prediction of Commercial Buildings

François Maréchal, Samira Fazlollahi, Araz Ashouri

The integration of weather forecasts and demand prediction into the energy management system of buildings is usually achieved using a model-based predictive control. The performance of such control techniques strongly depends on the accuracy of the thermal ...

2015

SwarmViz: An Open-Source Visualization Tool for Particle Swarm Optimization

Alcherio Martinoli, Inaki Navarro Oiza, Ezequiel Leonardo Di Mario, Guillaume Stefan Jornod

Particle Swarm Optimization (PSO) is a meta-heuristic for solving high dimensional optimization problems. Due to the large number of dimensions usually employed with PSO, it is not trivial to visualize and monitor the progress of the algorithm. Because of ...

2015

Distributed Particle Swarm Optimization using Optimal Computing Budget Allocation for Multi-Robot Learning

Alcherio Martinoli, Inaki Navarro Oiza, Ezequiel Leonardo Di Mario

Particle Swarm Optimization (PSO) is a population-based metaheuristic that can be applied to optimize controllers for multiple robots using only local information. In order to cope with noise in the robotic performance evaluations, different re-evaluation ...

2015

Distributed Multi-Robot Learning using Particle Swarm Optimization

Ezequiel Leonardo Di Mario

This thesis studies the automatic design and optimization of high-performing robust controllers for mobile robots using exclusively on-board resources. Due to the often large parameter space and noisy performance metrics, this constitutes an expensive opti ...

EPFL2015

Distributed Particle Swarm Optimization - Particle Allocation and Neighborhood Topologies for the Learning of Cooperative Robotic Behaviors

Alcherio Martinoli, Inaki Navarro Oiza, Ezequiel Leonardo Di Mario

In this article we address the automatic synthesis of controllers for the coordinated movement of multiple mobile robots, as a canonical example of cooperative robotic behavior. We use five distributed noise-resistant variations of Particle Swarm Optimizat ...

2015

Distributed Graph-based Convoy Control for Networked Intelligent Vehicles

Alcherio Martinoli, Ali Marjovi, Milos Vasic, Joseph Chadi Benoit Lemaitre

This paper presents an approach for formation control of multi-lane vehicular convoys in highways. We extend a Laplacian graph-based, distributed control law such that networked intelligent vehicles can join or leave the formation dynamically without jeopa ...

IEEE2015

A Distributed Noise-Resistant Particle Swarm Optimization Algorithm for High-Dimensional Multi-Robot Learning

Alcherio Martinoli, Inaki Navarro Oiza, Ezequiel Leonardo Di Mario

Population-based learning techniques have been proven to be effective in dealing with noise in numerical benchmark functions and are thus promising tools for the high-dimensional optimization of controllers for multiple robots with limited sensing capabili ...

2015

Particle Swarm Optimization of Memory usage in Embedded Systems

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Noise-Resistant Particle Swarm Optimization for the Learning of Robust Obstacle Avoidance Controllers using a Depth Camera

Exact Algorithms for $ L ^{ 1 } $ -TV Regularization of Real-Valued or Circle-Valued Signals

Optimal Load Sharing of Parallel Compressors via Modifier Adaptation

Particle Swarm Optimization and Kalman Filtering for Demand Prediction of Commercial Buildings

SwarmViz: An Open-Source Visualization Tool for Particle Swarm Optimization

Distributed Particle Swarm Optimization using Optimal Computing Budget Allocation for Multi-Robot Learning

Distributed Multi-Robot Learning using Particle Swarm Optimization

Distributed Particle Swarm Optimization - Particle Allocation and Neighborhood Topologies for the Learning of Cooperative Robotic Behaviors

Distributed Graph-based Convoy Control for Networked Intelligent Vehicles

A Distributed Noise-Resistant Particle Swarm Optimization Algorithm for High-Dimensional Multi-Robot Learning

Optimal Load Sharing of Parallel Compressors via Modifier Adaptation

Distributed Particle Swarm Optimization using Optimal Computing Budget Allocation for Multi-Robot Learning

Distributed Multi-Robot Learning using Particle Swarm Optimization

SwarmViz: An Open-Source Visualization Tool for Particle Swarm Optimization

Distributed Particle Swarm Optimization - Particle Allocation and Neighborhood Topologies for the Learning of Cooperative Robotic Behaviors

A Distributed Noise-Resistant Particle Swarm Optimization Algorithm for High-Dimensional Multi-Robot Learning

Noise-Resistant Particle Swarm Optimization for the Learning of Robust Obstacle Avoidance Controllers using a Depth Camera

Exact Algorithms for $ L ^{ 1 } $ -TV Regularization of Real-Valued or Circle-Valued Signals

Particle Swarm Optimization and Kalman Filtering for Demand Prediction of Commercial Buildings

Distributed Graph-based Convoy Control for Networked Intelligent Vehicles