Machine learning at the service of meta-heuristics for solving combinatorial optimization problems: a state-of-the-art
From MaRDI portal
Publication:2242290
DOI10.1016/j.ejor.2021.04.032zbMath1490.90250OpenAlexW3159134414WikidataQ124838388 ScholiaQ124838388MaRDI QIDQ2242290
Mehrdad Mohammadi, Patrick Meyer, Maryam Karimi-Mamaghan, El-Ghazali Talbi, Amir Mohammad Karimi-Mamaghan
Publication date: 9 November 2021
Published in: European Journal of Operational Research (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.ejor.2021.04.032
Learning and adaptive systems in artificial intelligence (68T05) Approximation methods and heuristics in mathematical programming (90C59) Combinatorial optimization (90C27)
Related Items (13)
Boosting ant colony optimization via solution prediction and machine learning ⋮ Towards a machine learning-aided metaheuristic framework for a production/distribution system design problem ⋮ Multi-objective energy-efficient hybrid flow shop scheduling using Q-learning and GVNS driven NSGA-II ⋮ A general VNS for the multi-depot open vehicle routing problem with time windows ⋮ Metaheuristics for bilevel optimization: a comprehensive review ⋮ Multi-constructor CMSA for the maximum disjoint dominating sets problem ⋮ Multi-armed bandit-based hyper-heuristics for combinatorial optimization problems ⋮ Ils-based algorithms for the profit maximizing uncapacitated hub network design problem with multiple allocation ⋮ The stochastic pseudo-star degree centrality problem ⋮ A systematic review of multi-objective hybrid flow shop scheduling ⋮ Learn to decompose multiobjective optimization models for large‐scale networks ⋮ Unnamed Item ⋮ Learning to select operators in meta-heuristics: an integration of Q-learning into the iterated greedy algorithm for the permutation flowshop scheduling problem
Uses Software
Cites Work
- Optimization by Simulated Annealing
- ParaDisEO
- ParEGO
- An automatic algorithm selection approach for the multi-mode resource-constrained project scheduling problem
- The third comprehensive survey on scheduling problems with setup times/costs
- Thirty years of heterogeneous vehicle routing
- A multi-agent based cooperative approach to scheduling and routing
- A hybrid data mining GRASP with path-relinking
- Towards objective measures of algorithm performance across instance space
- Water wave optimization: a new nature-inspired metaheuristic
- A hybrid differential evolution approach based on surrogate modelling for scheduling bottleneck stages
- Learnheuristics: hybridizing metaheuristics with machine learning for optimization with dynamic inputs
- Coalition-based metaheuristic: a self-adaptive metaheuristic using reinforcement learning and mimetism
- Knowledge incorporation in evolutionary computation
- Solving the short-term electrical generation scheduling problem by an adaptive evolutionary approach
- Discovering dispatching rules using data mining
- Case-based heuristic selection for timetabling problems
- A cross entropy-lagrangean hybrid algorithm for the multi-item capacitated lot-sizing problem with setup times
- How easy is local search?
- New optimization heuristics. The great deluge algorithm and the record- to-record travel
- The tabu search metaheuristic: How we used it
- Variable neighborhood search
- Differential evolution -- a simple and efficient heuristic for global optimization over continuous spaces
- Evolution of appropriate crossover and mutation operators in a genetic process
- Learnable evolution model: Evolutionary processes guided by machine learning
- A machine-learning based memetic algorithm for the multi-objective permutation flowshop scheduling problem
- Ant colony optimization with clustering for solving the dynamic location routing problem
- What makes a VRP solution good? The generation of problem-specific knowledge for heuristics
- A hybrid breakout local search and reinforcement learning approach to the vertex separator problem
- Measuring instance difficulty for combinatorial optimization problems
- Greedy randomized adaptive search procedures
- Guided local search and its application to the traveling salesman problem
- Metaheuristics: A bibliography
- Synergies between operations research and data mining: the emerging use of multi-objective approaches
- Machine learning for combinatorial optimization: a methodological tour d'horizon
- A learning-based algorithm to quickly compute good primal solutions for stochastic integer programs
- Recent advances in selection hyper-heuristics
- A learning-based metaheuristic for a multi-objective agile inspection planning model under uncertainty
- A novel feature-based approach to characterize algorithm performance for the traveling salesperson problem
- Stochastic mixed-model assembly line sequencing problem: mathematical modeling and Q-learning based simulated annealing hyper-heuristics
- On the use of fitness landscape features in meta-learning based algorithm selection for the quadratic assignment problem
- Design of a reliable logistics network with hub disruption under uncertainty
- Selecting algorithms for large berth allocation problems
- Benchmarks for basic scheduling problems
- Learning variable neighborhood search for a scheduling problem with time windows and rejections
- Hybridization of GRASP metaheuristic with data mining techniques
- Ant colony optimization theory: a survey
- A systematic study on meta-heuristic approaches for solving the graph coloring problem
- Parallel Hybrid Metaheuristics
- Performance Prediction and Automated Tuning of Randomized and Parametric Algorithms
- TSPLIB—A Traveling Salesman Problem Library
- Guided Local Search
- Big Data Optimization: Recent Developments and Challenges
- Algorithm Selection for Combinatorial Search Problems: A Survey
- Equation of State Calculations by Fast Computing Machines
- GPU Computing for Parallel Local Search Metaheuristic Algorithms
- Parallel Metaheuristics
- Evolutionary Multi-Criterion Optimization
- CONTINUOUS INSPECTION SCHEMES
- Combinatorial optimization. Theory and algorithms.
- Handbook of metaheuristics
- Combining metaheuristics with mathematical programming, constraint programming and machine learning
- Finite-time analysis of the multiarmed bandit problem
- Unnamed Item
- Unnamed Item
- Unnamed Item
This page was built for publication: Machine learning at the service of meta-heuristics for solving combinatorial optimization problems: a state-of-the-art
