Rooster Colony Algorithm: A two-step Multi-Swarm Optimization Approach
Particle Swarm Optimization is a metaheuristic optimization algorithm inspired by the collective behavior of animal swarms where a set of candidate solutions, called particles, are randomly initialized in the search space, and their movements are iteratively updated based on their individual best so...
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| Format: | Article |
| Language: | English |
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LibraryPress@UF
2023-05-01
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| Series: | Proceedings of the International Florida Artificial Intelligence Research Society Conference |
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| Online Access: | https://journals.flvc.org/FLAIRS/article/view/133368 |
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| author | Miguel A. Salido Adriana Giret Christian Perez Carlos March |
| author_facet | Miguel A. Salido Adriana Giret Christian Perez Carlos March |
| author_sort | Miguel A. Salido |
| collection | DOAJ |
| description | Particle Swarm Optimization is a metaheuristic optimization algorithm inspired by the collective behavior of animal swarms where a set of candidate solutions, called particles, are randomly initialized in the search space, and their movements are iteratively updated based on their individual best solutions and the global best solution found by the swarm. This paper proposes a Multi-Swarm rooster colony algorithm (RCA) that considers a set of roosters, each owning a group of hens to compose a team. Each team (rooster and its hens) competes for the resource (food) with the other teams. From the combinatorial optimization point of view, each team analyzes part of the search space by an independent PSO algorithm with the same objective function. The RCA algorithm concurrently executes all PSO algorithms with different inertial weights for exploring different regions and the best solution (Gbest) of each team will compose the initial population for a new further centralized PSO algorithm that will exploit the previous solutions to search for the optimal one. Thus, the proposed RCA is composed of two steps, based on exploration and exploitation strategies to find an optimized solution in the search space. The results show that the proposed algorithm is competitive in solving well-known optimization functions. The objective is to apply this technique to solving real-life scheduling problems. |
| format | Article |
| id | doaj-art-2949fc3d9df84ace89fad9964838f13e |
| institution | DOAJ |
| issn | 2334-0754 2334-0762 |
| language | English |
| publishDate | 2023-05-01 |
| publisher | LibraryPress@UF |
| record_format | Article |
| series | Proceedings of the International Florida Artificial Intelligence Research Society Conference |
| spelling | doaj-art-2949fc3d9df84ace89fad9964838f13e2025-08-20T03:07:44ZengLibraryPress@UFProceedings of the International Florida Artificial Intelligence Research Society Conference2334-07542334-07622023-05-013610.32473/flairs.36.13336869674Rooster Colony Algorithm: A two-step Multi-Swarm Optimization ApproachMiguel A. Salido0https://orcid.org/0000-0002-4835-4057Adriana Giret1https://orcid.org/0000-0002-2311-0785Christian Perez2Carlos March3https://orcid.org/0009-0009-7525-9133Universitat Politècnica de Valencia Universitat Politecnica de ValenciaValencian Graduate School of Artificial Intelligence Universitat Politecnica de ValenciaParticle Swarm Optimization is a metaheuristic optimization algorithm inspired by the collective behavior of animal swarms where a set of candidate solutions, called particles, are randomly initialized in the search space, and their movements are iteratively updated based on their individual best solutions and the global best solution found by the swarm. This paper proposes a Multi-Swarm rooster colony algorithm (RCA) that considers a set of roosters, each owning a group of hens to compose a team. Each team (rooster and its hens) competes for the resource (food) with the other teams. From the combinatorial optimization point of view, each team analyzes part of the search space by an independent PSO algorithm with the same objective function. The RCA algorithm concurrently executes all PSO algorithms with different inertial weights for exploring different regions and the best solution (Gbest) of each team will compose the initial population for a new further centralized PSO algorithm that will exploit the previous solutions to search for the optimal one. Thus, the proposed RCA is composed of two steps, based on exploration and exploitation strategies to find an optimized solution in the search space. The results show that the proposed algorithm is competitive in solving well-known optimization functions. The objective is to apply this technique to solving real-life scheduling problems.https://journals.flvc.org/FLAIRS/article/view/133368particle swarm optimizationmetaheuristicrooster colony algorithm |
| spellingShingle | Miguel A. Salido Adriana Giret Christian Perez Carlos March Rooster Colony Algorithm: A two-step Multi-Swarm Optimization Approach Proceedings of the International Florida Artificial Intelligence Research Society Conference particle swarm optimization metaheuristic rooster colony algorithm |
| title | Rooster Colony Algorithm: A two-step Multi-Swarm Optimization Approach |
| title_full | Rooster Colony Algorithm: A two-step Multi-Swarm Optimization Approach |
| title_fullStr | Rooster Colony Algorithm: A two-step Multi-Swarm Optimization Approach |
| title_full_unstemmed | Rooster Colony Algorithm: A two-step Multi-Swarm Optimization Approach |
| title_short | Rooster Colony Algorithm: A two-step Multi-Swarm Optimization Approach |
| title_sort | rooster colony algorithm a two step multi swarm optimization approach |
| topic | particle swarm optimization metaheuristic rooster colony algorithm |
| url | https://journals.flvc.org/FLAIRS/article/view/133368 |
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