Swarm intelligence and the quest to solve a garbage and recycling collection problem
Abstract This work focuses on the application of Swarm Intelligence to a problem of garbage and recycling collection using a swarm of robots. Computational algorithms inspired by nature, such as Particle Swarm Optimization (PSO) and Ant Colony Optimization, have been successfully applied to a range...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Pessin, Gustavo [verfasserIn] |
|---|
| Format: |
Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2013 |
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| Schlagwörter: |
Particle Swarm Optimisation Algorithm |
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| Anmerkung: |
© Springer-Verlag Berlin Heidelberg 2013 |
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| Übergeordnetes Werk: |
Enthalten in: Soft computing - Springer Berlin Heidelberg, 1997, 17(2013), 12 vom: 13. Aug., Seite 2311-2325 |
|---|---|
| Übergeordnetes Werk: |
volume:17 ; year:2013 ; number:12 ; day:13 ; month:08 ; pages:2311-2325 |
| Links: |
|---|
| DOI / URN: |
10.1007/s00500-013-1107-6 |
|---|
| Katalog-ID: |
OLC2034874641 |
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| 520 | |a Abstract This work focuses on the application of Swarm Intelligence to a problem of garbage and recycling collection using a swarm of robots. Computational algorithms inspired by nature, such as Particle Swarm Optimization (PSO) and Ant Colony Optimization, have been successfully applied to a range of optimization problems. Our idea is to train a number of robots to interact with each other, attempting to simulate the way a collective of animals behave, as a single cognitive entity. What we have achieved is a swarm of robots that interacts like a swarm of insects, cooperating with each other accurately and efficiently. We describe two different PSO topologies implemented, showing the obtained results, a comparative evaluation, and an explanation of the rationale behind the choices of topologies that enhanced the PSO algorithm. Moreover, we describe and implement an Ant Colony Optimization (ACO) approach that presents an unusual grid implementation of a robot physical simulation. Hence, generating new concepts and discussions regarding the necessary modifications for the algorithm towards an improved performance. The ACO is then compared to the PSO results in order to choose the best algorithm to solve the proposed problem. | ||
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| 700 | 1 | |a Sales, Daniel O. |4 aut | |
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Swarm intelligence and the quest to solve a garbage and recycling collection problem |
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Pessin, Gustavo |
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Pessin, Gustavo Sales, Daniel O. Dias, Maurício A. Klaser, Rafael L. Wolf, Denis F. Ueyama, Jó Osório, Fernando S. Vargas, Patrícia A. |
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swarm intelligence and the quest to solve a garbage and recycling collection problem |
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Swarm intelligence and the quest to solve a garbage and recycling collection problem |
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Abstract This work focuses on the application of Swarm Intelligence to a problem of garbage and recycling collection using a swarm of robots. Computational algorithms inspired by nature, such as Particle Swarm Optimization (PSO) and Ant Colony Optimization, have been successfully applied to a range of optimization problems. Our idea is to train a number of robots to interact with each other, attempting to simulate the way a collective of animals behave, as a single cognitive entity. What we have achieved is a swarm of robots that interacts like a swarm of insects, cooperating with each other accurately and efficiently. We describe two different PSO topologies implemented, showing the obtained results, a comparative evaluation, and an explanation of the rationale behind the choices of topologies that enhanced the PSO algorithm. Moreover, we describe and implement an Ant Colony Optimization (ACO) approach that presents an unusual grid implementation of a robot physical simulation. Hence, generating new concepts and discussions regarding the necessary modifications for the algorithm towards an improved performance. The ACO is then compared to the PSO results in order to choose the best algorithm to solve the proposed problem. © Springer-Verlag Berlin Heidelberg 2013 |
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Abstract This work focuses on the application of Swarm Intelligence to a problem of garbage and recycling collection using a swarm of robots. Computational algorithms inspired by nature, such as Particle Swarm Optimization (PSO) and Ant Colony Optimization, have been successfully applied to a range of optimization problems. Our idea is to train a number of robots to interact with each other, attempting to simulate the way a collective of animals behave, as a single cognitive entity. What we have achieved is a swarm of robots that interacts like a swarm of insects, cooperating with each other accurately and efficiently. We describe two different PSO topologies implemented, showing the obtained results, a comparative evaluation, and an explanation of the rationale behind the choices of topologies that enhanced the PSO algorithm. Moreover, we describe and implement an Ant Colony Optimization (ACO) approach that presents an unusual grid implementation of a robot physical simulation. Hence, generating new concepts and discussions regarding the necessary modifications for the algorithm towards an improved performance. The ACO is then compared to the PSO results in order to choose the best algorithm to solve the proposed problem. © Springer-Verlag Berlin Heidelberg 2013 |
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Abstract This work focuses on the application of Swarm Intelligence to a problem of garbage and recycling collection using a swarm of robots. Computational algorithms inspired by nature, such as Particle Swarm Optimization (PSO) and Ant Colony Optimization, have been successfully applied to a range of optimization problems. Our idea is to train a number of robots to interact with each other, attempting to simulate the way a collective of animals behave, as a single cognitive entity. What we have achieved is a swarm of robots that interacts like a swarm of insects, cooperating with each other accurately and efficiently. We describe two different PSO topologies implemented, showing the obtained results, a comparative evaluation, and an explanation of the rationale behind the choices of topologies that enhanced the PSO algorithm. Moreover, we describe and implement an Ant Colony Optimization (ACO) approach that presents an unusual grid implementation of a robot physical simulation. Hence, generating new concepts and discussions regarding the necessary modifications for the algorithm towards an improved performance. The ACO is then compared to the PSO results in order to choose the best algorithm to solve the proposed problem. © Springer-Verlag Berlin Heidelberg 2013 |
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Swarm intelligence and the quest to solve a garbage and recycling collection problem |
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