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 variab...

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Published in:	Evolutionary intelligence Vol. 17; no. 5-6; pp. 3767 - 3791
Main Authors:	de Sousa, Mateus Clemente, Meneghini, Ivan Reinaldo, Guimarães, Frederico Gadelha
Format:	Journal Article
Language:	English
Published:	Berlin/Heidelberg Springer Berlin Heidelberg 01.10.2024 Springer Nature B.V
Subjects:	Applications of Mathematics Artificial Intelligence Bioinformatics Clustering Control Decision making Engineering Evolutionary algorithms Function generators Genetic algorithms Mathematical and Computational Engineering Mechatronics Multiple objective analysis Noise levels Optimization Pareto optimum Research Paper Robotics Robustness (mathematics) Statistical Physics and Dynamical Systems Uncertainty Variables Multi-objective optimization Robust optimization Noisy optimization Clustering Evolutionary algorithm
ISSN:	1864-5909, 1864-5917
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Abstract	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.
AbstractList	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.
Author	de Sousa, Mateus Clemente Meneghini, Ivan Reinaldo Guimarães, Frederico Gadelha
Author_xml	– sequence: 1 givenname: Mateus Clemente surname: de Sousa fullname: de Sousa, Mateus Clemente email: mateus.clemente@ifmg.edu.br organization: Department of Engineering and Computing, Instituto Federal de Educação, Ciência e Tecnologia de Minas Gerais, Machine Intelligence and Data Science - MINDS Lab – sequence: 2 givenname: Ivan Reinaldo surname: Meneghini fullname: Meneghini, Ivan Reinaldo organization: Instituto Federal de Educação, Ciência e Tecnologia de Minas Gerais, Machine Intelligence and Data Science - MINDS Lab – sequence: 3 givenname: Frederico Gadelha surname: Guimarães fullname: Guimarães, Frederico Gadelha organization: Department of Computer Science, Universidade Federal de Minas Gerais, Machine Intelligence and Data Science - MINDS Lab
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SubjectTerms	Applications of Mathematics Artificial Intelligence Bioinformatics Clustering Control Decision making Engineering Evolutionary algorithms Function generators Genetic algorithms Mathematical and Computational Engineering Mechatronics Multiple objective analysis Noise levels Optimization Pareto optimum Research Paper Robotics Robustness (mathematics) Statistical Physics and Dynamical Systems Uncertainty Variables
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Title	A clustering-based coevolutionary multi-objective evolutionary algorithm for handling robust and noisy optimization
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