Swarm intelligence (SI) was first introduced and used in the context of robotic cellular systems and has become a subfield of artificial intelligence. The collective behavior and self-organization skills of different animal populations are used in the design of different SI techniques to solve computational problems, especially in the field of optimization. SI techniques, like Particle Swarm optimization or Ant Colony optimization algorithms are often used together with the Gradient Descent, the Gradient Conjugate and some Quasi-Newton methods to improve the effectiveness of high-dimensional optimization problems. In my talk, I would like to introduce a hybrid model that demonstrates how to avoid local minimum traps in a global, non-convex problem.

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UMBC Center for AI