Pareto Ant Colony Optimization: A metaheuristic approach to multiobjective portfolio selection

Autor(en)

Karl Dörner, Walter Gutjahr, Richard Hartl, Christine Strauss, Christian Stummer

Abstrakt

Selecting the "best" project portfolio out of a given set of investment proposals is a common and often critical management issue. Decision-makers must regularly consider multiple objectives and often have little a priori preference information available to them. Given these constraints, they can improve their chances of achieving success by following a two-phase procedure that first determines the solution space of all efficient (i.e., Pareto-optimal) portfolios and then allows them to interactively explore that space. However, the task of determining the solution space is not trivial: brute-force complete enumeration only works for small instances and the underlying NP-hard problem becomes increasingly demanding as the number of projects grows. Meta-heuristics provide a useful compromise between the amount of computation time necessary and the quality of the approximated solution space. This paper introduces Pareto Ant Colony Optimization as an especially effective meta-heuristic for solving the portfolio selection problem and compares its performance to other heuristic approaches (i.e., Pareto Simulated Annealing and the Non-Dominated Sorting Genetic Algorithm) by means of computational experiments with random instances. Furthermore, we provide a numerical example based on real world data.

Organisation(en)

Institut für Rechnungswesen, Innovation und Strategie, Institut für Statistik und Operations Research

Journal

Annals of Operations Research

Band

131

Seiten

79-99

Anzahl der Seiten

21

ISSN

0254-5330

DOI

https://doi.org/10.1023/B:ANOR.0000039513.99038.c6

Publikationsdatum

2004

Peer-reviewed

Ja

ÖFOS 2012

101015 Operations Research, 502052 Betriebswirtschaftslehre

Link zum Portal

https://ucrisportal.univie.ac.at/de/publications/e34575b0-43c6-4907-ba92-ba72ae5bdb54