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Pareto Ant Colony Optimization: A Metaheuristic Approach to Multiobjective Portfolio Selection

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Abstract

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 contraints, 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.

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Authors and Affiliations

  1. Department of Management Science, University of Vienna, Bruenner Str, 72, A-1210, Vienna, Austria

    Karl Doerner

  2. Department of Statistics and Decision Support Systems, University of Vienna, Universitaetsstr. 5/3, A-1010, Vienna, Austria

    Walter J. Gutjahr

  3. Department of Management Science, University of Vienna, Bruenner Str, 72, A-1210, Vienna, Austria

    Richard F. Hartl

  4. Department of Management Science, University of Vienna, Bruenner Str, 72, A-1210, Vienna, Austria

    Christine Strauss

  5. Department of Management Science, University of Vienna, Bruenner Str, 72, A-1210, Vienna, Austria

    Christian Stummer

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  1. Karl Doerner
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Doerner, K., Gutjahr, W.J., Hartl, R.F. et al. Pareto Ant Colony Optimization: A Metaheuristic Approach to Multiobjective Portfolio Selection. Ann Oper Res 131, 79–99 (2004). https://doi.org/10.1023/B:ANOR.0000039513.99038.c6

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