Solving travelling salesman problem using egyptian vulture optimization algorithm-A new approach

Abstract

Travelling Salesman Problem (TSP) is a NP-Hard combinatorial optimization problem and many real life problems are constrained replica of it which possesses exponential time complexity and requires heavy combination capability. In this work a new nature inspired meta-heuristics called Egyptian Vulture Optimization Algorithm (EVOA) is being introduced and presented for the first time and illustrated with examples how it can be utilized for the constrained graph based problems and is utilized to solve the various dimensional datasets of the traditional travelling salesman problem. There are not many discrete optimization bio-inspired algorithms available in the literature and in that respect it is a novel one which can readily utilized for the graph based and assignment based problems. This EVOA is inspired by the natural and skilled phenomenal habits, unique perceptions and intelligence of the Egyptian Vulture bird for carry out the livelihood and acquisition of food which is inevitable for any kind of organisms. The Egyptian Vulture bird is one of the few birds who are known for showing dexterous capability when it comes to its confrontation with tough challenges and usage of tools with combinations of force and weakness finding ability. The results show that the Egyptian Vulture Optimization meta-heuristics has potential for deriving solutions for the TSP combinatorial problem and it is found that the quality and perfection of the solutions for the datasets depend mainly on the number of dimensions when considerable for the same number of iterations. © 2013 Springer-Verlag Berlin Heidelberg.