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International Journal of Machine Learning and Cybernetics
Erschienen in: International Journal of Machine Learning and Cybernetics 3/2016

01.06.2016 | Original Article

Solving 0–1 Knapsack Problem using Cohort Intelligence Algorithm

Erschienen in: International Journal of Machine Learning and Cybernetics | Ausgabe 3/2016

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Abstract

An emerging technique, inspired from the natural and social tendency of individuals to learn from each other referred to as Cohort Intelligence (CI) is presented. Learning here refers to a cohort candidate’s effort to self supervise its own behavior and further adapt to the behavior of the other candidate which it intends to follow. This makes every candidate improve/evolve its behavior and eventually the entire cohort behavior. This ability of the approach is tested by solving an NP-hard combinatorial problem such as Knapsack Problem (KP). Several cases of the 0–1 KP are solved. The effect of various parameters on the solution quality has been discussed.The advantages and limitations of the CI methodology are also discussed.
Vorheriger Artikel Least squares recursive projection twin support vector machine for multi-class classification
Nächster Artikel The generation of band information law and the dynamic hiding of information law

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Metadaten
Titel
Solving 0–1 Knapsack Problem using Cohort Intelligence Algorithm
Publikationsdatum
01.06.2016
Erschienen in
International Journal of Machine Learning and Cybernetics / Ausgabe 3/2016
Print ISSN: 1868-8071
Elektronische ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-014-0272-y

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