Take a look at our research blog. Starting from October 2020, we will frequently post in this blog. 


In our research, we work on the methodologies and algorithms in the area of Computational Intelligence. Our major expertise are multi-objective optimization and decision-making algorithms and their applications in science, games and robotics. Decision-making is usually required when we are confronted with conflicting objectives and is in fact a very challenging task even for human decision-makers. This is due to the fact that we first need to find all the possible optimal alternatives and then choose the right alternative using a decision policy.

We specifically work on evolutionary multi-objective optimization techniques and develop algorithms for large-scale and multi-modal problems in various applications such as path finding and collective search. In addition, we design such algorithms for autonomous systems, where we have to replace the human decision-maker with the autonomous system.  One major challenge for autonomous systems concerns the limited time frame for optimization and decision-making. 

In our research, we work on a range of scenarios in which the autonomous systems are confronted with conflicting objectives. This will enable such systems to change their (pre-defined) decision policy according to the unforeseen circumstances. This ability can contribute to their applicability in critical missions, such as rescue robotics where the intervention of a human-controller is not always possible. Such behaviors are additionally of great interest for autonomous driving-cars. 

The challenge is not only in finding and selecting the best alternative, but also in acting in a limited timeframe during the mission. One more focus of our research concerns collective decision-making algorithms. Suppose that we have many autonomous systems such as many autonomous driving-cars. These systems can communicate with each other and collectively learn and decide certain behaviors. For this purpose, we work on swarm intelligence and collective decision-making algorithms in theory, simulation and swarm robotics. This research leads us to the topic of self-organization and organic computing. In organic computing, we work on the controlled self-organization mechanisms. In our research, we are interested to know whether collective learning of a decision policy is more efficient than individual decision-making. 


Multi-Objective Optimization and Decision Making: For several years, we are working on evolutionary algorithms and swarm intelligence to solve various kinds of multi-objective optimization problems. Such optimization problems appear in many applications, such as in economics, mechanical engineering design or computational science. A major characteristic of such problems is that their solution is a set of trade-offs known as Pareto-optimal solutions. These solutions can be described by a domination ordering. The main research question to be answered is how to find a set of diverse Pareto-optimal solutions and how to select one of the solutions (decision making).

Our research focus in this area:

  • Large scale optimization
  • Many-objective optimization
  • Decision Making at real-time
  • Visualization of the solutions
  • Algorithmic design issues
  • Preference-based optimization

Swarm Intelligence: In this area we work on collective intelligence methods for search and exploration in unknown and dynamic environments. The research topics are:

  • Swarms in vector fields
  • Multi-objective decision making in swarms
  • Self-aware swarms
  • Evolutionary swarms
  • Uncertainty in swarms

Swarm Robotics: In this research area, we work on concepts of swarm intelligence for a large group of small and simple robots (Flying Robots) without positioning system (No GPS, No external positioning system). We have several projects in this area to analyze the stability of the flight. We mainly work on developing optimized controllers. Since the environment in which the robots are moving is very complex, it is very difficult to find an optimal controller which can fulfill a desired task. In the research in this area, we aim to let a set of autonomous robotic systems to learn and perform some given task on-line and on-board. The research topics are:

  • Stable flight of swarm robots
  • Indoor flight
  • Real-time decision making
  • Energy-aware flying robots

Computational Intelligence in Games: We apply the methodologies from Computational Intelligence in games. The research topics are: 

  • Multi-objective decision making in real-time
  • Evolutionary game theory
  • Procedural content generation in Games







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