A Parameter Control Strategy for Parallel Island-Based Metaheuristics
In the field of optimisation, the accurate configuration of parameters in metaheuristic algorithms is a critical yet often arduous task that significantly impacts the efficiency and efficacy of the search process. This study was motivated by the need to address the inefficiencies and limitations ass...
| Autores: | , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2025 |
| País: | España |
| Institución: | Consejo Superior de Investigaciones Científicas (CSIC) |
| Repositorio: | DIGITAL.CSIC. Repositorio Institucional del CSIC |
| OAI Identifier: | oai:dnet:digitalcsic_::c5e06fe16a0528484e37269f81c596ab |
| Acceso en línea: | http://hdl.handle.net/10261/428686 |
| Access Level: | acceso abierto |
| Palabra clave: | Ant Colony Optimisation Binary Combinatorial Optimisation Metaheuristics Parallel strategies Parameter control |
| Sumario: | In the field of optimisation, the accurate configuration of parameters in metaheuristic algorithms is a critical yet often arduous task that significantly impacts the efficiency and efficacy of the search process. This study was motivated by the need to address the inefficiencies and limitations associated with conventional methods of parameter configuration, which typically involve manual, trial-and-error approaches. These traditional methods can lead to suboptimal performance and increased computational overhead. To tackle these challenges, this study introduces a novel adaptive parameter control strategy for parallel island-based metaheuristics, with a particular emphasis on the ant colony optimisation (ACO) algorithm. Our research process involved extensive experimentation to evaluate the effectiveness of this adaptive strategy. We conducted a series of tests to enable real-time adjustment of key parameters based on the performance of ACO colonies, thereby enhancing both exploration and exploitation capabilities. The results indicate that the adaptive strategy consistently outperforms offline manual and automated tuning configurations, particularly in larger and more complex problem instances, providing a more efficient solution for parameter optimisation in metaheuristics. These findings highlight the potential of dynamic parameter control to reduce dependency on expert knowledge and manual tuning while improving algorithmic performance. |
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