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

Author

Listed:
  • Karl Doerner
  • Walter Gutjahr
  • Richard Hartl
  • Christine Strauss
  • Christian Stummer

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. Copyright Kluwer Academic Publishers 2004

Suggested Citation

  • Karl Doerner & Walter Gutjahr & Richard Hartl & Christine Strauss & Christian Stummer, 2004. "Pareto Ant Colony Optimization: A Metaheuristic Approach to Multiobjective Portfolio Selection," Annals of Operations Research, Springer, vol. 131(1), pages 79-99, October.
    • Handle: RePEc:spr:annopr:v:131:y:2004:i:1:p:79-99:10.1023/b:anor.0000039513.99038.c6
    DOI: 10.1023/B:ANOR.0000039513.99038.c6
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