Work

Enhancing Decision Space Diversity in Multi -Objective Evolutionary Optimization for the Diet Problem

Year: 2025

Type: conference-paper

Abstract: Multi-objective evolutionary algorithms (MOEAs) are essential for solving complex optimization problems, such as the diet problem, where balancing conflicting objectives, like cost and nutritional content, is crucial. However, most MOEAs focus on optimizing solutions in the objective space, often neglecting the diversity of solutions in the decision space, which is critical for providing decision-makers with a wide range of choices. This paper introduces an approach that directly integrates a Hamming distance-based measure of uniformity into the selection mechanism of a MOEA to enhance decision space diversity. Experiments on a multi-objective formulation of the diet problem demonstrate that our approach significantly improves decision space diversity compared to NSGA-II, while maintaining comparable objective space performance. The proposed method offers a generalizable strategy for integrating decision space awareness into MOEAs.

DOI: 10.21528/cbic2025-1168382

Authors: Gustavo Nascimento, Ivan Meneghini, Valéria Santos, Eduardo Luz, Gladston Moreira

Institutions: Universidade Federal de Ouro Preto, Instituto Federal de Educação, Ciência e Tecnologia de Minas Gerais

Language: English

Topic: Advanced Multi-Objective Optimization Algorithms

Subfield: Computational Theory and Mathematics

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Funders: Conselho Nacional de Desenvolvimento Científico e Tecnológico, Fundação de Amparo à Pesquisa do Estado de Minas Gerais, Universidade Federal de Ouro Preto

Awards: G2463473590, G6132485245, G7192278928

OpenAlex ID: W4416241096