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

TL;DR

This paper proposes a method to improve decision space diversity in multi-objective evolutionary algorithms for the diet problem by integrating a Hamming distance-based measure, leading to more diverse solutions without sacrificing objective performance.

Contribution

It introduces a novel approach that incorporates decision space diversity directly into MOEA selection, enhancing solution variety in complex optimization problems.

Findings

Significantly improves decision space diversity over NSGA-II.

Maintains comparable objective space performance.

Provides a generalizable strategy for decision space awareness.

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.