Detection of Hidden Objectives and Interactive Objective Reduction
Seyed Mahdi Shavarani, Manuel L\'opez-Ib\'a\~nez, Richard Allmendinger

TL;DR
This paper introduces a preference-aware, interactive method for detecting and reducing irrelevant objectives in multi-objective optimization, improving efficiency and solution quality by leveraging decision-maker preferences.
Contribution
It presents a novel, computationally efficient approach using univariate feature selection to identify and replace irrelevant objectives during optimization, integrating seamlessly with existing interactive EMOAs.
Findings
Reduces computational cost and objective evaluations.
Improves quality of final solutions.
Effectively detects hidden and irrelevant objectives.
Abstract
In multi-objective optimization problems, there might exist hidden objectives that are important to the decision-maker but are not being optimized. On the other hand, there might also exist irrelevant objectives that are being optimized but are of less importance to the DM. The question that arises here is whether it is possible to detect and reduce irrelevant objectives without deteriorating the quality of the final results? In fact, when dealing with multi-objective problems, each objective implies a significant cost best avoided if possible. However, existing methods that pertain to the reduction of objectives are computationally intensive and ignore the preferences of the decision-maker. In this paper, we propose an approach to exploit the capabilities of interactive evolutionary multi-objective optimization algorithms (EMOAs) and the preference information provided by the…
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Taxonomy
TopicsAdvanced Multi-Objective Optimization Algorithms · Metaheuristic Optimization Algorithms Research · Evolutionary Algorithms and Applications
