2022 - Research.com Computer Science in Belgium Leader Award
His primary scientific interests are in Mathematical optimization, Local search, Ant colony optimization algorithms, Metaheuristic and Algorithm. His research integrates issues of Evolutionary algorithm, Tabu search, Heuristic and Benchmark in his study of Local search. His Ant colony optimization algorithms study integrates concerns from other disciplines, such as Swarm intelligence and Travelling salesman problem.
His biological study spans a wide range of topics, including Optimization problem, Artificial Ants and Combinatorial optimization. His Algorithm research is multidisciplinary, incorporating elements of Stochastic programming, Task and Job shop scheduling. His study in Artificial intelligence is interdisciplinary in nature, drawing from both Machine learning and Flexibility.
Thomas Stützle focuses on Mathematical optimization, Local search, Algorithm, Metaheuristic and Artificial intelligence. His Ant colony optimization algorithms, Travelling salesman problem, Quadratic assignment problem, Combinatorial optimization and Tabu search investigations are all subjects of Mathematical optimization research. His research investigates the connection with Local search and areas like Heuristic which intersect with concerns in Heuristic.
His work focuses on many connections between Algorithm and other disciplines, such as Evolutionary algorithm, that overlap with his field of interest in Multi-objective optimization. His work carried out in the field of Metaheuristic brings together such families of science as Optimization problem and Continuous optimization. The various areas that Thomas Stützle examines in his Artificial intelligence study include Swarm intelligence and Machine learning.
Thomas Stützle spends much of his time researching Mathematical optimization, Metaheuristic, Algorithm, Artificial intelligence and Local search. His research investigates the link between Mathematical optimization and topics such as Benchmark that cross with problems in Artificial bee colony algorithm and Task. His Metaheuristic research also works with subjects such as
His Artificial intelligence research is multidisciplinary, relying on both Swarm intelligence and Machine learning, Surrogate model. His research in Local search intersects with topics in Data science, Tardiness, Job shop scheduling and Distributed computing. His work deals with themes such as Optimization algorithm, Travelling salesman problem, Quadratic assignment problem and Discrete optimization, which intersect with Ant colony optimization algorithms.
Mathematical optimization, Metaheuristic, Local search, Artificial intelligence and Multi-objective optimization are his primary areas of study. Particularly relevant to Combinatorial optimization is his body of work in Mathematical optimization. His Metaheuristic research incorporates elements of Algorithm design, Quadratic assignment problem, Decision problem and Operations research.
The study incorporates disciplines such as Travelling salesman problem, Ant colony optimization algorithms and Component in addition to Quadratic assignment problem. His research on Local search concerns the broader Algorithm. His Artificial intelligence study combines topics from a wide range of disciplines, such as Machine learning and Algorithm configuration.
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Ant colony optimization: artificial ants as a computational intelligence technique
Marco Dorigo;Mauro Birattari;Thomas Stützle.
IEEE Computational Intelligence Magazine (2006)
Ant Colony Optimization
M. Dorigo;M. Birattari;T. Stutzle.
MAX-MIN Ant system
Thomas Stützle;Holger H. Hoos.
Future Generation Computer Systems (2000)
Iterated local search
Helena Ramalhino Lourenco;Olivier Martin;Thomas Stützle.
Science Kluwer (2003)
The irace package: Iterated racing for automatic algorithm configuration
Manuel López-Ibáñez;Jérémie Dubois-Lacoste;Leslie Pérez Cáceres;Mauro Birattari.
Operations Research Perspectives (2016)
The Ant Colony Optimization Metaheuristic: Algorithms, Applications, and Advances
Marco Dorigo;Thomas Stützle.
MAX-MIN Ant System and local search for the traveling salesman problem
T. Stutzle;H. Hoos.
ieee international conference on evolutionary computation (1997)
Empirical Scoring Functions for Advanced Protein−Ligand Docking with PLANTS
Oliver Korb;Thomas Stützle;Thomas E. Exner.
Journal of Chemical Information and Modeling (2009)
A SIMPLE AND EFFECTIVE ITERATED GREEDY ALGORITHM FOR THE PERMUTATION FLOWSHOP SCHEDULING PROBLEM
Rubén Ruiz;Thomas Stützle.
European Journal of Operational Research (2007)
Improvements on the Ant-System: Introducing the MAX-MIN Ant System
Thomas Stützle;Holger H. Hoos.
international conference on artificial neural networks (1998)
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