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

2025-07-02

AI Summary

This study investigates how landscape characteristics of multi-objective combinatorial optimization problems influence algorithm performance prediction. We propose a feature extraction framework based on the Compressed Pareto Local Optima Subnetwork (C-PLOS-net) to systematically quantify key terrain properties—such as ruggedness and objective correlation—in rmnk-landscapes, and evaluate PLS, GSEMO, and NSGA-II across diverse landscapes using resolution and hypervolume metrics. Our key contribution is a novel algorithm–landscape co-analysis paradigm: for each algorithm, we identify a tailored set of landscape features whose importance dynamically varies with the number of objectives and landscape complexity. Empirical results reveal intrinsic “algorithm–landscape” matching mechanisms, offering interpretable, landscape-driven guidance for algorithm selection and design in multi-objective optimization.

Technology Category

• Search and Optimization: Combinatorial Optimization
• Constraint Satisfaction and Optimization: Constraint Optimization
• Machine Learning: Optimization

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Web Mining and Content Analysis: Web data quality in the era of algorithmically-generated content

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.

Problem

• Analyzing landscape features for multi-objective optimization performance prediction
• Evaluating C-PLOS-net features on rmnk-landscapes with varying objectives
• Identifying feature combinations affecting specific algorithm and landscape performance

Innovation

• Uses C-PLOS-net for landscape feature analysis
• Analyzes rmnk-landscapes with multiple objectives
• Tailors feature combinations to specific algorithms