Further Results on Swarms Solving Graph Coloring

Graña, Manuel; Cases, Blanca; Hernández, Carmen Añez; D’Anjou, Alicia

doi:10.1007/978-3-642-12179-1_44

Cited by 9 publications

(6 citation statements)

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“…8,16 This paper deals with the convergence conditions for this problem solving approach, both its accuracy and time response. Convergence is conditioned by the information flow between the boids, which depend of their connectivity.…”

Section: Discussionmentioning

confidence: 99%

“…The Graph Coloring Boid Swarm (GCBS) 8,16 is an enrichment of the EBS designed to solve the graph coloring problem on a given graph G = (B, E). The graph nodes are embodied into the boids, which move according to EBS dynamics modified by the graph connectivity information: Nodes that can not be colored with the same color correspond to mutually repellent boids.…”

Section: The Graph Coloring Boid Swarm (Gcbs)mentioning

confidence: 99%

“…a Graph Coloring Boid Swarm (GCBS) 8,16 is the pioneering application of Boids to solve a NP-complete combinatorial problem. GCBS boids follow conventional behavior rules of Reynolds' Boids plus some additional rules derived from the graph coloring problem.…”

Section: Introductionmentioning

confidence: 99%

“…The GCBS is built as follows: 8,16 For a given graph, boids correspond to the graph nodes. Any pair of boids corresponding to nodes linked by an edge of the graph has a repulsive relation, they move in opposite directions when they fall in the perception area.…”

Section: Introductionmentioning

confidence: 99%

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Effect of Spatial Percolation on the Convergence of a Graph Coloring Boid Swarm

Rebollo

Graña

Cases

2012

Int. J. Artif. Intell. Tools

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Graph Coloring Boid Swarm (GCBS) is the successful application of Reynolds' Boid Swarm to solve the graph coloring problem, following appropriate mapping of the problem into boids' behaviors and interpretation of the global swarm states as problem solutions. A population P of boids moves in a closed torus-shaped space S. Each individual boid perceives a disk of radius R of its surrounding space, being able to exchange information with other boids lying inside this area of perception. Two mutually perceiving boids exchanging information are connected. In this paper we show that the ratio of the radius of perception to the space size can be critical for the convergence of the Boid Swarm to the optimal configurations. First, we derive the Percolation threshold for Elementary Boid Swarms (EBS), whose dynamics are the aggregation of the boids into spatial clusters. When the radius of perception is above this percolation threshold for Elementary Boid Swarms (EBS), whose dynamics are the aggregation of the boids into spatial clusters. When the radius of perception is above this percolation threshold there is complete connectivity and convergence to a single cluster. Second, we show empirically the existence of such a Percolation effect on the Graph Coloring Boid Swarm (GCBS). We find that complete connectivity has an adverse effect on the performance of the GCBS. Finally we show a comparison of GCBS with some other algorithms over a family of generated graphs.

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Section: Discussionmentioning

confidence: 99%

Section: The Graph Coloring Boid Swarm (Gcbs)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Effect of Spatial Percolation on the Convergence of a Graph Coloring Boid Swarm

Rebollo

Graña

Cases

2012

Int. J. Artif. Intell. Tools

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“…Heuristics and random search allow to obtain approximations to the optimal solutions in bounded time. Some recent approches have applied Ant Colony Optimization (ACO) [10], Particle Swarm Optimization (PSO) [14], and Reynolds Boyd swarms [4], [11].…”

Section: Introductionmentioning

confidence: 99%

Further results of Gravitational Swarm Intelligence for Graph Coloring

Rebollo-Ruiz

Graña

2011

2011 Third World Congress on Nature and Biologically Inspired Computing

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We provide enhanced results of an innovative nature inspired algorithm to solve the Graph Coloring Problem (GCP): the Gravitational Swarm Intelligence (GSI). Swarm Intelligence solves complex problems by extracting information from the spatial configurations of of agents that decide their actions/motions based on local available information, without any central control system. In the GSI application to GCP, agents correspond to graph's nodes, moving as particles in the gravitatory field defined by some target objects corresponding to graph node colors. Knowledge of the graph's topology is available to the agents' local navigation control as friend-orfoe information. We discuss the convergence of the algorithm and test it over well-known benchmarking graphs, achieving good results in a reasonable time.

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User Assistance Tool for a WebService ERP

Ruiz

Romay

2013

Trends in Practical Applications of Agents and Multiagent Systems

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Further Results on Swarms Solving Graph Coloring

Cited by 9 publications

References 10 publications

Effect of Spatial Percolation on the Convergence of a Graph Coloring Boid Swarm

Effect of Spatial Percolation on the Convergence of a Graph Coloring Boid Swarm

Further results of Gravitational Swarm Intelligence for Graph Coloring

User Assistance Tool for a WebService ERP

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