Proactive local search based on fdc
This paper introduces a proactive version of Hill Climbing (or Local Search). It is based on the identification of the best neighborhood through the repeated application of mutations and the evaluation of theses neighborhood by using FDC (Fitness Distance Correlation). The best neighborhood is used...
- Autores:
-
Moreno-Espino, Mailyn
Rosete-Suárez, Alejandro
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2014
- Institución:
- Universidad Nacional de Colombia
- Repositorio:
- Universidad Nacional de Colombia
- Idioma:
- spa
- OAI Identifier:
- oai:repositorio.unal.edu.co:unal/72037
- Acceso en línea:
- https://repositorio.unal.edu.co/handle/unal/72037
http://bdigital.unal.edu.co/36509/
- Palabra clave:
- Metaheuristics
Agents
Proactive Behavior
Variable Neighborhood Search
FDC
- Rights
- openAccess
- License
- Atribución-NoComercial 4.0 Internacional
| Summary: | This paper introduces a proactive version of Hill Climbing (or Local Search). It is based on the identification of the best neighborhood through the repeated application of mutations and the evaluation of theses neighborhood by using FDC (Fitness Distance Correlation). The best neighborhood is used during a time window, and then the analysis is repeated. An experimental study was conducted in 28 functions on binary strings. The proposed algorithm achieves good performance compared to other metaheuristics (Evolutionary Algorithms, Great Deluge Algorithm, Threshold Accepting, and RRT). |
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