Multi-objective optimization

So far in this chapter, we have taken the examples of the problem with one objective (finding a food source for an ant colony). However, in real-world scenarios, often there is more than one objective that needs to be met by the individual agents as well as the swarm. For example, in the case of honey bees they need to look for the food source, gather the food, and find a safe and viable place for the beehive. One objective is fulfilled at the cost of another objective. The agent should be programmed to consider the trade-off in the larger interest of the swarm.

As far as possible, the optimization function for the agent should bring optimum solution for more than one objective, but it is not feasible to mutual ...

Get Artificial Intelligence for Big Data now with the O’Reilly learning platform.

O’Reilly members experience books, live events, courses curated by job role, and more from O’Reilly and nearly 200 top publishers.