Intelligent and distributed multi-objective methods for optimization and control of multiagents/cooperative systems

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Large teams of robots can accomplish complex tasks such as land-mine clearance. To do so, researchers from robotics to computer science have developed algorithms to have each agent in the multiagent robotic swarm make smart choices in optimizing several objectives. At this level, multi-objective optimization techniques will make multiagent systems more efficient and customized to accomplish the tasks at hand, saving time, life, and energy; however, the existing theory is falling behind. Indeed, in multiagent problems with many objectives, the existing literature considers mainly problems in which agents optimize the diverse functions with equal priorities; however, there exist many cases in which there are objectives with different importance. For example, teams of robots may want to explore different regions of an area, or agents may have different priorities in trajectory planning when minimizing both energy consumption and travel time. Therefore, the next breakthrough in multi-objective optimization multiagent problems is to capacitate agents to prioritize objectives individually.  Thus, through distributed multi-objective optimization, the research program's objective is enhancing the decision-making skills of multiagent systems to enable new practical features, increase agent capacities, and provide a broader range of operating conditions of such systems. To support the research program's objective and help with the next breakthrough, this discovery grant consists of four innovative and independent projects but that are interconnected. In particular, the first five years of this research program will focus on developing distributed multi-objective optimization methods, addressing the existing theoretical limitations in a multiagent context, including in swarm intelligence techniques, and integrating distributed multi-objective optimization methods into foraging and target search tasks. The proposed program will have a significant impact on various fields. From a technological and humanitarian standpoint, with the developed algorithms, land-mine clearance robots will independently prioritize areas requiring more exploration according to external data received in real-time by the agents. It will boost exploration efficiency, resulting in saving more lives. Moreover, the developed algorithms will improve foraging tasks performed by multi-robot systems used in many applications such as surface chemical skimming of unintentional oil spills. From an environmental and social standpoint, the algorithms will dispatch the distributed energy resources in smart-grid more efficiently, resulting in saving money for the consumers and the stakeholders and preserving the environment. Also, in the domain of intelligent transportation systems, the algorithms will be useful tools for distributed routes planning.