Analysis of Parameterless Particle Swarm Algorithm for Traveling Salesman Problem

Abstract

Evolutionary Algorithms (EA) are standard search mechanisms that use Natural Selection and Survival of the Best as the fundamental algorithm progressing mechanism. The parameterless portfolio is a special technique designed to resolve various categories of problems without any prior requirement of parameter setting. This technique involves an increase in computational effort that can be considered acceptable. In this work, parameterless swarm algorithm using the method of Particle Swarm Optimization has been defined for the application of Traveling Salesman Problem. The performance of the algorithm through the application of parameterless portfolio has been analysed and it can be deduced that the effort of making the Evolutionary Process parameterless can be justified through the benefits discussed in this work.