Master Thesis

Deep learning methods applied on Path Planning for Multi-Agents systems

Work default illustration


  • Started: 01/11/2021
  • Finished: 20/05/2022


In [KukHyunHan2002], a new approach of the classical evolutionary algorithms is introduced based on quantum computing, named ’Quantum-inspired evolutionary algorithm’, to solve the knaspack problem. In this novel system, quantum properties are used to mutate the solutions over iterations in order to find the most optimal solution of the problem.

It was shown that this new configuration performs better than the classical method. With that said, the main objective of this work is to adapt this algorithm into a service-matching coverage problem, an optimization problem with cardinality constrains included into Assignment Problems.