What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Mathematical optimization
- Algorithm
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.
His most cited work include:
- Ant Colony Optimization (6551 citations)
- MAX-MIN Ant system (2218 citations)
- Ant colony optimization: artificial ants as a computational intelligence technique (1497 citations)
What are the main themes of his work throughout his whole career to date?
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.
He most often published in these fields:
- Mathematical optimization (50.22%)
- Local search (36.22%)
- Algorithm (29.33%)
What were the highlights of his more recent work (between 2016-2021)?
- Mathematical optimization (50.22%)
- Metaheuristic (28.89%)
- Algorithm (29.33%)
In recent papers he was focusing on the following fields of study:
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
- Algorithm design which is related to area like Industrial engineering and Scheduling,
- Continuous optimization and related Selection.
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.
Between 2016 and 2021, his most popular works were:
- An iterated greedy algorithm with optimization of partial solutions for the makespan permutation flowshop problem (43 citations)
- Stochastic Local Search Algorithms for Multiobjective Combinatorial Optimization: A Review (40 citations)
- Ant Colony Optimization: A Component-Wise Overview (28 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Mathematical optimization
- Algorithm
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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