Uniform mutation and SPV rule based optimized PSO algorithm for TSP problem

Swarm intelligence system are basically made up simple agents population which are interacting locally with each other and with their surroundings Particle swarm optimization (PSO) algorithm is based on heuristic search and also one of important optimization technique, used to optimize those problems (Like Travelling salesman problem, task scheduling problem et.) which cannot be solved easily. In this work, enhanced version of PSO algorithm which contains uniform mutation operator and smallest position value (SPV) is used and implemented on travelling salesman problem. Here in the enhanced PSO algorithm, one extra phase, in the form of mutation operator of genetic algorithm, is used and SPV rule (smallest position value), which is based on the dimensions index value corresponding to position vector, is used for enhancing local search. Proposed work is compared with the standard PSO algorithm. Experimental results show that the proposed work is better than original PSO algorithm.