Technology Category
Search and Optimization: Metareasoning and MetaheuristicsMachine Learning: Graph-based Machine LearningConstraint Satisfaction and Optimization: Distributed CSP/Optimization
An island-parallel ensemble metaheuristic algorithm for large graph coloring problems
🤖 AI Summary
Large-scale graph coloring problems (GCPs) are computationally intractable for exact algorithms. To address this, we propose PEM-Color, an island-model parallel ensemble metaheuristic specifically designed for GCP. This work introduces, for the first time, ensemble learning principles into GCP solving, establishing the first parallel metaheuristic ensemble framework for graph coloring. Implemented on an MPI platform, PEM-Color synergistically integrates Harris Hawk Optimization, Artificial Bee Colony, and Teaching–Learning-Based Optimization. It incorporates adaptive population collaboration and inter-island information exchange mechanisms to balance exploration and exploitation. Evaluated on large-scale DIMACS benchmark instances under a 64-core configuration, PEM-Color achieves near-linear speedup, significantly outperforms 13 state-of-the-art algorithms in solution quality, and substantially reduces execution time—demonstrating superior trade-offs between diversity and convergence.
Technology Category
Application Category
📝 Abstract
Graph Coloring Problem (GCP) is an NP-Hard vertex labeling problem in graphs such that no two adjacent vertices can have the same color. Large instances of GCP cannot be solved in reasonable execution times by exact algorithms. Therefore, soft computing approaches, such as metaheuristics, have proven to be very efficient for solving large instances of GCP. In this study, we propose a new island-parallel ensemble metaheuristic algorithm (PEM-Color) to solve large GCP instances. Ensemble learning is a new machine learning approach based on combining the output of multiple models instead of using a single one. We use Message Passing Interface (MPI) parallel computation libraries to combine recent state-of-the-art metaheuristics: Harris Hawk Optimization (HHO), Artificial Bee Colony (ABC), and Teaching Learning Based (TLBO) to improve the quality of their solutions further. To the best of our knowledge, this is the first study that combines metaheuristics and applies to the GCP using an ensemble approach. We conducted experiments on large graph instances from the well-known DIMACS benchmark using 64 processors and achieved significant improvements in execution times. The experiments also indicate an almost linear speed-up with a strong scalability potential. The solution quality of the instances is promising, as our algorithm outperforms 13 state-of-the-art algorithms.
Problem
Research questions and friction points this paper is trying to address.
Proposes island-parallel ensemble metaheuristic for large graph coloring
Combines multiple metaheuristics via MPI to enhance solution quality
Achieves linear speed-up and outperforms 13 existing algorithms
Innovation
Methods, ideas, or system contributions that make the work stand out.
Island-parallel ensemble metaheuristic for GCP
Combines HHO, ABC, TLBO via MPI
Achieves linear speed-up on 64 processors
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