Machine Learning-Assisted Constraint Handling Under Variable Uncertainty for Preference-based Multi-Objective Optimization
Abstract
Evolutionary Multi-objective Optimization (EMO) algorithms are widely used to solve real-world multi-objective optimization problems, aiming to obtain a set of non-dominated solutions close to the Pareto front. However, most EMO methods assume deterministic decision variables, ignoring inherent uncertainties in engineering applications, which can lead to design failures, especially in reliability-based designs. Reliability-based Multi-objective Optimization (ReMOO) addresses this issue by incorporating variable uncertainty and probabilistic constraints to generate a Reliable Front. ReMOO operates using a bi-level framework: the outer level optimizes objective functions, while the inner level estimates reliability through computationally intensive methods, like Monte Carlo Simulation (MCS) or the Performance Measure Approach (PMA). Additionally, decision-makers (DMs) often select only a subset of reliable solutions, limiting computational efficiency. To overcome these challenges, this paper proposes a Machine Learning-assisted reliability-based Multi-Criteria Decision-Making (ML-ReMCDM) technique. ML models are trained on reliability-based constraints within the decision space before an EMO execution. In the inner loop, ML models predict probabilistic constraints and reliability indices, significantly reducing computational costs. Moreover, the outer loop computes only the DM-preferred segment of the reliable front, further enhancing efficiency. The ML-ReMCDM approach, implemented on several benchmark and real-world examples, demonstrates substantial improvements in computational efficiency as well as practical applicability.
Authors: Deepanshu Yadav, Palaniappan Ramu, Kalyanmoy Deb
Published in: Genetic and Evolutionary Computation Conference (GECCO) (2025)