Aiding decision makers in articulating a preference closeness model through compensatory fuzzy logic for many-objective optimization problems

One of the main challenges in applying preference-based approaches to many-objective optimization problems is that decision makers (DMs) initially have only a vague notion of the solution they want and can obtain. In this paper, we propose an interactive approach that aids DMs in articulating a preference model in a progressive way. The quality of a solution is determined in terms of its “preference closeness” to an aspiration point, which is a subjective concept that can be outlined by the DM. Our proposal is based on compensatory fuzzy logic, which allows for the construction of predicates that are expressed in language that is close to natural. One main advantage is that the model can be optimized via metaheuristics, and we utilize an ant colony optimization algorithm for this. Our model complies with the principles of hybrid augmented intelligence, not only because the algorithm is enriched with knowledge from the DM, but also because the DM also learns the concept of “preference closeness” throughout the process. The proposed model is validated on benchmarks with five and 10 objective functions, and is compared with two state-of-the-art algorithms. Our approach allows for better convergence to the best compromise solutions. The advantages of our approach are supported by statistical tests of the results.