What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Operating system
- Programming language
Hartmut Schmeck spends much of his time researching Mathematical optimization, Evolutionary algorithm, Organic computing, Metaheuristic and Ant colony optimization algorithms.
His research in Mathematical optimization focuses on subjects like Artificial intelligence, which are connected to Machine learning.
His research in the fields of Evolutionary programming overlaps with other disciplines such as Parametric programming, Portfolio optimization and Project portfolio management.
He combines subjects such as Control, Complex system and Simulation with his study of Organic computing.
His Metaheuristic research focuses on Multi-swarm optimization and how it relates to Grid computing, Computational science and Computer cluster.
Hartmut Schmeck interconnects Simulated annealing and Tabu search in the investigation of issues within Ant colony optimization algorithms.
His most cited work include:
- Ant colony optimization for resource-constrained project scheduling (609 citations)
- A Multi-population Approach to Dynamic Optimization Problems (279 citations)
- Guidance in evolutionary multi-objective optimization (221 citations)
What are the main themes of his work throughout his whole career to date?
Hartmut Schmeck mainly investigates Organic computing, Distributed computing, Mathematical optimization, Evolutionary algorithm and Smart grid.
Hartmut Schmeck has included themes like Self-organization, Autonomic computing, Control engineering, Control and Software engineering in his Organic computing study.
Distributed computing and Simulation are frequently intertwined in his study.
Mathematical optimization connects with themes related to Set in his study.
The concepts of his Smart grid study are interwoven with issues in Distributed generation, Renewable energy, Energy management, Embedded system and Flexibility.
His Renewable energy study which covers Energy management system that intersects with Management system.
He most often published in these fields:
- Organic computing (16.04%)
- Distributed computing (17.54%)
- Mathematical optimization (16.42%)
What were the highlights of his more recent work (between 2015-2020)?
- Building automation (9.33%)
- Smart grid (11.57%)
- Renewable energy (10.07%)
In recent papers he was focusing on the following fields of study:
His primary areas of investigation include Building automation, Smart grid, Renewable energy, Flexibility and Mathematical optimization.
His research in Building automation intersects with topics in Systems engineering, Reliability engineering, Embedded system and Energy management.
His Smart grid research is multidisciplinary, incorporating perspectives in Distributed generation and Co-simulation.
His Renewable energy research includes elements of Electricity system, Risk analysis, Evolutionary algorithm, Environmental economics and Provisioning.
His study in Evolutionary algorithm is interdisciplinary in nature, drawing from both Multi-objective optimization, Management system and Preference.
His work on Fitness function as part of general Mathematical optimization research is frequently linked to General problem, thereby connecting diverse disciplines of science.
Between 2015 and 2020, his most popular works were:
- Modeling and Valuation of Residential Demand Flexibility for Renewable Energy Integration (68 citations)
- Adaptive building energy management with multiple commodities and flexible evolutionary optimization (40 citations)
- Adaptive building energy management with multiple commodities and flexible evolutionary optimization (40 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Operating system
- Programming language
Hartmut Schmeck mainly focuses on Building automation, Evolutionary algorithm, Energy management system, Renewable energy and Mathematical optimization.
Evolutionary algorithm is closely attributed to Multi-objective optimization in his work.
His studies examine the connections between Multi-objective optimization and genetics, as well as such issues in Optimization problem, with regards to Electricity.
In his research on the topic of Energy management system, Distributed computing, Energy consumption, Efficient energy use, Energy carrier and Modular design is strongly related with Management system.
His work carried out in the field of Renewable energy brings together such families of science as Energy mix, Simulation and Demand response.
His Mathematical optimization study combines topics in areas such as Battery, Control theory, Curvature, Dimensioning and Preference.
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