Abstract:
After the concept of Swarm Intelligence was introduced in late eighties and
became known as a distributed solution for complex tasks, a variety of
Swarm Intelligence heuristics were initiated. Swarm Intelligence heuristics
are often used in solving combinatorial problems such as Travelling
Salesman Problem (TSP). Even though there are numerous attempts to
solve TSP, yet there is space in improving the solution quality for solving
large scale TSP instances. Among Swarm Intelligence algorithms, Particle
Swarm Optimization (PSO) and Ant Colony Optimization (ACO)
algorithms have taken much of the interest of researchers since of their
simplicity, effectiveness and efficiency in applications. The objective of this
study is to attempt a reduction in delay in convergence while maintaining an
acceptable accuracy in solving large scale TSP instances by hybridizing
PSO and ACO. In this proposed ACO followed PSO approach, influences of
cooperation and competition of swarm populations were adapted and fine tuned to increase the solution quality. The experimental results show an
error less than 2.5% when converging to the optimum for TSP instances not
more than 3038 nodes. Further the experimental results demonstrate that
the attempt of reducing the delay in convergence is successful while
maintaining an acceptable solution quality when the proposed approach is
used in solving instances of moderate scale Travelling Salesman Problems.
