Multi-agent systems (MAS) tackle multi-objective optimization by allowing multiple independent agents to work collaboratively or competitively to find optimal solutions for complex problems with several conflicting objectives. Each agent in a MAS can represent a different objective, or they can all contribute to a shared goal. By distributing the optimization process across multiple agents, the system can explore a wider solution space more efficiently than a single agent might, facilitating the balancing of different objectives more effectively.
In practical terms, agents can employ various strategies to optimize their objectives. For instance, in a scenario where an agent must minimize cost while maximizing performance, separate agents might focus on either cost reduction or performance enhancement. They can communicate their results, share insights, or even negotiate compromises. Different agents can utilize local optimization techniques tailored to their specific objectives, leading to a diverse set of potential solutions. Once several solutions are identified, a meta-agent or coordinator can evaluate them, deciding on one that best meets the overall goals based on predefined criteria.
A concrete example of this approach can be found in traffic management systems. In such systems, multiple agents, each controlling different intersections, may operate to optimize traffic flow while minimizing congestion and reducing travel time for vehicles. These agents may have access to real-time traffic data, allowing them to adjust signals to balance the conflicting goals of keeping traffic moving and minimizing wait times for pedestrians. By coordinating their actions, these agents can collectively work toward a more efficient and effective solution than any single traffic controller could achieve alone.