Metaheurística ACO en dos etapas aplicada al problema del Job Shop Scheduling
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Fecha
2007-07-06
Autores
Trujillo Reyes, Yaima
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Editor
Universidad Central “Marta Abreu” de Las Villas
Resumen
La metaheurística “Optimización basada en colonias de hormigas” (Ant Colony
Optimization, ACO) es uno de los nuevos paradigmas que permiten la resolución de
problemas combinatorios del tipo NP-Hard. En este trabajo de diploma proponemos
una nueva estrategia de cooperación nombrada ACO en dos etapas aplicada al
problema del Job Shop Scheduling (JSSP). Esta técnica fue propuesta por el grupo
de Inteligencia Artificial del departamento de computación de la facultad. La nueva
estrategia propone hacer una división en el espacio de búsqueda en dos etapas, en la
primera una parte de las hormigas van a solucionar un subproblema de tamaño
inferior al problema original, estas subsoluciones servirán de estado inicial para que
las hormigas restantes busquen soluciones al problema general, logrando una mejor
organización más cooperativa. Para evaluar la nueva estrategia se escogió el Ant
System (AS) y el análisis de los resultados se hizo con el paquete matemático SPSS.
El nuevo modelo muestra un mayor rendimiento en cuanto al tiempo de ejecución y
la calidad de las soluciones.
The Ant Colony Optimization metaheuristic is one of the new paradigms in the solution of NP-hard combinatorial problems. In the present work we propose a new cooperation strategy named Two-Step ACO applied to the Job Shop Scheduling Problem (JSSP). This technique was proposed by the Artificial Intelligence Research Group of the Computer Science department of our faculty. The new strategy propose a division of the search space in two stages, in the first stage, a part of the ants reach preliminary solutions with a size smaller than the original problem, these partial solutions serve as initial states for the search performed by the ants in the second stage. In order to evaluate the new strategy the Ant System algorithm was chosen; the results analysis was done through the software SPSS. The new model shows better performance than the traditional in the execution time and the solutions quality.
The Ant Colony Optimization metaheuristic is one of the new paradigms in the solution of NP-hard combinatorial problems. In the present work we propose a new cooperation strategy named Two-Step ACO applied to the Job Shop Scheduling Problem (JSSP). This technique was proposed by the Artificial Intelligence Research Group of the Computer Science department of our faculty. The new strategy propose a division of the search space in two stages, in the first stage, a part of the ants reach preliminary solutions with a size smaller than the original problem, these partial solutions serve as initial states for the search performed by the ants in the second stage. In order to evaluate the new strategy the Ant System algorithm was chosen; the results analysis was done through the software SPSS. The new model shows better performance than the traditional in the execution time and the solutions quality.
Descripción
Palabras clave
Metaheurística ACO, Estrategia de Cooperación, Problema del Job Shop Scheduling