A novel based Hidden Markov Model approach for controlling the ACS-TSP evaporation parameter

The aim of this paper is to propose a new method capable of dynamically controlling the evaporation parameter in an Ant Colony System (ACS) using a Hidden Markov Model. The purpose is to improve the performance of ACS by controlling the exploration and exploitation in the search space. To this end, two HMM approaches are proposed. The first is a training method that best suits the observed data of the Hidden Markov Model. The second is a method that dynamically controls the adapted parameter by applying several processes. To test our algorithm we used a set of Travelling Salesman Problem (TSP) instances. © 2016 IEEE.