Ant Colony Optimization in Multiobjective Portfolio Selection

Abstract

Multiobjective decision-making and combinatorial optimization have been studied extensively over the past few decades (cf. [16], and [4] for bibliographies). Both fields play a decisive role in multiobjective combinatorial optimization, for which the class of (multiobjective) portfolio selection is of particularly high practical relevance (cf. [10] for a survey). Research and development (R&D) management provides an especially useful example: when large amounts of resources (see [14]) and, more importantly, a product's long-term commercial success are at stake it is crucial for a firm to determine the "best" subset of R&D projects out of dozens of proposals. Support for making multiple objectives decisions is based on either (i) approaches aggregating different types of benefits (e.g., cash flow, sales or even intangibles such as image) in a unique overall objective function or (ii) approaches that (partially) determine the efficient (i. e., Pareto-optimal) portfolio candid