A clustering-based coevolutionary multi-objective evolutionary algorithm for handling robust and noisy optimization

The presence of uncertainty is commonplace in real-world scenarios. Uncertainties can be present in both the objective space and the decision space in optimization problems. These uncertainties can pose significant challenges for evolutionary algorithms. For example, perturbations in decision variables (Robust Optimization) and noise in objective functions (Noisy Optimization). Despite the plethora of methods proposed for Robust or Noisy Optimization, addressing both forms of uncertainty concurrently remains an open research question. We introduce a novel approach based on TEDA-CMOEA/D, augmented with clustering techniques for descendant generation in Robust and Noisy Optimization problems. Notably, the proposed algorithm yields promising results for uncertainty simultaneously sans the requirement for sampling, thereby reducing computational complexity. We leverage an extension of an existing test function generator for Multi-Objective Optimization of the tests. The benchmark integrates uncertainties in decision variables and/or objective functions. Experimental evaluations encompassed varying noise intensities, elucidating the impact of different noise levels on algorithmic performance. The results demonstrate the superior performance of the proposed approach compared to existing algorithms, specifically RNSGA-II and CRMOEA/D. The proposed algorithm emerges as a promising solution for Robust and Noisy Multi-Objective Optimization problems.