These teams, constituting a multi-agent system, can perform tasks such as exploring inaccessible or dangerous regions for humans. To accomplish this task, several control and optimization algorithms have been developed to enable robots to carry out their tasks intelligently. However, existing theories on distributed optimization methods lag behind current needs. Indeed, in multi-agent problems with numerous objectives, the literature has primarily focused on problems where agents optimize objectives with equal priorities. This approach needs to be revised for many applications. In fact, there are several cases where objectives have different importance and vary depending on the agents’ state. For example, a team of robots may want to explore different regions of an area and choose between minimizing energy consumption or travel time. Also, real-life deployments of these kinds of algorithms are currently scarce.

Thus, our research aims to develop distributed multi-objective optimization techniques that have the potential to make multi-agent systems more efficient and suitable for accomplishing their tasks, saving time, energy, and, in some situations, potentially lives. And we work towards shortening the gap between theories and applications. 

In recent years, extensive research efforts have been devoted to proposing new energy management system. Notable advances include algorithms designed a centralized manner. These algorithms necessitate a robust central controller capable of collecting global information and processing large volumes of data. However, centralized control approaches tend to be costly, vulnerable to single-point failures, lack robustness, and necessitate reconfiguration upon the installation of new generators and loads.

Conversely, decentralized methods, such as distributed optimization, have emerged as more robust and scalable alternatives to centralized approaches. Moreover, a distributed optimization approach allows for parallel optimization, effectively reducing computational complexity and solution time. 
Thus, our research aims to develop distributed multi-objective optimization techniques that consider economic and environmental objectives as well as real-world constraints, to manage power dispatch within multi-energy sources of power systems.

A favorable environment for athletes is composed of coaches, nutritionists, psychologists, and physiotherapists, to name a few. This team provides valuable inputs and recommendations to athletes, and after that, the outcomes of their implementation will provide helpful feedback for better planning and preparation for future events. In other words, scientific literature, experience, and trial and error are the main components that feed decisions that we hope will yield greater performance. From a mathematical perspective, improving sports performance, e.g., training planning to nutrition planning, can be seen as a multi-objective optimization problem. What distinguishes MORELab research from the literature is that we are leveraging our expertise in distributed multi-objective optimization techniques to improve sports performance.