Machine learning at the service of meta-heuristics for solving combinatorial optimization problems: A state-of-the-art
In recent years, there has been a growing research interest in integrating machine learning
techniques into meta-heuristics for solving combinatorial optimization problems. This …
techniques into meta-heuristics for solving combinatorial optimization problems. This …
Learning to select operators in meta-heuristics: An integration of Q-learning into the iterated greedy algorithm for the permutation flowshop scheduling problem
M Karimi-Mamaghan, M Mohammadi… - European Journal of …, 2023 - Elsevier
This paper aims at integrating machine learning techniques into meta-heuristics for solving
combinatorial optimization problems. Specifically, our study develops a novel efficient …
combinatorial optimization problems. Specifically, our study develops a novel efficient …
Hybrid algorithms based on combining reinforcement learning and metaheuristic methods to solve global optimization problems
A Seyyedabbasi, R Aliyev, F Kiani, MU Gulle… - Knowledge-Based …, 2021 - Elsevier
This paper introduces three hybrid algorithms that help in solving global optimization
problems using reinforcement learning along with metaheuristic methods. Using the …
problems using reinforcement learning along with metaheuristic methods. Using the …
Semiconductor final testing scheduling using Q-learning based hyper-heuristic
J Lin, YY Li, HB Song - Expert Systems with Applications, 2022 - Elsevier
Semiconductor final testing scheduling problem (SFTSP) has extensively been studied in
advanced manufacturing and intelligent scheduling fields. This paper presents a Q-learning …
advanced manufacturing and intelligent scheduling fields. This paper presents a Q-learning …
A reinforcement learning-variable neighborhood search method for the capacitated vehicle routing problem
Finding the best sequence of local search operators that yields the optimal performance of
Variable Neighborhood Search (VNS) is an important open research question in the field of …
Variable Neighborhood Search (VNS) is an important open research question in the field of …
Learning-driven feasible and infeasible tabu search for airport gate assignment
M Li, JK Hao, Q Wu - European Journal of Operational Research, 2022 - Elsevier
The gate assignment problem (GAP) is an important task in airport management. This study
investigates an original probability learning based heuristic algorithm for solving the …
investigates an original probability learning based heuristic algorithm for solving the …
Automated algorithm design using proximal policy optimisation with identified features
Automated algorithm design is attracting considerable recent research attention in solving
complex combinatorial optimisation problems, due to that most metaheuristics may be …
complex combinatorial optimisation problems, due to that most metaheuristics may be …
A learning automata-based multiobjective hyper-heuristic
Metaheuristics, being tailored to each particular domain by experts, have been successfully
applied to many computationally hard optimization problems. However, once implemented …
applied to many computationally hard optimization problems. However, once implemented …
MAB-OS: multi-armed bandits metaheuristic optimizer selection
Metaheuristic algorithms are derivative-free optimizers designed to estimate the global
optima for optimization problems. Keeping balance between exploitation and exploration …
optima for optimization problems. Keeping balance between exploitation and exploration …
[HTML][HTML] An adaptive parallel evolutionary algorithm for solving the uncapacitated facility location problem
Metaheuristics, providing high level guidelines for heuristic optimisation, have successfully
been applied to many complex problems over the past decades. However, their …
been applied to many complex problems over the past decades. However, their …