The Impact of Network Structure on Ant Colony Optimization
Fuente:
arXiv
Salvato in:
| Autori principali: | , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2024
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866912095192219648 |
|---|---|
| author | Shimizu, Taiyo Mori, Shintaro |
| author_facet | Shimizu, Taiyo Mori, Shintaro |
| contents | Ant Colony Optimization (ACO) is a swarm intelligence methodology utilized for solving optimization problems through information transmission mediated by pheromones. As ants sequentially secrete pheromones that subsequently evaporate, the information conveyed predominantly comprises pheromones secreted by recent ants. This paper introduces a network structure into the information transmission process and examines its impact on optimization performance. The network structure is characterized by an asymmetric BA model with parameters for in-degree $r$ and asymmetry $ω$. At $ω=1$, the model describes a scale-free network; at $ω=0$, a random network; and at $ω=-1$, an extended lattice. We aim to solve the ground state search of the mean-field Ising model, employing a linear decision function for the ants with their response to pheromones quantified by the parameter $α$. For $ω>-1$, the pheromone rates for options converge to stable fixed points of the stochastic system. Below the critical threshold $α_c$, there is one stable fixed point, while above $α_c$, there are two. Notably, as $ω\to -1$, both the driving force toward stable fixed points and the strength of the noise reach their maximum, significantly enhancing the probability of finding the ground state of the Ising model. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_09059 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | The Impact of Network Structure on Ant Colony Optimization Shimizu, Taiyo Mori, Shintaro Neural and Evolutionary Computing Ant Colony Optimization (ACO) is a swarm intelligence methodology utilized for solving optimization problems through information transmission mediated by pheromones. As ants sequentially secrete pheromones that subsequently evaporate, the information conveyed predominantly comprises pheromones secreted by recent ants. This paper introduces a network structure into the information transmission process and examines its impact on optimization performance. The network structure is characterized by an asymmetric BA model with parameters for in-degree $r$ and asymmetry $ω$. At $ω=1$, the model describes a scale-free network; at $ω=0$, a random network; and at $ω=-1$, an extended lattice. We aim to solve the ground state search of the mean-field Ising model, employing a linear decision function for the ants with their response to pheromones quantified by the parameter $α$. For $ω>-1$, the pheromone rates for options converge to stable fixed points of the stochastic system. Below the critical threshold $α_c$, there is one stable fixed point, while above $α_c$, there are two. Notably, as $ω\to -1$, both the driving force toward stable fixed points and the strength of the noise reach their maximum, significantly enhancing the probability of finding the ground state of the Ising model. |
| title | The Impact of Network Structure on Ant Colony Optimization |
| topic | Neural and Evolutionary Computing |
| url | https://arxiv.org/abs/2410.09059 |