A distributed and decentralized approach for ant colony optimization with fuzzy parameter adaptation in traveling salesman problem

Ant Colony Optimization (ACO) is a swarm intelligence technique often applied to find solutions to hard optimization problems. In this paper, we present a new decentralized peer-to-peer approach for implementing ACO on distributed memory clusters. In addition, the approach is augmented with a fuzzy logic controller to reactively adapt several parameters of the ACO as a method of offsetting the increased exploitation resulting from the way in which information is shared between computing processes. We build an implementation of the approach for the Travelling Salesman Problem (TSP). The implementation is tested with several TSP problem instances with different numbers of processes in a cluster. The adaptive version is compared with the non-adaptive version and shown to agree with our expectations and performance is evaluated for different numbers of processes with an improvement shown.