Portfolio Optimization From a Set of Preference Ordered Projects Using an Ant Colony Based Multi-objective Approach

In this paper, a good portfolio is found through an ant colony algorithm (including a local search) that approximates the Pareto front regarding some kind of project categorization, cardinalities, discrepancies with priorities given by the ranking, and the average rank of supported projects; this ap...

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Main Authors: S. Samantha Bastiani, Laura Cruz-Reyes, Eduardo Fernandez, Claudia Gomez
Format: Article
Language:English
Published: Springer 2015-12-01
Series:International Journal of Computational Intelligence Systems
Subjects:
Online Access:https://www.atlantis-press.com/article/25868672.pdf
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author S. Samantha Bastiani
Laura Cruz-Reyes
Eduardo Fernandez
Claudia Gomez
author_facet S. Samantha Bastiani
Laura Cruz-Reyes
Eduardo Fernandez
Claudia Gomez
author_sort S. Samantha Bastiani
collection DOAJ
description In this paper, a good portfolio is found through an ant colony algorithm (including a local search) that approximates the Pareto front regarding some kind of project categorization, cardinalities, discrepancies with priorities given by the ranking, and the average rank of supported projects; this approach is an improvement towards a proper modeling of preferences. The available information is only projects’ ranking and costs, and usually, resource allocation follows the ranking priorities until they are depleted. Results show that our proposal outperforms previous approaches.
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spelling doaj.art-b775831964554eb6b77d409ce014d2ea2022-12-22T02:56:47ZengSpringerInternational Journal of Computational Intelligence Systems1875-68832015-12-01810010.1080/18756891.2015.1129590Portfolio Optimization From a Set of Preference Ordered Projects Using an Ant Colony Based Multi-objective ApproachS. Samantha BastianiLaura Cruz-ReyesEduardo FernandezClaudia GomezIn this paper, a good portfolio is found through an ant colony algorithm (including a local search) that approximates the Pareto front regarding some kind of project categorization, cardinalities, discrepancies with priorities given by the ranking, and the average rank of supported projects; this approach is an improvement towards a proper modeling of preferences. The available information is only projects’ ranking and costs, and usually, resource allocation follows the ranking priorities until they are depleted. Results show that our proposal outperforms previous approaches.https://www.atlantis-press.com/article/25868672.pdfProject portfoliomulti-objective optimizationant-colony meta-heuristicMulti-Criteria Decision
spellingShingle S. Samantha Bastiani
Laura Cruz-Reyes
Eduardo Fernandez
Claudia Gomez
Portfolio Optimization From a Set of Preference Ordered Projects Using an Ant Colony Based Multi-objective Approach
International Journal of Computational Intelligence Systems
Project portfolio
multi-objective optimization
ant-colony meta-heuristic
Multi-Criteria Decision
title Portfolio Optimization From a Set of Preference Ordered Projects Using an Ant Colony Based Multi-objective Approach
title_full Portfolio Optimization From a Set of Preference Ordered Projects Using an Ant Colony Based Multi-objective Approach
title_fullStr Portfolio Optimization From a Set of Preference Ordered Projects Using an Ant Colony Based Multi-objective Approach
title_full_unstemmed Portfolio Optimization From a Set of Preference Ordered Projects Using an Ant Colony Based Multi-objective Approach
title_short Portfolio Optimization From a Set of Preference Ordered Projects Using an Ant Colony Based Multi-objective Approach
title_sort portfolio optimization from a set of preference ordered projects using an ant colony based multi objective approach
topic Project portfolio
multi-objective optimization
ant-colony meta-heuristic
Multi-Criteria Decision
url https://www.atlantis-press.com/article/25868672.pdf
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