Preference Incorporation into Many-Objective Optimization: An Outranking-based Ant Colony Algorithm

TL;DR

This paper introduces IO-ACO, a novel ant colony optimization algorithm that incorporates interval outranking to effectively handle preferences in many-objective problems, focusing search on the decision maker's region of interest.

Contribution

The paper presents the first ant colony optimizer embedding an outranking model to address vagueness in decision maker preferences for many-objective optimization.

Findings

IO-ACO better approximates the Region of Interest than existing methods.

IO-ACO outperforms leading metaheuristics on benchmark problems.

The method effectively incorporates decision maker preferences into the optimization process.

Abstract

In this paper, we enriched Ant Colony Optimization (ACO) with interval outranking to develop a novel multiobjective ACO optimizer to approach problems with many objective functions. This proposal is suitable if the preferences of the Decision Maker (DM) can be modeled through outranking relations. The introduced algorithm (named Interval Outranking-based ACO, IO-ACO) is the first ant-colony optimizer that embeds an outranking model to bear vagueness and ill-definition of DM preferences. This capacity is the most differentiating feature of IO-ACO because this issue is highly relevant in practice. IO-ACO biases the search towards the Region of Interest (RoI), the privileged zone of the Pareto frontier containing the solutions that better match the DM preferences. Two widely studied benchmarks were utilized to measure the efficiency of IO-ACO, i.e., the DTLZ and WFG test suites. Accordingly, IO-ACO was compared with two competitive many-objective optimizers: The Indicator-based Many-Objective ACO and the Multiobjective Evolutionary Algorithm Based on Decomposition. The numerical results show that IO-ACO approximates the Region of Interest (RoI) better than the leading metaheuristics based on approximating the Pareto frontier alone.