Preference incorporation into many-objective optimization: An Ant colony algorithm based on interval outranking

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