Publication

Applying Aspects of Multi-Robot Search to Particle Swarm Optimization

Alcherio Martinoli, James Pugh, Loïc Segapelli
2006
Conference Papers

Abstract

We present a modified version of the Particle swarm Optimization algorithm in which we adjust the virtual swarm search by incorporating inter-agent dynamics native to multi-robot search scenarios. The neighborhood structure of PSO is modified to accurately represent feasible neighborhoods in multiple robot systems with limited communication in several different ways. The new algorithms are tested on several standard benchmark problems with a varying number of dimensions and are shown to offer superior performances to the standard algorithm in some cases. Further potential modifications and uses of the new algorithms are discussed.

Official source

https://infoscience.epfl.ch/entities/publication/c6d7021d-1630-4d1b-9e9d-786902570a8a

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Alcherio Martinoli, James Pugh, Loïc Segapelli
2006
Conference Papers

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/c6d7021d-1630-4d1b-9e9d-786902570a8a

About this result

Related concepts (25)

Particle swarm optimization

In computational science, particle swarm optimization (PSO) is a computational method that optimizes a problem by iteratively trying to improve a candidate solution with regard to a given measure of quality. It solves a problem by having a population of candidate solutions, here dubbed particles, and moving these particles around in the search-space according to simple mathematical formula over the particle's position and velocity.

Ant colony optimization algorithms

In computer science and operations research, the ant colony optimization algorithm (ACO) is a probabilistic technique for solving computational problems which can be reduced to finding good paths through graphs. Artificial ants stand for multi-agent methods inspired by the behavior of real ants. The pheromone-based communication of biological ants is often the predominant paradigm used. Combinations of artificial ants and local search algorithms have become a method of choice for numerous optimization tasks involving some sort of graph, e.

Swarm robotics

Swarm robotics is an approach to the coordination of multiple robots as a system which consist of large numbers of mostly simple physical robots. ′′In a robot swarm, the collective behavior of the robots results from local interactions between the robots and between the robots and the environment in which they act.′′ It is supposed that a desired collective behavior emerges from the interactions between the robots and interactions of robots with the environment.

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