Regionalized Metric Framework: A Novel Approach for Evaluating Multimodal Multi-Objective Optimization Algorithms

TL;DR

This paper introduces a Regionalized Metric Framework for evaluating multimodal multi-objective optimization algorithms, reducing reference set dependence and providing more accurate assessments of solution quality.

Contribution

It proposes a novel evaluation metric dividing solutions into regions with tailored scoring functions, enhancing evaluation accuracy and independence from reference sets.

Findings

Comparable trend with existing metrics

Accurately assesses solutions equidistant from reference set

Provides a new perspective for evaluation metric research

Abstract

This study aims to optimize the evaluation metric of multimodal multi-objective optimization problems using a Regionalized Metric Framework, which provides a certain boost to research in this field. Existing evaluation metrics usually use the reference set as the evaluation basis, which inevitably leads to reference set dependence. To optimize this problem, this study proposes an evaluation metric based on a Regionalized Metric Framework. The algorithm divides the set of solutions to be evaluated into three regions, and evaluates each solution according to a unique scoring function for each region, which is combined to form the evaluation value of the solution set. To verify the feasibility of this method, a comparative experiment was conducted in this study. The results of the experiment are roughly the same as the trend of existing indicators, and at the same time, it can accurately judge the advantages and disadvantages of points equidistant from the reference set. Our method provides a new perspective for further research on evaluation metrics for multimodal multi-objective optimization algorithms.