Learning from the opposite: Strategies for Ants that solve multidimensional Knapsack problem

Nicolás Rojas-Morales; R. María Cristina Riff; U. Elizabeth Montero

doi:10.1109/CEC.2016.7743795

Learning from the opposite: Strategies for Ants that solve multidimensional Knapsack problem

Nicolás Rojas-Morales, R. María Cristina Riff, U. Elizabeth Montero

Facultad Ingeniería

Producción científica: Contribución a los tipos de informe/libro › Contribución a la conferencia › revisión exhaustiva

5 Citas (Scopus)

Resumen

This work presents different opposite learning strategies for Ant Knapsack, an ant based algorithm for the Multidimensional Knapsack Problem. We propose to include a previous opposite learning phase to Ant Knapsack, for discarding regions of the search space. This opposite knowledge is then used by Ant Knapsack for solving the original problem. The objective is to improve the search process of Ant Knapsack maintaining its original design. We present three strategies which differ on how the solutions can be constructed on a opposite way. The results obtained are promising and encourage to use this approach for solving other problems.

Idioma original	Inglés
Título de la publicación alojada	2016 IEEE Congress on Evolutionary Computation, CEC 2016
Editorial	Institute of Electrical and Electronics Engineers Inc.
Páginas	193-200
Número de páginas	8
ISBN (versión digital)	9781509006229
DOI	https://doi.org/10.1109/CEC.2016.7743795
Estado	Publicada - 14 nov. 2016
Evento	2016 IEEE Congress on Evolutionary Computation, CEC 2016 - Vancouver, Canadá Duración: 24 jul. 2016 → 29 jul. 2016

Conferencia

Conferencia	2016 IEEE Congress on Evolutionary Computation, CEC 2016
País/Territorio	Canadá
Ciudad	Vancouver
Período	24/07/16 → 29/07/16

Áreas temáticas de ASJC Scopus

Inteligencia artificial
Modelización y simulación
Informática aplicada
Control y optimización

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10.1109/CEC.2016.7743795

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title = "Learning from the opposite: Strategies for Ants that solve multidimensional Knapsack problem",

abstract = "This work presents different opposite learning strategies for Ant Knapsack, an ant based algorithm for the Multidimensional Knapsack Problem. We propose to include a previous opposite learning phase to Ant Knapsack, for discarding regions of the search space. This opposite knowledge is then used by Ant Knapsack for solving the original problem. The objective is to improve the search process of Ant Knapsack maintaining its original design. We present three strategies which differ on how the solutions can be constructed on a opposite way. The results obtained are promising and encourage to use this approach for solving other problems.",

author = "Nicol{\'a}s Rojas-Morales and Riff, {R. Mar{\'i}a Cristina} and Montero, {U. Elizabeth}",

year = "2016",

month = nov,

day = "14",

doi = "10.1109/CEC.2016.7743795",

language = "English",

pages = "193--200",

booktitle = "2016 IEEE Congress on Evolutionary Computation, CEC 2016",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

address = "United States",

note = "2016 IEEE Congress on Evolutionary Computation, CEC 2016 ; Conference date: 24-07-2016 Through 29-07-2016",

}

Rojas-Morales, N, Riff, RMC & Montero, UE 2016, Learning from the opposite: Strategies for Ants that solve multidimensional Knapsack problem. En 2016 IEEE Congress on Evolutionary Computation, CEC 2016., 7743795, Institute of Electrical and Electronics Engineers Inc., pp. 193-200, 2016 IEEE Congress on Evolutionary Computation, CEC 2016, Vancouver, Canadá, 24/07/16. https://doi.org/10.1109/CEC.2016.7743795

Learning from the opposite: Strategies for Ants that solve multidimensional Knapsack problem. / Rojas-Morales, Nicolás; Riff, R. María Cristina; Montero, U. Elizabeth.
2016 IEEE Congress on Evolutionary Computation, CEC 2016. Institute of Electrical and Electronics Engineers Inc., 2016. p. 193-200 7743795.

Producción científica: Contribución a los tipos de informe/libro › Contribución a la conferencia › revisión exhaustiva

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AB - This work presents different opposite learning strategies for Ant Knapsack, an ant based algorithm for the Multidimensional Knapsack Problem. We propose to include a previous opposite learning phase to Ant Knapsack, for discarding regions of the search space. This opposite knowledge is then used by Ant Knapsack for solving the original problem. The objective is to improve the search process of Ant Knapsack maintaining its original design. We present three strategies which differ on how the solutions can be constructed on a opposite way. The results obtained are promising and encourage to use this approach for solving other problems.

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Learning from the opposite: Strategies for Ants that solve multidimensional Knapsack problem

Resumen

Conferencia

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