A Comparative Study of Geometric Hopfield Network and Ant Colony Algorithm to Solve Travelling Salesperson Problem
Journal: International Journal of Advanced Computer Research (IJACR) (Vol.4, No. 16)Publication Date: 2014-09-18
Authors : Yogeesha C.B; Ramachandra V Pujeri;
Page : 843-848
Keywords : Hopfield Neural Networks; Combinatorial Optimization Problem; Geometric ?TSP; Ant Colony Algorithm - ACA.;
Abstract
The classical methods have limited scope in practical applications as some of them involve objective functions which are not continuous and/or differentiable. Evolutionary Computation is a subfield of artificial intelligence that involves combinatorial optimization problems. Travelling Salesperson Problem (TSP), which considered being a classic example for Combinatorial Optimization problem. It is said to be NP-Complete problem that cannot be solved conventionally particularly when number of cities increase. So Evolutionary techniques is the feasible solution to such problem. This paper explores an evolutionary technique: Geometric Hopfield Neural Network model to solve Travelling Salesperson Problem. Paper also achieves the results of Geometric TSP and compares the result with one of the existing widely used nature inspired heuristic approach Ant Colony Optimization Algorithms (ACA/ACO) to solve Travelling Salesperson Problem.
Other Latest Articles
- Plan, Design and Simulation of University Network
- An analysis of popularity of Consumer to Consumer websites
- Processing of Natural Signals like EMG for Person Identification using NUFB-GMM
- Clustering with Data Encryption in Wireless Communication: A Critical Survey
- Energy Efficient RFID Technique for Wireless Sensor Network- A Review?
Last modified: 2014-12-18 23:01:35