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

Abstract

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.

Design and Solution of a Surrogate Model for Portfolio Optimization Based on Project Ranking

“…The works of [16][17][18] are similar to Scoring and Ranking methods [10] or additive functions [12,19] in that they prioritize projects according to a certain utility function to measure their importance. Alternatively, the use of proxy variables [9,11,14] has offered versatile and satisfactory results that extend the information derived from a ranking of projects. However, none of those approaches offer a strategy that incorporates the DM's preferences, the key element in the present work that guides the construction of better solutions.…”

“…This paper is organized into six sections. Section 2 presents a criticism of previous related approaches and describes the many-objective optimization model proposed by Bastiani et al [9] for PSPSOP. Section 3 details the decision-support mechanisms used for the PSPSOP; this mechanism is based on DM preferences.…”

“…For example, recently, the Portfolio Selection Problem on a Set of Ordered Projects, or PSPSOP, has been reported to focus on the construction of a portfolio from a set of projects subject to a limited budget (cf. Bastiani et al [9]). This case also involves a distinctive feature, which is that the only information available about the projects is their rank; that is, they are ordered according to the decision maker's (DM) preferences.…”

“…Bastiani et al [9] propose a method to solve PSPSOP based on the cardinality and the discrepancies present in a portfolio, where cardinality refers to the total number of projects involved in it, and the term discrepancy is a concept that reflects the negative effect that is applied over the DM's thinking because one of the projects, when it is compared against others, seems to have merits that belong to the portfolio but it is not in it. The strategy balances the priorities and the number of projects in the final portfolio through a model that minimizes discrepancies concerning the ranking and costs and maximizes its cardinality, all of which are defined in ten objectives.…”

A Multi-objective Evolutionary Algorithm with Dynamic Topology and its Application to Network-Wide Flight Trajectory Planning

“…Because of the conflicting objectives and complicated decision space, it is commonly impossible to find a single optimal solution for such multi-objective problems, but rather a set of solutions known as the Pareto front. Evolutionary algorithms (EAs), being stochastic population-based search methods that simulate the process of evolution, are recognized to be suitable for multi-objective optimization problems (MOP) with the idea of reproduction, recombination, and selection [1][2][3] . For multi-objective evolutionary algorithms (MOEAs), the optimality of solutions should be a minimization of the distance between the solution set and the true Pareto front, while a thorough exploration of the search space should be guaranteed by finding a set of solutions as diverse as possible in the objective space.…”