Customized Exploration of Landscape Features Driving Multi-Objective Combinatorial Optimization Performance

TL;DR

This paper analyzes landscape features from C-PLOS-net models to predict the performance of multi-objective combinatorial optimization algorithms on rmnk-landscapes, revealing feature combinations that influence algorithm success.

Contribution

It introduces a landscape feature analysis approach tailored to specific rmnk-landscapes and algorithms, enhancing understanding of performance predictors.

Findings

Identifies feature combinations that predict algorithm performance.

Provides insights into landscape features affecting optimization success.

Analyzes three algorithms across various landscape ruggedness levels.

Abstract

We present an analysis of landscape features for predicting the performance of multi-objective combinatorial optimization algorithms. We consider features from the recently proposed compressed Pareto Local Optimal Solutions Networks (C-PLOS-net) model of combinatorial landscapes. The benchmark instances are a set of rmnk-landscapes with 2 and 3 objectives and various levels of ruggedness and objective correlation. We consider the performance of three algorithms -- Pareto Local Search (PLS), Global Simple EMO Optimizer (GSEMO), and Non-dominated Sorting Genetic Algorithm (NSGA-II) - using the resolution and hypervolume metrics. Our tailored analysis reveals feature combinations that influence algorithm performance specific to certain landscapes. This study provides deeper insights into feature importance, tailored to specific rmnk-landscapes and algorithms.