Name
Abstract | ||
|---|---|---|
In this research work we present two novel swarm heuristics based respectively on the ants and bees artificial colonies, called AS-GCP and ABC-GCP. The first is based mainly on the combination of Greedy Partitioning Crossover (GPX), and a local search approach that interact with the pheromone trails system; the last, instead, has as strengths three evolutionary operators, such as a mutation operator; an improved version of GPX and a Temperature mechanism. The aim of this work is to evaluate the efficiency and robustness of both developed swarm heuristics, in order to solve the classical Graph Coloring Problem (GCP). Many experiments have been performed in order to study what is the real contribution of variants and novelty designed both in AS-GCP and ABC-GCP. A first study has been conducted with the purpose for setting the best parameters tuning, and analyze the running time for both algorithms. Once done that, both swarm heuristics have been compared with 1 5 different algorithms using the classical DIMACS benchmark. Inspecting all the experiments done is possible to say that AS-GCP and ABC-GCP are very competitive with all compared algorithms, demonstrating thus the goodness of the variants and novelty designed. Moreover, focusing only on the comparison among AS-GCP and ABC-GCP is possible to claim that, albeit both seem to be suitable to solve the GCP, they show different features: AS-GCP presents a quickly convergence towards good solutions, reaching often the best coloring; ABC-GCP, instead, shows performances more robust, mainly in graphs with a more dense, and complex topology. Finally, ABC-GCP in the overall has showed to be more competitive with all compared algorithms than AS-GCP as average of the best colors found. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/CEC.2013.6557792 | 2013 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC) |
Keywords | Field | DocType |
robustness,color,convergence,graph coloring problem,greedy algorithms,particle swarm optimization,algorithm design and analysis,evolutionary computation | Mathematical optimization,Crossover,Swarm behaviour,Computer science,Swarm intelligence,Evolutionary computation,Greedy algorithm,Heuristics,Artificial intelligence,Local search (optimization),Machine learning,Graph coloring | Conference |
Citations | PageRank | References |
6 | 0.47 | 18 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Piero Consoli | 1 | 6 | 0.47 |
| Alessio Collera | 2 | 6 | 0.47 |
| Mario Pavone | 3 | 212 | 19.41 |