|
Download PDFOpen PDF in browserAn Online Learning-based Metaheuristic for Solving Combinatorial Optimization ProblemsEasyChair Preprint 27662 pages•Date: February 26, 2020AbstractCombinatorial optimization problems (COPs) are a complex class of optimization problems with discrete decision variables and finite search space. They have a wide application in many real-world problems, including transportation, scheduling, network design, assignment, and so on. Many COPs belong to the NP-Hard class of problems, which require exponential time to be solved to optimality. For these problems, metaheuristics (MHs) provide acceptable solutions in reasonable computation times, and are often good substitutes for exact algorithms. Machine learning (ML) techniques are also good approaches for solving COPs. In this regard, the hybridization of ML techniques with MHs is an emerging research field that has attracted numerous researchers in recent years. ML techniques can be used to improve the performance of MHs, particularly for solving complex COPs. In the hybrid framework, ML techniques are used to extract knowledge from available data, and inject it into MHs, with the aim of reducing computational time, and improving solutions quality. The goal of this contribution is twofold: 1) Proposing a state of the art review on hybridization methods between MHs and ML; and 2) Introducing the concept of a novel approach focused on online learning in population-based MHs. Keyphrases: Hybridization, Metaheuristics, combinatorial optimization, machine learning Download PDFOpen PDF in browser |
|
|